Gaussian RibbonThe Gaussian Ribbon utilizes two "Arnaud Legoux" moving averages with the same length to identify changes in trend direction. The plotted channel consists of two lines, one based on the default offset and sigma values, and the other with slightly adjusted customizable parameters.
ALMA is a type of moving average that is related to the Gaussian function through its mathematical formula and the concept of weighted averages.
The ALMA is designed to reduce lag in moving averages and provide more timely responses to price changes. It achieves this by applying a Gaussian distribution (bell-shaped curve) as a weighting function to the price data.
The Gaussian function is used to calculate the weights in the ALMA formula. These weights give more importance to recent price data while gradually reducing the influence of older data points. This results in a smoother and more responsive moving average.
In summary, the Gaussian Ribbon uses the offset and power of the second ALMA to create a lag that still calculates using the same length.
Cerca negli script per "curve"
[GTH decimals heatmap] (wide screen advised)Preface
I share my personal general view on indicators below; skip ahead to the Description below if you are not interested.
It is my personal conviction that most - if not all - indicators rely mainly on trader's belief that they work, and in a feedback system like free markets they might become a self-fulfilling prophecy as a result, if (!) a big part of the traders believes in it, because some famous trader releases an indicator, or such person's public statement goes viral.
One of those voodoo indicators is the famous "follow-through day". There is zero statistical evidence for its validity, beyond the validity of a statement like "If it's bright at day it's usually the sun shining". The uselessness was proven exactly on its inventor's YT channel, Investors Business Daily. According to the examiner, its inventor William J. O'Neil himself could not explain the values used for this indicator. It might have been an incidental observation at some point without general validity. A.k.a "curve fitting". Still, it's being used by many today.
Another one of those indicators is the three points reversal on the S&P 500 Volatility Index (VIX) which allegedly might potentially maybe indicate a possible shift in trend. Both indicators share an immediately problematic feature: They use absolute values. Nothing is ever absolute in a highly subjective and emotionally driven game like the markets where a lot of money can be made and lost.
Most indicators can not produce additional information since they can only re-pack price/volume action. Many times an interpretion of the distance between price and a moving average and/or the slope of a moving average deliver very similar - if not better - results than MACD, RSI etc., especially with standard settings, the origin of which are usually unknown (always a warning sign). Very few indicators can deliver information which is otherwise hard to quantify, e. g. market noise (Kaufman's Efficiency Ratio or Price Density) or volatility, standard deviation etc.
It is common knowledge that trading the markets is a game of probability. No indicator works all the time (or at all, see above). In order to make decisions based on any indicator, the probability for its validity and the conditions under which validity seemed to have occurred, must be known. Otherwise it is just coffee grounds reading under the illusion of adding to the edge, when in fact it is only adding to the trees, making it even harder to see the forest.
Description
A common belief is that whole or half-dollar prices tend to be attraction points in price action, so a number of traders include those into decision making. But are they really...?
Spoiler Alert:
Generally, it is safe to say that for the big majority of stocks there is very thin evidence for it. It depends vastly on the asset, the timeframe used and the market period (pre/post/main trading times). If at all, there seems to be an above random but still thin evidence for whole prices being significant attraction points. Interesting/surprising patterns are visible on many stocks/timeframes/session periods, though.
The screenshot shows TSLA, 30m timeframe, two heatmaps added. The top one shows pre/post-market data only, the bottom one main market data only. The cyan fields indicate the strongest occurrence, the dark blue fields indicate the weakest occurrence of open/high/low/close prices at the respective decimal. The red field indicates the current/last price decimal.
Clearly, TSLA displays a strong pre-market attraction for .00, followed by .33 and .67 and .50. This pattern of thirds seems to be a unique feature of TSLA. In the main trading session it is being diluted by a more random distribution.
Other interesting equities to examine:
SPY: No significant pattern on any timeframe!
META: Generally weak patterns on all timeframes, but interestingly on the 1D there is evidence for less randomness on O and H, more on L and most on C.
AAPL: 1D, foggy attraction areas around .35 and .12. Whole price is no attraction area at all! Very weak attraction around .73.
AMD: Strong pattern on D, W, M, attraction areas around 1/16th intervals. No patterns on lower timeframes.
AMZN: Significant differences between pre/post and main session. Strong 1/16th pattern below D in pre/post.
TAOP: Strong 1/5th pattern on all timeframes.
Read the tool tips and go explore!
AI Moving Average (Expo)█ Overview
The AI Moving Average indicator is a trading tool that uses an AI-based K-nearest neighbors (KNN) algorithm to analyze and interpret patterns in price data. It combines the logic of a traditional moving average with artificial intelligence, creating an adaptive and robust indicator that can identify strong trends and key market levels.
█ How It Works
The algorithm collects data points and applies a KNN-weighted approach to classify price movement as either bullish or bearish. For each data point, the algorithm checks if the price is above or below the calculated moving average. If the price is above the moving average, it's labeled as bullish (1), and if it's below, it's labeled as bearish (0). The K-Nearest Neighbors (KNN) is an instance-based learning algorithm used in classification and regression tasks. It works on a principle of voting, where a new data point is classified based on the majority label of its 'k' nearest neighbors.
The algorithm's use of a KNN-weighted approach adds a layer of intelligence to the traditional moving average analysis. By considering not just the price relative to a moving average but also taking into account the relationships and similarities between different data points, it offers a nuanced and robust classification of price movements.
This combination of data collection, labeling, and KNN-weighted classification turns the AI Moving Average (Expo) Indicator into a dynamic tool that can adapt to changing market conditions, making it suitable for various trading strategies and market environments.
█ How to Use
Dynamic Trend Recognition
The color-coded moving average line helps traders quickly identify market trends. Green represents bullish, red for bearish, and blue for neutrality.
Trend Strength
By adjusting certain settings within the AI Moving Average (Expo) Indicator, such as using a higher 'k' value and increasing the number of data points, traders can gain real-time insights into strong trends. A higher 'k' value makes the prediction model more resilient to noise, emphasizing pronounced trends, while more data points provide a comprehensive view of the market direction. Together, these adjustments enable the indicator to display only robust trends on the chart, allowing traders to focus exclusively on significant market movements and strong trends.
Key SR Levels
Traders can utilize the indicator to identify key support and resistance levels that are derived from the prevailing trend movement. The derived support and resistance levels are not just based on historical data but are dynamically adjusted with the current trend, making them highly responsive to market changes.
█ Settings
k (Neighbors): Number of neighbors in the KNN algorithm. Increasing 'k' makes predictions more resilient to noise but may decrease sensitivity to local variations.
n (DataPoints): Number of data points considered in AI analysis. This affects how the AI interprets patterns in the price data.
maType (Select MA): Type of moving average applied. Options allow for different smoothing techniques to emphasize or dampen aspects of price movement.
length: Length of the moving average. A greater length creates a smoother curve but might lag recent price changes.
dataToClassify: Source data for classifying price as bullish or bearish. It can be adjusted to consider different aspects of price information
dataForMovingAverage: Source data for calculating the moving average. Different selections may emphasize different aspects of price movement.
-----------------
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!
TASC 2023.09 The Weekly Factor█ OVERVIEW
TASC's September 2023 edition of Traders' Tips features an article written by Andrea Unger titled “The Weekly Factor", discussing the application of price patterns as filters for trade entries. This script implements a sample trading strategy presented in the article for demonstration purposes only. It explores how the strategy's equity curve might benefit from filtering trade entries using a specific price pattern.
█ CONCEPTS
Pattern filters represent valuable tools that assess current market conditions based on price movements and determine when those conditions become more favorable for trade entries.
The filter used and tested in this article is a metric called the "weekly factor", which measures the price range over the last five trading days and compares it to the open of the session five days ago and the close of the session one day ago (i.e., the "body" of the five-day period). When the five-day body is small compared to the five-day range, this could indicate "indecision" or "compression", potentially followed by a price expansion. Thus, the weekly factor metric can help identify areas in the market where a period of compression might signal a potential breakout.
This script demonstrates the use of the weekly factor for a sample intraday trading strategy (intended for educational and exploratory purposes only). In this strategy, the entry signal is triggered when a 15-minute bar breaks out of the previous day's high-low range, and the position is closed at the end of the day.
█ CALCULATIONS
The script uses two timeframes:
• The strategy entries are processed on the 15-minute timeframe.
• The weekly factor is obtained from the daily timeframe using the request.security function and the following formula:
math.abs(open - close ) < RangeFilter * (ta.highest(5) - ta.lowest(5) )
Here, RangeFilter is an input that can be optimized to find the favorable ratio between the five-day body and the five-day range. Smaller RangeFilter values will lead to fewer trade entries. A RangeFilter value of 1 is equivalent to turning off the filtering altogether.
Multi Kernel Regression [ChartPrime]The "Multi Kernel Regression" is a versatile trading indicator that provides graphical interpretations of market trends by using different kernel regression methods. It's beneficial because it smoothes out price data, creating a clearer picture of price movements, and can be tailored according to the user's preference with various options.
What makes this indicator uniquely versatile is the 'Kernel Select' feature, which allows you to choose from a variety of regression kernel types, such as Gaussian, Logistic, Cosine, and many more. In fact, you have 17 options in total, making this an adaptable tool for diverse market contexts.
The bandwidth input parameter directly affects the smoothness of the regression line. While a lower value will make the line more sensitive to price changes by sticking closely to the actual prices, a higher value will smooth out the line even further by placing more emphasis on distant prices.
It's worth noting that the indicator's 'Repaint' function, which re-estimates work according to the most recent data, is not a deficiency or a flaw. Instead, it’s a crucial part of its functionality, updating the regression line with the most recent data, ensuring the indicator measurements remain as accurate as possible. We have however included a non-repaint feature that provides fixed calculations, creating a steady line that does not change once it has been plotted, for a different perspective on market trends.
This indicator also allows you to customize the line color, style, and width, allowing you to seamlessly integrate it into your existing chart setup. With labels indicating potential market turn points, you can stay on top of significant price movements.
Repaint : Enabling this allows the estimator to repaint to maintain accuracy as new data comes in.
Kernel Select : This option allows you to select from an array of kernel types such as Triangular, Gaussian, Logistic, etc. Each kernel has a unique weight function which influences how the regression line is calculated.
Bandwidth : This input, a scalar value, controls the regression line's sensitivity towards the price changes. A lower value makes the regression line more sensitive (closer to price) and higher value makes it smoother.
Source : Here you denote which price the indicator should consider for calculation. Traditionally, this is set as the close price.
Deviation : Adjust this to change the distance of the channel from the regression line. Higher values widen the channel, lower values make it smaller.
Line Style : This provides options to adjust the visual style of the regression lines. Options include Solid, Dotted, and Dashed.
Labels : Enabling this introduces markers at points where the market direction switches. Adjust the label size to suit your preference.
Colors : Customize color schemes for bullish and bearish trends along with the text color to match your chart setup.
Kernel regression, the technique behind the Multi Kernel Regression Indicator, has a rich history rooted in the world of statistical analysis and machine learning.
The origins of kernel regression are linked to the work of Emanuel Parzen in the 1960s. He was a pioneer in the development of nonparametric statistics, a domain where kernel regression plays a critical role. Although originally developed for the field of probability, these methods quickly found application in various other scientific disciplines, notably in econometrics and finance.
Kernel regression became really popular in the 1980s and 1990s along with the rise of other nonparametric techniques, like local regression and spline smoothing. It was during this time that kernel regression methods were extensively studied and widely applied in the fields of machine learning and data science.
What makes the kernel regression ideal for various statistical tasks, including financial market analysis, is its flexibility. Unlike linear regression, which assumes a specific functional form for the relationship between the independent and dependent variables, kernel regression makes no such assumptions. It creates a smooth curve fit to the data, which makes it extremely useful in capturing complex relationships in data.
In the context of stock market analysis, kernel regression techniques came into use in the late 20th century as computational power improved and these techniques could be more easily applied. Since then, they have played a fundamental role in financial market modeling, market prediction, and the development of trading indicators, like the Multi Kernel Regression Indicator.
Today, the use of kernel regression has solidified its place in the world of trading and market analysis, being widely recognized as one of the most effective methods for capturing and visualizing market trends.
The Multi Kernel Regression Indicator is built upon kernel regression, a versatile statistical method pioneered by Emanuel Parzen in the 1960s and subsequently refined for financial market analysis. It provides a robust and flexible approach to capturing complex market data relationships.
This indicator is more than just a charting tool; it reflects the power of computational trading methods, combining statistical robustness with visual versatility. It's an invaluable asset for traders, capturing and interpreting complex market trends while integrating seamlessly into diverse trading scenarios.
In summary, the Multi Kernel Regression Indicator stands as a testament to kernel regression's historic legacy, modern computational power, and contemporary trading insight.
Moving Average CandlesInspired by Ricardo Santos's " Multiple Moving Average Candle System V0" ()
This script plots 6 moving averages using the plotcandle function rather than the normal plot function. Result is a stylish indicator that shows moving average crossovers in a more visual way. Moving average type options available are , or Simple, Exponential, Hull, Relative, Volume Weighted, and Arnaud Legoux Moving Averages, Linear Regression Curve, and Median. Lengths for each can be set in settings along with selection specific parameters. Good for plotting/visualizing potential entry/exit points based on your preferred moving averages crossing over, or just as some eye candy.
US Inversions & RecessionsUnderstand when the US yield curve inverted and when recessions took place. Select from Federal Funds Rate, 3 month yield, 2 year yield and 10 year yield.
Default ratio = Federal Funds Rate / 10 year yield
When line goes from white to red = inversion
When line goes from red to white = un-inversion
Yellow shading shows times when the rates are inverted.
Blue shading shows when recessions officially occurred.
Moving Average Contrarian IndicatorThis indicator is designed to identify potential turning points in the market. By measuring the distance between the price and a moving average, and normalizing it, the MACI provides valuable insights into market sentiment and potential reversals. In this article, we will explore the calculation, interpretation, and practical applications of the MACI, along with its potential limitations.
The MACI is calculated in several steps. First, a moving average is computed using a user-defined length, representing the average price over the specified period. The distance between the current price and the moving average is then determined. This distance is normalized using the highest and lowest distances observed within the chosen length, resulting in a value between 0 and 100. Higher MACI values indicate that the price is relatively far from the moving average, potentially signaling an overextension, while lower values suggest price consolidation or convergence with the moving average.
Altering the parameters of the Moving Average Contrarian Indicator can provide traders with additional flexibility and adaptability to suit different market conditions and trading styles. By adjusting the length parameter, traders can customize the sensitivity of the indicator to price movements. A shorter length may result in more frequent and responsive signals, which can be useful for short-term traders aiming to capture quick price reversals. On the other hand, a longer length may provide smoother signals, suited for traders who prefer to focus on longer-term trends and are less concerned with minor fluctuations. Experimenting with different parameter values allows traders to fine-tune the indicator to align with their preferred trading timeframes and risk tolerance. However, it is essential to strike a balance and avoid excessive parameter adjustments that may lead to over-optimization or curve fitting. Regular evaluation and optimization based on historical data and real-time market observations can help identify the most suitable parameter values for optimal performance.
The coloration of the Moving Average Contrarian Indicator provides visual cues that assist traders in interpreting its signals. The background color, set based on the indicator's values, adds an additional layer of context to the chart. When the indicator is indicating bullish conditions, the background color is set to lime, suggesting a favorable environment for long positions. Conversely, when the indicator signals bearish conditions, the background color is set to fuchsia, indicating a potential advantage for short positions. In neutral or transitional periods, the background color is set to yellow, indicating caution and the absence of a clear bias.
The bar color complements the histogram and provides additional visual clarity. When the MACI value is greater than the MACI SMA value and exceeds the threshold of 30, the bars are colored lime, signaling potential bullish conditions. Conversely, when the MACI value is below the MACI SMA value and falls below the threshold of 70, the bars are colored fuchsia, indicating potential bearish conditions. For values that fall between these thresholds, the bars are colored yellow, highlighting a neutral or transitional state.
Practical Uses and Strategies:
The MACI offers traders and analysts valuable insights into market dynamics and potential reversal points. When the MACI is above its moving average and above a predefined threshold (e.g., 30), it suggests that prices have deviated significantly from the average and may be overbought. This could serve as an early indication for potential short-selling opportunities or taking profits on existing long positions. Conversely, when the MACI is below its moving average and below a predefined threshold (e.g., 70), it suggests oversold conditions, potentially signaling a buying opportunity. Traders can combine MACI with other technical indicators or price patterns to further refine their trading strategies.
The MACI can be a powerful tool for identifying potential market reversals. When the MACI reaches extreme levels, such as above 70 or below 30, it indicates overbought or oversold conditions, respectively. Traders can use these signals to anticipate price reversals and adjust their trading strategies accordingly. For example, when the MACI enters the overbought zone, traders may consider initiating short positions or tightening stop-loss levels on existing long positions. Conversely, when the MACI enters the oversold zone, it may indicate a buying opportunity, prompting traders to consider initiating long positions or loosening stop-loss levels.
The MACI can also be used in conjunction with price action to identify potential divergence patterns. Divergence occurs when the MACI and price move in opposite directions. For instance, if the price is making higher highs while the MACI is making lower highs, it suggests a bearish divergence, indicating a potential trend reversal. Conversely, if the price is making lower lows while the MACI is making higher lows, it suggests a bullish divergence, signaling a potential trend reversal to the upside. Traders can use these divergence patterns as additional confirmation signals when making trading decisions.
Limitations:
-- Sideways and Choppy Markets : The MACI performs best in trending markets where price movements are more pronounced. In sideways or choppy markets with limited directional bias, the MACI may generate false signals or provide less reliable indications. Traders should exercise caution when relying solely on the MACI in such market conditions and consider incorporating additional analysis techniques or filters to confirm potential signals.
-- Lagging Indicator : The MACI is a lagging indicator, as it relies on moving averages and historical price data. It may not provide timely signals for very short-term trading or capturing rapid price movements. Traders should be aware that there may be a delay between the occurrence of a signal and its confirmation by the MACI.
-- False Signals : Like any technical indicator, the MACI is not immune to false signals. It is essential to use the MACI in conjunction with other technical indicators, chart patterns, or fundamental analysis to increase the probability of accurate predictions. Combining multiple confirmation signals can help filter out false signals and enhance the overall reliability of trading decisions.
-- Market Conditions : It's important to consider that the effectiveness of the MACI may vary across different markets and asset classes. Each market has its own characteristics, and what works well in one market may not work as effectively in another. Traders should evaluate the performance of the MACI within their specific trading environment and adapt their strategies accordingly.
This indicator can be a valuable addition to a trader's toolkit, offering insights into potential entry and exit points. However, it should be used in conjunction with other analysis techniques and should not be relied upon as a standalone trading signal. Understanding its calculation, interpreting its values, and considering its limitations will empower traders to make more informed decisions in their pursuit of trading success.
Log Distance From MA + RibbonTaken inspiration from @dkncrypto from his "Percentage Distance from MA" indicator.
I did a couple of very simple modifications.
I added exotic calculations so that it works properly in charts that reach the zero value (like the yield curve).
I also added a ribbon. It is now clearer when price is under resistance when compared to the deviation of it's trend.
The standard price ribbon describes support/resistance levels based on absolute vertical price distance.
The ribbon on the indicator I built describes support/resistance levels based around the trend of the price.
One could couple it with the KST-Based MACD indicator I built.
In my future analyses, I will consider skipping the standard price ribbon and consult the Distance-From-MA ribbon.
Tread lightly, for this is hallowed ground.
-Father Grigori
ATR Adaptive EMA (AEMA)In the world of trading, it's essential to stay ahead of the curve and adapt to the ever-changing market conditions. One of the key aspects of successful trading is using the right tools to analyze and predict market trends. Traditional moving averages, such as the exponential moving average (EMA), have been a staple of technical analysis for decades. However, the limitations of fixed EMA lengths have prompted traders to look for more adaptable and dynamic alternatives. This is where our innovative Adaptive EMA Length Indicator, based on ATR, comes into play.
An Overview of the Adaptive EMA Length Indicator
Our Adaptive EMA Length Indicator is a powerful and versatile tool that utilizes the Average True Range (ATR) to dynamically determine the ideal EMA length based on current market conditions. This unique approach offers traders an edge by providing a more accurate representation of market trends, enabling them to make more informed trading decisions.
Key Features of the Adaptive EMA Length Indicator
Utilizing ATR for Enhanced Volatility Analysis: The Average True Range (ATR) is a well-established measure of market volatility. By incorporating ATR in our indicator, we ensure a more accurate representation of market conditions, allowing traders to better adapt their strategies to the prevailing volatility levels.
Customizable Parameters: Our Adaptive EMA Length Indicator allows traders to adjust key parameters, such as minimum and maximum EMA lengths, ATR length, outlier length, and outlier deviation level. This level of customization gives traders the ability to fine-tune the indicator according to their trading style and preferences.
Versatile Application Across Markets: The Adaptive EMA Length Indicator is designed to work with various financial markets, including stocks, commodities, and cryptocurrencies. Its versatility makes it a valuable addition to any trader's toolkit, regardless of their chosen market.
How to Use the Adaptive EMA Length Indicator
Set your preferred parameters: Begin by adjusting the minimum and maximum EMA lengths, ATR length, outlier length, and outlier deviation level to fit your trading style.
Apply the indicator to your chosen market: Add the Adaptive EMA Length Indicator to your chart and observe the dynamic EMA line adjusting based on current market conditions.
Use the dynamic EMA for trade entry and exit points: Monitor the EMA line in relation to the price action. When the price crosses the EMA line from below, consider it a potential buy signal. Conversely, when the price crosses the EMA line from above, it could indicate a sell signal. However, it's crucial to consider other technical analysis tools and market factors before making any trading decisions.
Continuously assess and adjust: As with any trading strategy, it's essential to keep monitoring market conditions and adjusting your parameters accordingly. Stay vigilant and be prepared to adapt your strategy as needed.
Our Adaptive EMA Length Indicator, based on ATR, offers a revolutionary approach to determining the ideal EMA length. By providing a more accurate representation of market trends, this innovative tool empowers traders to make better-informed decisions and stay ahead of the market. Try it out for yourself and see why it's a game-changer for traders seeking adaptable, dynamic, and effective trading strategies.
[blackcat] L3 Banker Fund SwingLevel 3
Background
The large funds or banker fund are often referred to as Whale. Whale can have a significant impact on the price movements in various markets, especially in cryptocurrency . Therefore, how to monitor Whale trends is of great significance both in terms of fundamentals and technical aspects.
Function
To understand banker fund more directly, a banker fund model is applied for main chart as different candles with colors, as well as short, middle, long term moving averages in yellow, fuchsia, aqua colors, respectively. The banker fund model is made of a fast line of EMA2, and a slow line of EMA42 of a artifical curve fitting line with xsl(close,21))*(20)+close, where xsl is used to calculate the slope of a data series. And then, with definition of the golden cross and dead cross status, banker fund behavior can be extracted as green candle color for bullish an red color candle for bearish. At the mean time, a new type of candle with yellow color is defined as well standing for a bullish swing start.
Remarks
Feedbacks are appreciated.
Simple Dominance Momentum IndicatorThe Simple Dominance Momentum Indicator is a powerful tool for tracking market trends in the world of cryptocurrency. By analyzing the relationship between dominance and market movement, this indicator helps traders identify when money is flowing into or out of the market.
Using the pane structure on TradingView, the Dominance Momentum Indicator makes it easy to visualize and track data from CryptoCap charts. Whether you're a seasoned investor or starting out, this indicator can help you make more informed trading decisions.
All this indicator does is create the pane with a line chart using the Dominance charts to allow you to see the data with one button instead of doing it all manually. However with the addition to allow it to toggle between crypto and stables, so if you are using a /BTC pair, you don't have to add a new pane on, it automatically converts. If you are looking at USDT pairs for example, it will highlight that one for you.
While it can work under any conditions, the Dominance Momentum Indicator is particularly effective on higher timeframes, providing valuable insight into the overall plot of the market trend. With a 55EMA and a faster-moving average of 21EMA, this indicator is designed to help you stay ahead of the curve and make smarter trading decisions.
Remember the golden rule for stablecoin dominance. Down = good, and up = bad; however, you can just invert the indicator, so it flows with the market.
When it comes to the dominance of individual cryptocurrencies, for example, DOT.D, you might find that it going up = increasing dominance is STRENGTH. If the dominance of that is increasing it means it's growing.
Creator Credit: Jamie Goodland
Parabolic Scalp Take Profit[ChartPrime]Indicators can be a great way to signal when the optimal time is for taking profits. However, many indicators are lagging in nature and will get market participants out of their trades at less than optimal price points. This take profit indicator uses the concept of slope and exponential gain to calculate when the optimal time is to take profits on your trades, thus making this a leading indicator.
Usage:
In essence the indicator will draw a parabolic line that starts from the market participants entry point and exponentially grows the slope of the line eventually intersecting with the price action. When price intersects with the parabolic line a take profit signal will appear in the form of an x. We have found that this take profit indicator is especially useful for scalp trades on lower timeframes.
How To Use:
Add the indicator to the chart. Click on the candle which the trade is on. Click on either the price which the trade will be at, or at the bottom of the candle in a long, or the top of a candle in a short. Select long or short. Open the settings of the indicator and adjust the aggressiveness to the desired value.
Settings:
- Start Time -- This is the bar in which your entry will be at, or occured at and the script will ask you to click on the bar with your mouse upon first adding the script.
- Start Price -- This is the price in which the entry will be at, or was at and the script will ask you to click on the price with your mouse upon first adding the script.
- Long/Short -- This is a setting which lets the script know if it is a long or a short trade, and the script will ask you to confirm this upon first adding it to the chart.
- Aggressiveness -- This directly affects how aggressive the exponential curve is. A value of 101 is the lowest possible setting, indicating a very non-aggressive exponential buildup. A value of 200 is the highest and most aggressive setting, indicating a doubling effect per bar on the slope.
Vector2Library "Vector2"
Representation of two dimensional vectors or points.
This structure is used to represent positions in two dimensional space or vectors,
for example in spacial coordinates in 2D space.
~~~
references:
docs.unity3d.com
gist.github.com
github.com
gist.github.com
gist.github.com
gist.github.com
~~~
new(x, y)
Create a new Vector2 object.
Parameters:
x : float . The x value of the vector, default=0.
y : float . The y value of the vector, default=0.
Returns: Vector2. Vector2 object.
-> usage:
`unitx = Vector2.new(1.0) , plot(unitx.x)`
from(value)
Assigns value to a new vector `x,y` elements.
Parameters:
value : float, x and y value of the vector.
Returns: Vector2. Vector2 object.
-> usage:
`one = Vector2.from(1.0), plot(one.x)`
from(value, element_sep, open_par, close_par)
Assigns value to a new vector `x,y` elements.
Parameters:
value : string . The `x` and `y` value of the vector in a `x,y` or `(x,y)` format, spaces and parentesis will be removed automatically.
element_sep : string . Element separator character, default=`,`.
open_par : string . Open parentesis character, default=`(`.
close_par : string . Close parentesis character, default=`)`.
Returns: Vector2. Vector2 object.
-> usage:
`one = Vector2.from("1.0,2"), plot(one.x)`
copy(this)
Creates a deep copy of a vector.
Parameters:
this : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = Vector2.new(1.0) , b = a.copy() , plot(b.x)`
down()
Vector in the form `(0, -1)`.
Returns: Vector2. Vector2 object.
left()
Vector in the form `(-1, 0)`.
Returns: Vector2. Vector2 object.
right()
Vector in the form `(1, 0)`.
Returns: Vector2. Vector2 object.
up()
Vector in the form `(0, 1)`.
Returns: Vector2. Vector2 object.
one()
Vector in the form `(1, 1)`.
Returns: Vector2. Vector2 object.
zero()
Vector in the form `(0, 0)`.
Returns: Vector2. Vector2 object.
minus_one()
Vector in the form `(-1, -1)`.
Returns: Vector2. Vector2 object.
unit_x()
Vector in the form `(1, 0)`.
Returns: Vector2. Vector2 object.
unit_y()
Vector in the form `(0, 1)`.
Returns: Vector2. Vector2 object.
nan()
Vector in the form `(float(na), float(na))`.
Returns: Vector2. Vector2 object.
xy(this)
Return the values of `x` and `y` as a tuple.
Parameters:
this : Vector2 . Vector2 object.
Returns: .
-> usage:
`a = Vector2.new(1.0, 1.0) , = a.xy() , plot(ax)`
length_squared(this)
Length of vector `a` in the form. `a.x^2 + a.y^2`, for comparing vectors this is computationaly lighter.
Parameters:
this : Vector2 . Vector2 object.
Returns: float. Squared length of vector.
-> usage:
`a = Vector2.new(1.0, 1.0) , plot(a.length_squared())`
length(this)
Magnitude of vector `a` in the form. `sqrt(a.x^2 + a.y^2)`
Parameters:
this : Vector2 . Vector2 object.
Returns: float. Length of vector.
-> usage:
`a = Vector2.new(1.0, 1.0) , plot(a.length())`
normalize(a)
Vector normalized with a magnitude of 1, in the form. `a / length(a)`.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = normalize(Vector2.new(3.0, 2.0)) , plot(a.y)`
isNA(this)
Checks if any of the components is `na`.
Parameters:
this : Vector2 . Vector2 object.
Returns: bool.
usage:
p = Vector2.new(1.0, na) , plot(isNA(p)?1:0)
add(a, b)
Adds vector `b` to `a`, in the form `(a.x + b.x, a.y + b.y)`.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = one() , b = one() , c = add(a, b) , plot(c.x)`
add(a, b)
Adds vector `b` to `a`, in the form `(a.x + b, a.y + b)`.
Parameters:
a : Vector2 . Vector2 object.
b : float . Value.
Returns: Vector2. Vector2 object.
-> usage:
`a = one() , b = 1.0 , c = add(a, b) , plot(c.x)`
add(a, b)
Adds vector `b` to `a`, in the form `(a + b.x, a + b.y)`.
Parameters:
a : float . Value.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = 1.0 , b = one() , c = add(a, b) , plot(c.x)`
subtract(a, b)
Subtract vector `b` from `a`, in the form `(a.x - b.x, a.y - b.y)`.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = one() , b = one() , c = subtract(a, b) , plot(c.x)`
subtract(a, b)
Subtract vector `b` from `a`, in the form `(a.x - b, a.y - b)`.
Parameters:
a : Vector2 . vector2 object.
b : float . Value.
Returns: Vector2. Vector2 object.
-> usage:
`a = one() , b = 1.0 , c = subtract(a, b) , plot(c.x)`
subtract(a, b)
Subtract vector `b` from `a`, in the form `(a - b.x, a - b.y)`.
Parameters:
a : float . value.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = 1.0 , b = one() , c = subtract(a, b) , plot(c.x)`
multiply(a, b)
Multiply vector `a` with `b`, in the form `(a.x * b.x, a.y * b.y)`.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = one() , b = one() , c = multiply(a, b) , plot(c.x)`
multiply(a, b)
Multiply vector `a` with `b`, in the form `(a.x * b, a.y * b)`.
Parameters:
a : Vector2 . Vector2 object.
b : float . Value.
Returns: Vector2. Vector2 object.
-> usage:
`a = one() , b = 1.0 , c = multiply(a, b) , plot(c.x)`
multiply(a, b)
Multiply vector `a` with `b`, in the form `(a * b.x, a * b.y)`.
Parameters:
a : float . Value.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = 1.0 , b = one() , c = multiply(a, b) , plot(c.x)`
divide(a, b)
Divide vector `a` with `b`, in the form `(a.x / b.x, a.y / b.y)`.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = from(3.0) , b = from(2.0) , c = divide(a, b) , plot(c.x)`
divide(a, b)
Divide vector `a` with value `b`, in the form `(a.x / b, a.y / b)`.
Parameters:
a : Vector2 . Vector2 object.
b : float . Value.
Returns: Vector2. Vector2 object.
-> usage:
`a = from(3.0) , b = 2.0 , c = divide(a, b) , plot(c.x)`
divide(a, b)
Divide value `a` with vector `b`, in the form `(a / b.x, a / b.y)`.
Parameters:
a : float . Value.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = 3.0 , b = from(2.0) , c = divide(a, b) , plot(c.x)`
negate(a)
Negative of vector `a`, in the form `(-a.x, -a.y)`.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = from(3.0) , b = a.negate , plot(b.x)`
pow(a, b)
Raise vector `a` with exponent vector `b`, in the form `(a.x ^ b.x, a.y ^ b.y)`.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = from(3.0) , b = from(2.0) , c = pow(a, b) , plot(c.x)`
pow(a, b)
Raise vector `a` with value `b`, in the form `(a.x ^ b, a.y ^ b)`.
Parameters:
a : Vector2 . Vector2 object.
b : float . Value.
Returns: Vector2. Vector2 object.
-> usage:
`a = from(3.0) , b = 2.0 , c = pow(a, b) , plot(c.x)`
pow(a, b)
Raise value `a` with vector `b`, in the form `(a ^ b.x, a ^ b.y)`.
Parameters:
a : float . Value.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = 3.0 , b = from(2.0) , c = pow(a, b) , plot(c.x)`
sqrt(a)
Square root of the elements in a vector.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = from(3.0) , b = sqrt(a) , plot(b.x)`
abs(a)
Absolute properties of the vector.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = from(-3.0) , b = abs(a) , plot(b.x)`
min(a)
Lowest element of a vector.
Parameters:
a : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(3.0, 1.5) , b = min(a) , plot(b)`
max(a)
Highest element of a vector.
Parameters:
a : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(3.0, 1.5) , b = max(a) , plot(b)`
vmax(a, b)
Highest elements of two vectors.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 2.0) , b = new(2.0, 3.0) , c = vmax(a, b) , plot(c.x)`
vmax(a, b, c)
Highest elements of three vectors.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
c : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 2.0) , b = new(2.0, 3.0) , c = new(1.5, 4.5) , d = vmax(a, b, c) , plot(d.x)`
vmin(a, b)
Lowest elements of two vectors.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 2.0) , b = new(2.0, 3.0) , c = vmin(a, b) , plot(c.x)`
vmin(a, b, c)
Lowest elements of three vectors.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
c : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 2.0) , b = new(2.0, 3.0) , c = new(1.5, 4.5) , d = vmin(a, b, c) , plot(d.x)`
perp(a)
Perpendicular Vector of `a`, in the form `(a.y, -a.x)`.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = perp(a) , plot(b.x)`
floor(a)
Compute the floor of vector `a`.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = floor(a) , plot(b.x)`
ceil(a)
Ceils vector `a`.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = ceil(a) , plot(b.x)`
ceil(a, digits)
Ceils vector `a`.
Parameters:
a : Vector2 . Vector2 object.
digits : int . Digits to use as ceiling.
Returns: Vector2. Vector2 object.
round(a)
Round of vector elements.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = round(a) , plot(b.x)`
round(a, precision)
Round of vector elements.
Parameters:
a : Vector2 . Vector2 object.
precision : int . Number of digits to round vector "a" elements.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(0.123456, 1.234567) , b = round(a, 2) , plot(b.x)`
fractional(a)
Compute the fractional part of the elements from vector `a`.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.123456, 1.23456) , b = fractional(a) , plot(b.x)`
dot_product(a, b)
dot_product product of 2 vectors, in the form `a.x * b.x + a.y * b.y.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = dot_product(a, b) , plot(c)`
cross_product(a, b)
cross product of 2 vectors, in the form `a.x * b.y - a.y * b.x`.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = cross_product(a, b) , plot(c)`
equals(a, b)
Compares two vectors
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: bool. Representing the equality.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = equals(a, b) ? 1 : 0 , plot(c)`
sin(a)
Compute the sine of argument vector `a`.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = sin(a) , plot(b.x)`
cos(a)
Compute the cosine of argument vector `a`.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = cos(a) , plot(b.x)`
tan(a)
Compute the tangent of argument vector `a`.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = tan(a) , plot(b.x)`
atan2(x, y)
Approximation to atan2 calculation, arc tangent of `y/x` in the range (-pi,pi) radians.
Parameters:
x : float . The x value of the vector.
y : float . The y value of the vector.
Returns: float. Value with angle in radians. (negative if quadrante 3 or 4)
-> usage:
`a = new(3.0, 1.5) , b = atan2(a.x, a.y) , plot(b)`
atan2(a)
Approximation to atan2 calculation, arc tangent of `y/x` in the range (-pi,pi) radians.
Parameters:
a : Vector2 . Vector2 object.
Returns: float, value with angle in radians. (negative if quadrante 3 or 4)
-> usage:
`a = new(3.0, 1.5) , b = atan2(a) , plot(b)`
distance(a, b)
Distance between vector `a` and `b`.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = distance(a, b) , plot(c)`
rescale(a, length)
Rescale a vector to a new magnitude.
Parameters:
a : Vector2 . Vector2 object.
length : float . Magnitude.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = 2.0 , c = rescale(a, b) , plot(c.x)`
rotate(a, radians)
Rotates vector by a angle.
Parameters:
a : Vector2 . Vector2 object.
radians : float . Angle value in radians.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = 2.0 , c = rotate(a, b) , plot(c.x)`
rotate_degree(a, degree)
Rotates vector by a angle.
Parameters:
a : Vector2 . Vector2 object.
degree : float . Angle value in degrees.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = 45.0 , c = rotate_degree(a, b) , plot(c.x)`
rotate_around(this, center, angle)
Rotates vector `target` around `origin` by angle value.
Parameters:
this
center : Vector2 . Vector2 object.
angle : float . Angle value in degrees.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = rotate_around(a, b, 45.0) , plot(c.x)`
perpendicular_distance(a, b, c)
Distance from point `a` to line between `b` and `c`.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
c : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(1.5, 2.6) , b = from(1.0) , c = from(3.0) , d = perpendicular_distance(a, b, c) , plot(d.x)`
project(a, axis)
Project a vector onto another.
Parameters:
a : Vector2 . Vector2 object.
axis : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = project(a, b) , plot(c.x)`
projectN(a, axis)
Project a vector onto a vector of unit length.
Parameters:
a : Vector2 . Vector2 object.
axis : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = projectN(a, b) , plot(c.x)`
reflect(a, axis)
Reflect a vector on another.
Parameters:
a : Vector2 . Vector2 object.
axis
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = reflect(a, b) , plot(c.x)`
reflectN(a, axis)
Reflect a vector to a arbitrary axis.
Parameters:
a : Vector2 . Vector2 object.
axis
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = reflectN(a, b) , plot(c.x)`
angle(a)
Angle in radians of a vector.
Parameters:
a : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(3.0, 1.5) , b = angle(a) , plot(b)`
angle_unsigned(a, b)
unsigned degree angle between 0 and +180 by given two vectors.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = angle_unsigned(a, b) , plot(c)`
angle_signed(a, b)
Signed degree angle between -180 and +180 by given two vectors.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = angle_signed(a, b) , plot(c)`
angle_360(a, b)
Degree angle between 0 and 360 by given two vectors
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = angle_360(a, b) , plot(c)`
clamp(a, min, max)
Restricts a vector between a min and max value.
Parameters:
a : Vector2 . Vector2 object.
min
max
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = from(2.5) , d = clamp(a, b, c) , plot(d.x)`
clamp(a, min, max)
Restricts a vector between a min and max value.
Parameters:
a : Vector2 . Vector2 object.
min : float . Lower boundary value.
max : float . Higher boundary value.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = clamp(a, 2.0, 2.5) , plot(b.x)`
lerp(a, b, rate)
Linearly interpolates between vectors a and b by rate.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
rate : float . Value between (a:-infinity -> b:1.0), negative values will move away from b.
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = lerp(a, b, 0.5) , plot(c.x)`
herp(a, b, rate)
Hermite curve interpolation between vectors a and b by rate.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
rate : Vector2 . Vector2 object. Value between (a:0 > 1:b).
Returns: Vector2. Vector2 object.
-> usage:
`a = new(3.0, 1.5) , b = from(2.0) , c = from(2.5) , d = herp(a, b, c) , plot(d.x)`
transform(position, mat)
Transform a vector by the given matrix.
Parameters:
position : Vector2 . Source vector.
mat : M32 . Transformation matrix
Returns: Vector2. Transformed vector.
transform(position, mat)
Transform a vector by the given matrix.
Parameters:
position : Vector2 . Source vector.
mat : M44 . Transformation matrix
Returns: Vector2. Transformed vector.
transform(position, mat)
Transform a vector by the given matrix.
Parameters:
position : Vector2 . Source vector.
mat : matrix . Transformation matrix, requires a 3x2 or a 4x4 matrix.
Returns: Vector2. Transformed vector.
transform(this, rotation)
Transform a vector by the given quaternion rotation value.
Parameters:
this : Vector2 . Source vector.
rotation : Quaternion . Rotation to apply.
Returns: Vector2. Transformed vector.
area_triangle(a, b, c)
Find the area in a triangle of vectors.
Parameters:
a : Vector2 . Vector2 object.
b : Vector2 . Vector2 object.
c : Vector2 . Vector2 object.
Returns: float.
-> usage:
`a = new(1.0, 2.0) , b = from(2.0) , c = from(1.0) , d = area_triangle(a, b, c) , plot(d.x)`
random(max)
2D random value.
Parameters:
max : Vector2 . Vector2 object. Vector upper boundary.
Returns: Vector2. Vector2 object.
-> usage:
`a = from(2.0) , b = random(a) , plot(b.x)`
random(max)
2D random value.
Parameters:
max : float, Vector upper boundary.
Returns: Vector2. Vector2 object.
-> usage:
`a = random(2.0) , plot(a.x)`
random(min, max)
2D random value.
Parameters:
min : Vector2 . Vector2 object. Vector lower boundary.
max : Vector2 . Vector2 object. Vector upper boundary.
Returns: Vector2. Vector2 object.
-> usage:
`a = from(1.0) , b = from(2.0) , c = random(a, b) , plot(c.x)`
random(min, max)
2D random value.
Parameters:
min : Vector2 . Vector2 object. Vector lower boundary.
max : Vector2 . Vector2 object. Vector upper boundary.
Returns: Vector2. Vector2 object.
-> usage:
`a = random(1.0, 2.0) , plot(a.x)`
noise(a)
2D Noise based on Morgan McGuire @morgan3d.
Parameters:
a : Vector2 . Vector2 object.
Returns: Vector2. Vector2 object.
-> usage:
`a = from(2.0) , b = noise(a) , plot(b.x)`
to_string(a)
Converts vector `a` to a string format, in the form `"(x, y)"`.
Parameters:
a : Vector2 . Vector2 object.
Returns: string. In `"(x, y)"` format.
-> usage:
`a = from(2.0) , l = barstate.islast ? label.new(bar_index, 0.0, to_string(a)) : label(na)`
to_string(a, format)
Converts vector `a` to a string format, in the form `"(x, y)"`.
Parameters:
a : Vector2 . Vector2 object.
format : string . Format to apply transformation.
Returns: string. In `"(x, y)"` format.
-> usage:
`a = from(2.123456) , l = barstate.islast ? label.new(bar_index, 0.0, to_string(a, "#.##")) : label(na)`
to_array(a)
Converts vector to a array format.
Parameters:
a : Vector2 . Vector2 object.
Returns: array.
-> usage:
`a = from(2.0) , b = to_array(a) , plot(array.get(b, 0))`
to_barycentric(this, a, b, c)
Captures the barycentric coordinate of a cartesian position in the triangle plane.
Parameters:
this : Vector2 . Source cartesian coordinate position.
a : Vector2 . Triangle corner `a` vertice.
b : Vector2 . Triangle corner `b` vertice.
c : Vector2 . Triangle corner `c` vertice.
Returns: bool.
from_barycentric(this, a, b, c)
Captures the cartesian coordinate of a barycentric position in the triangle plane.
Parameters:
this : Vector2 . Source barycentric coordinate position.
a : Vector2 . Triangle corner `a` vertice.
b : Vector2 . Triangle corner `b` vertice.
c : Vector2 . Triangle corner `c` vertice.
Returns: bool.
to_complex(this)
Translate a Vector2 structure to complex.
Parameters:
this : Vector2 . Source vector.
Returns: Complex.
to_polar(this)
Translate a Vector2 cartesian coordinate into polar coordinates.
Parameters:
this : Vector2 . Source vector.
Returns: Pole. The returned angle is in radians.
Central Bank Dark Energy TracerCentral Bank Dark Energy Tracer (CBDE Tracer)
What makes The Universe grow at an accelerating pace?
Dark Energy.
What makes The Economy grow at an accelerating pace?
Debt.
Debt is the Dark Energy of The Economy.
The CBDE Tracer is a tool that tracks currency assets in US dollars that can be scaled to fit other assets on TradingView.
The example provided is QQQ with scale factors and offsets applied that best curve fit to the most recent price action.
The white line is non-US assets from the following central banks:
-JPY (Japan)
-CNY (China)
-UK (British Pound)
-SNB (Swiss National Bank)
-ECB (European Central Bank )
The lime green line is for US Federal Reserve data including a midpoint of WRESBAL and the fed liquidity calculation (WALCL - WTREGEN) and then subtracting RRPONTSYD
The purple line is the average of the two, US assets, and non-US.
The settings can be configured so that only the average is showing, which should the closest aggregate of all liquidity data.
[E5 Trading] Moving AveragesMoving Averages
Plot up to 12 moving averages and customize colors directly on the inputs tab.
Select from any of one of eight (8) moving averages types from the drop-down menu including 'EMA', 'HMA', 'LINREG', 'SWMA', 'SINE', 'SMA', 'VWMA', and 'WMA'.
Default 'SMA' for Plots 1 through 6, and default 'EMA' for plots 7 through 12.
Use this indicator to quickly transition between your favorite moving average combinations.
This indicator can also be used to create the Guppy Multiple Moving Average: www.investopedia.com
Definitions
'EMA' = Exponential Moving Average
'HMA' = Hull Moving Average
'LINREG' = Linear Regression Curve
'SWMA' = Symmetrically Weighted Moving Average
'SINE' = Sine Weighted Moving Average
'SMA' = Simple Moving Average
'VWMA' = Volume Weighted Moving Average
'WMA' = Weighted Moving Average
Fair value bands / quantifytools— Overview
Fair value bands, like other band tools, depict dynamic points in price where price behaviour is normal or abnormal, i.e. trading at/around mean (price at fair value) or deviating from mean (price outside fair value). Unlike constantly readjusting standard deviation based bands, fair value bands are designed to be smooth and constant, based on typical historical deviations. The script calculates pivots that take place above/below fair value basis and forms median deviation bands based on this information. These points are then multiplied up to 3, representing more extreme deviations.
By default, the script uses OHLC4 and SMA 20 as basis for the bands. Users can form their preferred fair value basis using following options:
Price source
- Standard OHLC values
- HL2 (High + low / 2)
- OHLC4 (Open + high + low + close / 4)
- HLC3 (High + low + close / 3)
- HLCC4 (High + low + close + close / 4)
Smoothing
- SMA
- EMA
- HMA
- RMA
- WMA
- VWMA
- Median
Once fair value basis is established, some additional customization options can be employed:
Trend mode
Direction based
Cross based
Trend modes affect fair value basis color that indicates trend direction. Direction based trend considers only the direction of the defined fair value basis, i.e. pointing up is considered an uptrend, vice versa for downtrend. Cross based trends activate when selected source (same options as price source) crosses fair value basis. These sources can be set individually for uptrend/downtrend cross conditions. By default, the script uses cross based trend mode with low and high as sources.
Cross based (downtrend not triggered) vs. direction based (downtrend triggered):
Threshold band
Threshold band is calculated using typical deviations when price is trading at fair value basis. In other words, a little bit of "wiggle room" is added around the mean based on expected deviation. This feature is useful for cross based trends, as it allows filtering insignificant crosses that are more likely just noise. By default, threshold band is calculated based on 1x median deviation from mean. Users can increase/decrease threshold band width via input menu for more/less noise filtering, e.g. 2x threshold band width would require price to cross wiggle room that is 2x wider than typical, 0x erases threshold band altogether.
Deviation bands
Width of deviation bands by default is based on 1x median deviations and can be increased/decreased in a similar manner to threshold bands.
Each combination of customization options produces varying behaviour in the bands. To measure the behaviour and finding fairest representation of fair and unfair value, some data is gathered.
— Fair value metrics
Space between each band is considered a lot, named +3, +2, +1, -1, -2, -3. For each lot, time spent and volume relative to volume moving average (SMA 20) is recorded each time price is trading in a given lot:
Depending on the asset, timeframe and chosen fair value basis, shape of the distributions vary. However, practically always time is distributed in a normal bell curve shape, being highest at lots +1 to -1, gradually decreasing the further price is from the mean. This is hardly surprising, but it allows accurately determining dynamic areas of normal and abnormal price behaviour (i.e. low risk area between +1 and -1, high risk area between +-2 to +-3). Volume on the other hand is typically distributed the other way around, being lowest at lots +1 to -1 and highest at +-2 to +-3. When time and volume are distributed like so, we can conclude that 1) price being outside fair value is a rare event and 2) the more price is outside fair value, the more anomaly behaviour in volume we tend to find.
Viewing metric calculations
Metric calculation highlights can be enabled from the input menu, resulting in a lot based coloring and visibility of each lot counter (time, cumulative relative volume and average relative volume) in data window:
— Alerts
Available alerts are the following:
Individual
- High crossing deviation band (bands +1 to +3 )
- Low crossing deviation band (bands -1 to -3 )
- Low at threshold band in an uptrend
- High at threshold band in a downtrend
- New uptrend
- New downtrend
Grouped
- New uptrend or downtrend
- Deviation band cross (+1 or -1)
- Deviation band cross (+2 or -2)
- Deviation band cross (+3 or -3)
— Practical guide
Example #1 : Risk on/risk off trend following
Ideal trend stays inside fair value and provides sufficient cool offs between the moves. When this is the case, fair value bands can be used for sensible entry/exit levels within the trend.
Example #2 : Mean reversions
When price shows exuberance into an extreme deviation, followed by a stall and signs of exhaustion (wicks), an opportunity for mean reversion emerges. The higher the deviation, the more volatility in the move, the more signalling of exhaustion, the better.
Example #3 : Tweaking bands for desired behaviour
The faster the length of fair value basis, the more momentum price needs to hit extreme deviation levels, as bands too are moving faster alongside price. Decreasing fair value basis length typically leads to more quick and aggressive deviations and less steady trends outside fair value.
OECD CLI Diffusion IndexWhat does the indicator measure?
This is a macro indicator. It uses OECD's composite leading indicator - see details about the CLI below.
What it does it calculate YoY changes for CLI of 38 countries that are members or are associated with the OECD. Then it measures a percent of countries which CLI is rising.
How this can be used?
The positive slope of the curve means that there probably will be an economic growth among those countries within next 6 - 9 months. The negative slope means there probably will be an economic contraction.
Forward-looking economic growth is correlated with positive S&P 500 YoY growth (equity markets are also forward looking). The chart above presents the CLI diffusion index with overlayed S&P500 YoY rate of change.
The CLI is also correlated with ISM PMI - see example below:
What is a CLI?
"The OECD system of Composite Leading Indicators (CLIs) is designed to provide early signals of turning points in business cycles - fluctuation in the output gap, i.e. fluctuation of the economic activity around its long term potential level. This approach, focusing on turning points (peaks and troughs), results in CLIs that provide qualitative rather than quantitative information on short-term economic movements."
I11L - Meanreverter 4h---Overview---
The system buys fear and sells greed.
Its relies on a Relative Strength Index (RSI) and moving averages (MA) to find oversold and overbought states.
It seems to work best in market conditions where the Bond market has a negative Beta to Stocks.
Backtests in a longer Timeframe will clearly show this.
---Parameter---
Frequency: Smothens the RSI curve, helps to "remember" recent highs better.
RsiFrequency: A Frequency of 40 implies a RSI over the last 40 Bars.
BuyZoneDistance: Spacing between the different zones. A wider spacing reduces the amount of signals and icnreases the holding duration. Should be finetuned with tradingcosts in mind.
AvgDownATRSum: The multiple of the Average ATR over 20 Bars * amount of opentrades for your average down. I choose the ATR over a fixed percent loss to find more signals in low volatility environments and less in high volatility environments.
---Some of my thoughts---
Be very careful about the good backtesting performance in many US-Stocks because the System had a favourable environment since 1970.
Be careful about the survivorship bias as well.
52% of stocks from the S&P500 were removed since 2000.
I discount my Annual Results by 5% because of this fact.
You will find yourself quite often with very few signals because of the high market correlation.
My testing suggests that there is no expected total performance difference between a signal from a bad and a signal from a good market condition but a higher volatility.
I am sharing this strategy because i am currently not able to implement it as i want to and i think that meanreversion is starting to be taken more serious by traders.
The challange in implementing this strategy is that you need to be invested 100% of the time to retrieve the expected annual performance and to reduce the fat tail risk by market crashes.
Dynamic Highest Lowest Moving AverageSimilar to my last script, although this one uses the RSI value of
(highest high - price) / (price - lowest low)
to feed into the the logic creating the dynamic length. Choose how the length curve works by selecting either Incline, Decline, Peak or Trough.
Lastly select the moving average type to filter the result through to smoothen things out a bit
to find something that works for your strategy. This is useful as an entry/exit indicator along with other moving averages, or even just a standalone if you play with the settings enough.
Quantitative Backtesting Panel + ROI Table - ShortsThis script is an aggregate of a backtesting panel with quantitative metrics, ROI table and open ROI reader. It also contains a mechanism for having a fixed percentage stop loss, similar to native TV backtester. For shorts only.
Backtesting Panel:
- Certain metrics are color coded, with green being good performance, orange being neutral, red being undesirable.
• ROI : return with the system, in %
• ROI(COMP=1): return if money is compounded at a rate of 100%
• Hit rate: accuracy of the system, as a %
• Profit factor: gross profit/gross loss
• Maximum drawdown: the maximum value from a peak to a successive trough of the system's equity curve
• MAE: Maximum Adverse Excursion. The biggest loss of a trade suffered while the position is still open
• Total trades: total number of closed trades
• Max gain/max loss: shows the biggest win over the biggest loss suffered
• Sharpe ratio: measures the performance of the system with adjusted risk (no comparison to risk-free asset)
• CAGR: Compound Annual Growth Rate. The mean annual rate of growth of the system of n years (provided n>1)
• Kurtosis: measures how heavily the tails of the distribution differ from that of a normal distribution (symmetric on both sides of mean where mean=0, standard deviation=1). A normal distribution has a kurtosis of 3, and skewness of 0. The kurtosis indicates whether or not the tails of the returns contain extreme values
• Skewness: measures the symmetry of the distribution of returns
- Leptokurtic: K > 0. Having more kurtosis than a normal distribution. It's stretched up and to the side too (2nd pic down). High kurtosis (leptokurtic) is bad as the wider tails (called heavy tails) suggest there is relatively high probability of extreme events
- Mesokurtic: K =0. Having the same kurtosis as a normal distribution
- Platykurtic: K < 0. Having less kurtosis than a normal distribution. This suggests there are light tails and fewer extreme events in the distribution
- Skewness is good: +/- 0.5 (fairly symmetrical)
- Skewness is average: -1 to -0.5 or 0.5 to 1 (moderately skewed)
- Skewness is bad: > +/- 1 (highly skewed)
Evolving ROI table:
- The table of ROI values evolve with the year and month. The sum of each year is given. Please avoid using it on non-cryptocurrencies or any market whose trading session is not 24/7
Open ROI reader:
- At the top center is the open ROI of a trade
Quantitative Backtesting Panel + ROI Table - LongsThis script is an aggregate of a backtesting panel with quantitative metrics, ROI table and open ROI reader. It also contains a mechanism for having a fixed percentage stop loss, similar to native TV backtester. For longs only.
Backtesting Panel:
- Certain metrics are color coded, with green being good performance, orange being neutral, red being undesirable.
• ROI : return with the system, in %
• ROI(COMP=1): return if money is compounded at a rate of 100%
• Hit rate: accuracy of the system, as a %
• Profit factor: gross profit/gross loss
• Maximum drawdown: the maximum value from a peak to a successive trough of the system's equity curve
• MAE: Maximum Adverse Excursion. The biggest loss of a trade suffered while the position is still open
• Total trades: total number of closed trades
• Max gain/max loss: shows the biggest win over the biggest loss suffered
• Sharpe ratio: measures the performance of the system with adjusted risk (no comparison to risk-free asset)
• CAGR: Compound Annual Growth Rate. The mean annual rate of growth of the system of n years (provided n>1)
• Kurtosis: measures how heavily the tails of the distribution differ from that of a normal distribution (symmetric on both sides of mean where mean=0, standard deviation=1). A normal distribution has a kurtosis of 3, and skewness of 0. The kurtosis indicates whether or not the tails of the returns contain extreme values
• Skewness: measures the symmetry of the distribution of returns
- Leptokurtic: K > 0. Having more kurtosis than a normal distribution. It's stretched up and to the side too (2nd pic down). High kurtosis (leptokurtic) is bad as the wider tails (called heavy tails) suggest there is relatively high probability of extreme events
- Mesokurtic: K =0. Having the same kurtosis as a normal distribution
- Platykurtic: K < 0. Having less kurtosis than a normal distribution. This suggests there are light tails and fewer extreme events in the distribution
- Skewness is good: +/- 0.5 (fairly symmetrical)
- Skewness is average: -1 to -0.5 or 0.5 to 1 (moderately skewed)
- Skewness is bad: > +/- 1 (highly skewed)
Evolving ROI table:
- The table of ROI values evolve with the year and month. The sum of each year is given. Please avoid using it on non-cryptocurrencies or any market whose trading session is not 24/7
Open ROI reader:
- At the top center is the open ROI of a trade
Williams Vix Fix OHLC candles plot indicator (Tartigradia)OHLC candles plot of the Williams VixFix indicator, which allows to draw trend lines.
Williams VixFix is a realized volatility indicator developed by Larry Williams, and can help in finding market bottoms.
Indeed, as Williams describe in his paper, markets tend to find the lowest prices during times of highest volatility, which usually accompany times of highest fear. The VixFix is calculated as how much the current low price statistically deviates from the maximum within a given look-back period.
The Williams VixFix indicator is usually presented as a curve or histogram. The novelty of this indicator is to present the data as a OHLC candles plot: whereas the original Williams VixFix calculation only involves the close value, we here use the open, high and low values as well. This led to some mathematical challenges because some of these calculations led to absurd values, so workarounds had to be found, but in the end I think the result was worth it, it reproduces the VIX chart quite well.
A great additional value of the OHLC chart is that it shows not just the close value, but all the values during the session: open, high and low in addition to close. This allows to draw trend lines and can provide additional information on momentum and sentiment. In addition, other indicators can be used on it, as if it was a price chart, such as RSI indicators (see RSI+ (alt) indicator for example).
For more information on the Vix Fix, which is a strategy published under public domain:
The VIX Fix, Larry Williams, Active Trader magazine, December 2007, web.archive.org
Fixing the VIX: An Indicator to Beat Fear, Amber Hestla-Barnhart, Journal of Technical Analysis, March 13, 2015, ssrn.com
Replicating the CBOE VIX using a synthetic volatility index trading algorithm, Dayne Cary and Gary van Vuuren, Cogent Economics & Finance, Volume 7, 2019, Issue 1, doi.org
This indicator includes only the Williams VixFix as an OHLC candles or bars plot, and price / vixfix candles plot, as well as the typical vixfix histogram. Indeed, it is much more practical for unbounded range indicators to be plotted in their own separate panel, hence why this indicator is released separately, so that it can work and be scaled adequately out of the box.
Note that the there are however no bottom buy signals. For a more complete indicator, which also includes the OHLC candles plots present here, but also bottom signals and Inverse VixFix (top signals), see:
Set Index symbol to SPX, and index_current = false, and timeframe Weekly, to reproduce the original VIX as close as possible by the VIXFIX (use the Add Symbol option, because you want to plot CBOE:VIX on the same timeframe as the current chart, which may include extended session / weekends). With the Weekly timeframe, off days / extended session days should not change much, but with lower timeframes this is important, because nights and weekends can change how the graph appears and seemingly make them different because of timing misalignment when in reality they are not when properly aligned.
