Realized Profit & Loss [BigBeluga]The Realized Loss & Profit indicator aims to find potential dips and tops in price by utilizing the security function syminfo.basecurrency + "_LOSSESADDRESSES".
The primary objective of this indicator is to present an average, favorable buying/selling opportunity based on the number of people currently in profit or loss.
The script takes into consideration the syminfo.basecurrency, so it should automatically adapt to the current coin.
🔶 USAGE
Users have the option to enable the display of either Loss or Profit, depending on their preferred visualization.
Examples of displaying Losses:
Example of displaying Profits:
🔶 CONCEPTS
The concept aims to assign a score to the data in the ticker representing the realized losses. This score will provide users with an average of buying/selling points that are better to the typical investor.
🔶 SETTINGS
Users have complete control over the script settings.
🔹 Calculation
• Profit: Display people in profit on an average of the selected length.
• Loss: Display people in loss on an average of the selected length.
🔹 Candle coloring
• True: Color the candle when data is above the threshold.
• False: Do not color the candle.
🔹 Levels
- Set the level of a specific threshold.
• Low: Low losses (green).
• Normal: Low normal (yellow).
• Medium: Low medium (orange).
• High: Low high (red).
🔹 Z-score Length: Length of the z-score moving window.
🔹 Threshold: Filter out non-significant values.
🔹 Histogram width: Width of the histogram.
🔹 Colors: Modify the colors of the displayed data.
🔶 LIMITATIONS
• Since the ticker from which we obtain data works only on the daily timeframe, we are
restricted to displaying data solely from the 1D timeframe.
• If the coin does not have any realized loss data, we can't use this script.
在腳本中搜尋"如何用wind搜索股票的发行价和份数"
InteliTrend StableFXThis appealing little tool is a derivation of the CCI indicator and was developed in 2023 by Mario Jemic for MT4. It has additional settings that the conventional CCI indicator does not have. Furthermore, it is combined with moving averages to create signals. This is lines crossing confirmation type indicator. Look for the orange line to cross the moving average (red line).
Differences from the original:
1. Though it was coded in 2023, the original is for people who are still running Windows 95 and would like to do technical analysis on MT4.
2. The original had an additional stochastic moving average that was not particularly useful and made the indicator busy.
3. All of the moving average options have been ported over with 2 additional choices. (Hull and Arnaud Legoux added).
4. The default options are set as the tweaks that were discovered by StoneHill Forex (stonehillforex.com). You can also download the original from them.
I will probably add a few more features and options in the near future such as visuals for crossovers etc.
Enjoy!
d1g1talshad0w
ICT Killzones + Pivots [TFO]Designed with the help of TTrades and with inspiration from the ICT Everything indicator by coldbrewrosh, the purpose of this script is to identify ICT Killzones while also storing their highs and lows for future reference, until traded through.
There are 5 Killzones / sessions whose times and labels can all be changed to one's liking. Some prefer slight alterations to traditional ICT Killzones, or use different time windows altogether. Either way, the sessions are fully customizable. The sessions will auto fit to keep track of the highs and lows made during their respective times, and these pivots will be extended until they are invalidated.
There are also 4 optional Open Price lines and 4 vertical Timestamps, where the user can change the time and style of each one as well.
To help maintain a clean chart, we can implement a Cutoff Time where all drawings will stop extending past a certain point. The indicator will apply this logic by default, as it can get messy with multiple drawings starting and stopping throughout the day at different times.
Given the amount of interest I've received about this indicator, I intend to leave it open to suggestions for further improvements. Let me know what you think & what you want to see added!
RSI Dot Party - All Lengths From 1 To 120The RSI Dot Party indicator displays all RSI lengths from 1 to 120 as different colored dots on the chart.
🔶 Purpose
Show the reversal point of price action to time entries and exits.
🔶 USAGE
When a dot displays it is a indication of the reversal of the price/trend. The larger the dot the more likely it is to reverse.
The Default settings generates dots for extreme cases where the RSI is over = 90 or under = 10 for every RSI length in the range of 1-120.
Example if the RSI of length 1 or 2 or 3 or 4 or ... or 15 or 16 or 17 or ... or 80 or 81 or 82 or ... if any of does RSI crosses a boundary a dot is shown.
A boundary is the over/under the RSI oscillates in.
Customize the settings until the dots match up with the high and lows of past price action.
🔶 SETTINGS
🔹 Source
Source 1: Is the First Source RSI is calculated from
Source 2: Is the Second Source RSI is calculated from
🔹 Meta Settings
Hours back to draw: To speed up the script calculate it only draws a set number of hours back, default is 300 hours back in time to draw then it cuts off.
Show Dots: Show or disable dots
Show Bar Color: Color the bars for each RSI incident
Filter Cross: Filters and only shows dots when the RSI crosses above or bellow a boundary. If not all candles above or bellow the boundaries will display a dot.
Dots Location Absolute: Instead of showing the dots above or bellow the candle, the dots will show up on the top and bottom of the window.
🔹 7 RSI Groups
There are a total of 7 RSI colors.
Range Very Tiny: Default Color Green
Range Tiny: Default Color Purple
Range Small: Default Color Yellow
Range Normal: Default Color Red
Range Large: Default Color Blue
Range Huge: Default Color Dark Purple
Range Very Huge: Default Color White
🔹 RSI Group Settings
Hi/Low Color: Change the Color of that group.
Start/End: The Start and End range of this RSI color. Example if start = 5 and end = 10 the RSI of 5,6,7,8,9,10 will be displayed on the chart for that color, if any of does RSI goes above or bellow the boundary a dot is displayed on that candle.
Delay: The RSI needs to be above or bellow a boundary for x number of candles before displaying a dot. For example if delay = 2 and the RSI is over = 70 for 2 candles then it will display a dot.
Under/Over: Boundaries that indicate when to draw a dot, if over = 70 and RSI crosses above 70 a dot is displayed.
🔹 Show
Section that allows you to disable RSI grounds you dont want to see, this also removes them from the alert signal generated.
Show Low: Show or disable Low RSI dots
Show High: Show or disable High RSI dots
🔶 ALERTS
Alert for all New RSIs Dots Created in real time
The alert generated depends on what groups are showing or not, if the green group is disabled for example the alert will not be generated.
🔶 Warning
When a dot shows up it can continue moving. For example if a purple dot shows itself above a 15 minute candle, if that candle/price continue to extend up the dot will move up with it.
Dots can also disappear occasionally if the RSI moves in and out of a boundary within that candles life span.
🔶 Community
I hope you guys find this useful, if you have any questions or feature requests leave me a comment! Take care :D
RelativeValue█ OVERVIEW
This library is a Pine Script™ programmer's tool offering the ability to compute relative values, which represent comparisons of current data points, such as volume, price, or custom indicators, with their analogous historical data points from corresponding time offsets. This approach can provide insightful perspectives into the intricate dynamics of relative market behavior over time.
█ CONCEPTS
Relative values
In this library, a relative value is a metric that compares a current data point in a time interval to an average of data points with corresponding time offsets across historical periods. Its purpose is to assess the significance of a value by considering the historical context within past time intervals.
For instance, suppose we wanted to calculate relative volume on an hourly chart over five daily periods, and the last chart bar is two hours into the current trading day. In this case, we would compare the current volume to the average of volume in the second hour of trading across five days. We obtain the relative volume value by dividing the current volume by this average.
This form of analysis rests on the hypothesis that substantial discrepancies or aberrations in present market activity relative to historical time intervals might help indicate upcoming changes in market trends.
Cumulative and non-cumulative values
In the context of this library, a cumulative value refers to the cumulative sum of a series since the last occurrence of a specific condition (referred to as `anchor` in the function definitions). Given that relative values depend on time, we use time-based conditions such as the onset of a new hour, day, etc. On the other hand, a non-cumulative value is simply the series value at a specific time without accumulation.
Calculating relative values
Four main functions coordinate together to compute the relative values: `maintainArray()`, `calcAverageByTime()`, `calcCumulativeSeries()`, and `averageAtTime()`. These functions are underpinned by a `collectedData` user-defined type (UDT), which stores data collected since the last reset of the timeframe along with their corresponding timestamps. The relative values are calculated using the following procedure:
1. The `averageAtTime()` function invokes the process leveraging all four of the methods and acts as the main driver of the calculations. For each bar, this function adds the current bar's source and corresponding time value to a `collectedData` object.
2. Within the `averageAtTime()` function, the `maintainArray()` function is called at the start of each anchor period. It adds a new `collectedData` object to the array and ensures the array size does not exceed the predefined `maxSize` by removing the oldest element when necessary. This method plays an essential role in limiting memory usage and ensuring only relevant data over the desired number of periods is in the calculation window.
3. Next, the `calcAverageByTime()` function calculates the average value of elements within the `data` field for each `collectedData` object that corresponds to the same time offset from each anchor condition. This method accounts for cases where the current index of a `collectedData` object exceeds the last index of any past objects by using the last available values instead.
4. For cumulative calculations, the `averageAtTime()` function utilizes the `isCumulative` boolean parameter. If true, the `calcCumulativeSeries()` function will track the running total of the source data from the last bar where the anchor condition was met, providing a cumulative sum of the source values from one anchor point to the next.
To summarize, the `averageAtTime()` function continually stores values with their corresponding times in a `collectedData` object for each bar in the anchor period. When the anchor resets, this object is added to a larger array. The array's size is limited by the specified number of periods to be averaged. To correlate data across these periods, time indexing is employed, enabling the function to compare corresponding points across multiple periods.
█ USING THIS LIBRARY
The library simplifies the complex process of calculating relative values through its intuitive functions. Follow the steps below to use this library in your scripts.
Step 1: Import the library and declare inputs
Import the library and declare variables based on the user's input. These can include the timeframe for each period, the number of time intervals to include in the average, and whether the calculation uses cumulative values. For example:
//@version=5
import TradingView/RelativeValue/1 as TVrv
indicator("Relative Range Demo")
string resetTimeInput = input.timeframe("D")
int lengthInput = input.int(5, "No. of periods")
Step 2: Define the anchor condition
With these inputs declared, create a condition to define the start of a new period (anchor). For this, we use the change in the time value from the input timeframe:
bool anchor = timeframe.change(resetTimeInput)
Step 3: Calculate the average
At this point, one can calculate the average of a value's history at the time offset from the anchor over a number of periods using the `averageAtTime()` function. In this example, we use True Range (TR) as the `source` and set `isCumulative` to false:
float pastRange = TVrv.averageAtTime(ta.tr, lengthInput, anchor, false)
Step 4: Display the data
You can visualize the results by plotting the returned series. These lines display the non-cumulative TR alongside the average value over `lengthInput` periods for relative comparison:
plot(pastRange, "Past True Range Avg", color.new(chart.bg_color, 70), 1, plot.style_columns)
plot(ta.tr, "True Range", close >= open ? color.new(color.teal, 50) : color.new(color.red, 50), 1, plot.style_columns)
This example will display two overlapping series of columns. The green and red columns depict the current TR on each bar, and the light gray columns show the average over a defined number of periods, e.g., the default inputs on an hourly chart will show the average value at the hour over the past five days. This comparative analysis aids in determining whether the range of a bar aligns with its typical historical values or if it's an outlier.
█ NOTES
• The foundational concept of this library was derived from our initial Relative Volume at Time script. This library's logic significantly boosts its performance. Keep an eye out for a forthcoming updated version of the indicator. The demonstration code included in the library emulates a streamlined version of the indicator utilizing the library functions.
• Key efficiencies in the data management are realized through array.binary_search_leftmost() , which offers a performance improvement in comparison to its loop-dependent counterpart.
• This library's architecture utilizes user-defined types (UDTs) to create custom objects which are the equivalent of variables containing multiple parts, each able to hold independent values of different types . The recently added feature was announced in this blog post.
• To enhance readability, the code substitutes array functions with equivalent methods .
Look first. Then leap.
█ FUNCTIONS
This library contains the following functions:
calcCumulativeSeries(source, anchor)
Calculates the cumulative sum of `source` since the last bar where `anchor` was `true`.
Parameters:
source (series float) : Source used for the calculation.
anchor (series bool) : The condition that triggers the reset of the calculation. The calculation is reset when `anchor` evaluates to `true`, and continues using the values accumulated since the previous reset when `anchor` is `false`.
Returns: (float) The cumulative sum of `source`.
averageAtTime(source, length, anchor, isCumulative)
Calculates the average of all `source` values that share the same time difference from the `anchor` as the current bar for the most recent `length` bars.
Parameters:
source (series float) : Source used for the calculation.
length (simple int) : The number of reset periods to consider for the average calculation of historical data.
anchor (series bool) : The condition that triggers the reset of the average calculation. The calculation is reset when `anchor` evaluates to `true`, and continues using the values accumulated since the previous reset when `anchor` is `false`.
isCumulative (simple bool) : If `true`, `source` values are accumulated until the next time `anchor` is `true`. Optional. The default is `true`.
Returns: (float) The average of the source series at the specified time difference.
David Varadi Intermediate OscillatorThe David Varadi Intermediate Oscillator (DVI) is a composite momentum oscillator designed to generate trading signals based on two key factors: the magnitude of returns over different time windows and the stretch, which measures the relative number of up versus down days. By combining these factors, the DVI aims to provide a reliable and objective assessment of market trends and momentum.
Methodology:
To calculate the DVI, a specific formula is applied. The magnitude component involves averaging smoothed returns over various lengths, weighted according to user-defined parameters. This calculation helps determine the magnitude of price changes. The stretch component follows a similar process, averaging smoothed returns over different lengths to gauge market momentum. Users have the flexibility to adjust the weights and lengths to suit their trading preferences and styles.
Utility:
The DVI offers versatility in its applications. It can be used for both momentum trading and trend analysis due to its smooth and consistent signals. Unlike some other oscillators, the DVI provides longer and uncorrelated signals, allowing traders to effectively combine trend-following and mean-reversion strategies. For example, the DVI is adept at identifying overbought levels above the 200-day moving average, serving as a useful tool for determining exit points during price strength and even potential shorting opportunities. Traders can develop simple trading systems based on the DVI, buying above the 200-day moving average and selling when the DVI exceeds a specified threshold. Conversely, they can consider short positions below the 200-day moving average and cover when the DVI falls below a specific threshold. The DVI's objective approach to analyzing market momentum makes it a valuable resource for traders seeking to identify trading opportunities.
Key Features:
Bar coloring: based on Trend, Extremeties or Reversions
Reversions: Potential reversal points marked with triangles above\below oscillator
Extremity Hues: Highlighting oxcillator reaching traditional OB\OS levels
Example Charts:
bar viewBar view is a simple script to show other higher time frame windows while you are focusing on lower time for precise decision making.
For example you are currently operating at 1 minute time frame and you want to see other bars on higher time frames e.g. 5 minute, 15 minutes etc.
Feel free to add multiple bar view to see different time frames.
Hui-Heubel Liquidity RatioThe Hui-Heubel Liquidity Ratio (lhh) is a measurement of market resiliency and liquidity. Higher values indicate a more liquid and resilient market, lower values indicate a more fragile market susceptible to volatile moves. It does not work on all tickers (for example, if something does not report volume).
Generally, you will see lhh rise when stocks sell off and fall when they are bought. Occasionally you will see scenarios where price will go up while lhh does as well, often this is a symptom of short covering.
Includes two configurable SMAs and a configurable lookback window.
Buy / Sell Fractal Algorithm with SL Line GenerationThis algorithm is designed for usage across indices.
How it works?
The algorithm uses a variation of fractals, momentum, RSI and LRSI to determine a trends direction.
The Relative Strength Index (RSI) is a momentum-based oscillator used to measure the speed (velocity) and change (magnitude) of directional price movements. It provides a visual means to monitor both the current and historical strength and weakness of a particular market. The strength or weakness is based on closing prices over the duration of a specified trading period, creating a reliable metric of price and momentum changes
Momentum in trading refers to the direction and magnitude of price. Momentum plays a key role in assessing trend strength, and it is important to know when a trend is slowing down. Less momentum does not always lead to a reversal, but it does signal that something is changing, and the trend may consolidate or reverse
Fractals are patterns within price changes which are repeated across thousands of bars. Examples of fractals include the golden ratio, PHI and the spirals of the milk way. They are quite literally a universal concept.
Basics of usage:
When a bullish trend is detected; the algorithm will generate a green "SL Line" at a calculated point, which can be interpreted as an invalidation line.
If the price goes below this line, the bullish trend is invalidated. So long as it holds, the bullish trend is true until the next detection change.
When a bearish trend is detected; the algorithm will generate a red "SL Line", at a calculated point, which can be interpreted as an invalidation line.
If the prices goes above this line, the bearish trend is invalidated. So long as it holds, the bearish trend is true until the next detection change.
When a given trend is invalidated, the SL Line turns yellow and you enter a "pause zone", where neither a bearish nor bullish trend is calculated.
This resets itself on the next trend detection.
Additional information:
I have coded my own backtest to this algorithm, along with plotting the profit / loss of each generated trade.
The profit is calculated by the difference between the open bar of the trade after a long ( or short ) and the following trade.
If we are calculating a short, the resulting value is then multiplied by -1 to get a positive integer.
For calculating a loss we take the value of the open bar of the trade that generates a long, and take the difference between this and the SL line, and similarly for short positions. The code assumes the user is placing their SL at the indicated line.
Within the input settings there are a few customisation options:
Alpha & Fractal Energy Length & Source - Should not be changed.
Highly bands crossover? - Has no visible effect whether on or off. It refers to the fractal chart which in this iteration is not visible and rather a backend mechanic.
Apply fractal energy? - Should generally be left turned on. This is a noise reduction. Disabling will result in over-trading.
Apply normalization? - Has no impact, is solely used to make the fractal values more human-readable rather than decimal format.
Offset - refers to the offset value of the SL Line generations. This should be set to a value that gives you enough breathing room, and remember to include any spreads! Default is 0.2, written in %
Trading hours - This simply gives a session input for the trading hours you want to trade within, and then colours the background green for that session. Trading 24/7 is never a wise strategy, stick to whatever is most optimal for you.
Leverage - Whatever leverage you are using. Default is x20. This will affect the profit / loss calculations accordingly.
Start equity - refers to the equity value you want to backtest with. Some assets will generate NA for this in the backtest label explained later.
Label customisation options.
Note that the backtest label is by default hidden, and appears when you hover over the black label at the current bar. When enabled to visible, it will show a large text label that may cover your chart screen more than you wish.
Alerts -
There are dozens of alert functionalities here; first are the timeframe assignments for each alert, set by default to 2hrs.
These timeframes then affect the asset you select in the corresponding setting.
In total there are 8 additional assets you can set alerts for.
Once you have assigned the timeframe and asset for an alert, you can then check the tick box for that individual alert.
Once done, you set the alert as normal through the tradingview alerts window. Remember to set "alert function calls only"
-
Timers:
I have added some functionality for timers to be set, values are in minutes. These work on the exact time of placement. Do not change the extra symbol formula option.
-
Note that this backtest is not intended as a replacement for tradingview backtest, nor is there a guarantee that historical results are to be replicated in the future. Trading is inherently risky.
Moonhub Cycle IndexMoonhub Cycle Index is a composite index derived from three popular technical analysis indicators: Moving Average Convergence Divergence (MACD), Schaff Trend Cycle (STC), and Detrended Price Oscillator (DPO). The indicator is designed to help identify potential trends and market sentiment by combining the unique characteristics of each indicator.
Key components of the indicator include:
Input Parameters:
COEMA Length (len_DIema): The length of the Exponential Moving Average (EMA) applied to the Custom Index. Default is set to 9.
COSMA Length (len_DIsma): The length of the Simple Moving Average (SMA) applied to the Custom Index. Default is set to 30.
Indicators:
MACD: A momentum oscillator that shows the relationship between two moving averages of a security's price. It is calculated using the difference between the 12-period and 26-period EMA, and a 9-period EMA (signal line) of the MACD.
STC: A cyclic indicator that identifies cyclical trends in the market. It is calculated using the Stochastic oscillator formula applied to the close, high, and low prices over a 10-period lookback window.
DPO: A price oscillator that eliminates the trend from price data to focus on underlying cycles. It is calculated using a custom function that shifts the price by half the length and subtracts the SMA from the shifted price.
Custom Index: The composite index is calculated by taking the average of the MACD line, STC, and DPO.
COEMA and COSMA: Exponential and Simple Moving Averages applied to the Custom Index using the lengths specified by the input parameters (len_DIema and len_DIsma).
Plots: The Custom Index, COEMA, and COSMA are plotted with different colors and line widths to visualize their interaction and provide insights into potential market trends.
This Custom Index Indicator can be useful for traders who want to analyze the market using a combination of these indicators to make more informed decisions. It can also help identify potential trends and market sentiment by combining the unique characteristics of each indicator.
ICT Algorithmic Macro Tracker° (Open-Source) by toodegreesDescription:
The ICT Algorithmic Macro Tracker° Indicator is a powerful tool designed to enhance your trading experience by clearly and efficiently plotting the known ICT Macro Times on your chart.
Based on the teachings of the Inner Circle Trader , these Time windows correspond to periods when the Interbank Price Delivery Algorithm undergoes a series of checks ( Macros ) and is probable to move towards Liquidity.
The indicator allows traders to visualize and analyze these crucial moments in NY Time:
- 2:33-3:00
- 4:03-4:30
- 8:50-9:10
- 9:50-10:10
- 10:50-11:10
- 11:50-12:10
- 13:10-13:50
- 15:15-15:45
By providing a clean and clutter-free representation of ICT Macros, this indicator empowers traders to make more informed decisions, optimize and build their strategies based on Time.
Massive shoutout to @reastruth for his ICT Macros Indicator , and for allowing to create one of my own, go check him out!
Indicator Features:
– Track ongoing ICT Macros to aid your Live analysis.
- Gain valuable insights by hovering over the plotted ICT Macros to reveal tooltips with interval information.
– Plot the ICT Macros in one of two ways:
"On Chart": visualize ICT Macro timeframes directly on your chart, with automatic adjustments as Price moves.
Pro Tip: toggle Projections to see exactly where Macros begin and end without difficulty.
"New Pane": move the indicator two a New Pane to see both Live and Upcoming Macro events with ease in a dedicated section
Pro Tip: this section can be collapsed by double-clicking on the main chart, allowing for seamless trading preparation.
This indicator is available only on the TradingView platform.
⚠️ Open Source ⚠️
Coders and TV users are authorized to copy this code base, but a paid distribution is prohibited. A mention to the original author is expected, and appreciated.
⚠️ Terms and Conditions ⚠️
This financial tool is for educational purposes only and not financial advice. Users assume responsibility for decisions made based on the tool's information. Past performance doesn't guarantee future results. By using this tool, users agree to these terms.
Nadaraya-Watson OscillatorThis indicator is based on the work of @jdehorty and his amazing Nadaraya-Watson Kernel Envelope, which you can see here:
General Description
The Nadaraya-Watson Oscillator (NWO) will give the same information as the Nadaraya-Watson Envelope, but as an oscillator off the main chart, by plotting the relationship between price and the Kernel and its bands. This also means that we can now detect divergences between price and the NWO.
You can see the relationship between the two here:
You can think of this indicator as the kernel envelope version of a Bollinger Band %B. In ranging markets the bands are perfect for mean reversion trades, but in certain situations the break of one of the bands can signal the beggining of a strong trend and price will remain close to the bands for a long period and will only give small pullbacks. As with any indicator, confluence with price and other tools must be taken into account.
Main Features
As with @jdehorty 's Envelope, you can change the following settings:
Lookback Window.
Relative Weighting.
The initial bar for the regression.
ATR period for the bands.
Inner and Outer Multiples for the bands.
I also added the following:
A middle band around the Kernel to filter out false crossovers.
A Hull Moving Average to smoothen out the movements of the oscillator and give extra confirmation of turnover points.
Colors
Some special things to note regarding the coloring:
The zero line features a gradient that changes color every time the Kernel slope changes direction.
The Oscillator plot has a gradient coloring that gets stronger the closer it gets to each of the bands.
Every time the oscillator crosses over/under the outer bands the background will be highlighted.
Happy trading!
Ehlers Undersampled Double Moving Average Indicator [CC]The Undersampled Double Moving Average was created by John Ehlers (Stocks and Commodities April 2023), and this is a double moving average system which is pretty rare for John Ehlers. For those of you who would like my other take on an Ehlers double moving average, be sure to check out my previous Ehlers double moving average script . He came up with a unique idea for this indicator to create a moving average using a sample of the price data. For example, we use his suggested length of 5 only to use the price data every 5 bars. Feel free to change this, and please let me know if you find a length that works better. He then smooths the indicator using the Hann Windowed Moving Average . I color-coded the lines to show stronger signals in darker colors or standard signals in lighter colors. Buy when the line turns green and sell when it turns red.
Let me know if there is an indicator or script you would like to see me publish!
Vector2ArrayLibrary "Vector2Array"
functions to handle vector2 Array operations.
.
references:
docs.unity3d.com
gist.github.com
github.com
gist.github.com
gist.github.com
gist.github.com
.
from(source, prop_sep, vect_sep)
Generate array of vector2 from string.
Parameters:
source : string Source string of the vectors.
prop_sep : string Separator character of the vector properties (x`,`y).
vect_sep : string Separator character of the vectors ((x,y)`;`(x,y)).
Returns: array.
max(vectors)
Combination of the highest elements in column of a array of vectors.
Parameters:
vectors : array, Array of Vector2 objects.
Returns: Vector2.Vector2, Vector2 object.
-> usage:
`a = Vector2.from(1.0) , b = Vector2.from(2.0), c = Vector2.from(3.0), d = max(array.from(a, b, c)) , plot(d.x)`
min(vectors)
Combination of the lowest elements in column of a array of vectors.
Parameters:
vectors : array, Array of Vector2 objects.
Returns: Vector2.Vector2, Vector2 object.
-> usage:
`a = Vector2.from(1.0) , b = Vector2.from(2.0), c = Vector2.from(3.0), d = min(array.from(a, b, c)) , plot(d.x)`
sum(vectors)
Total sum of all vectors.
Parameters:
vectors : array, ID of the vector2 array.
Returns: Vector2.Vector2, vector2 object.
-> usage:
`a = Vector2.from(1.0) , b = Vector2.from(2.0), c = Vector2.from(3.0), d = sum(array.from(a, b, c)) , plot(d.x)`
center(vectors)
Finds the vector center of the array.
Parameters:
vectors : array, ID of the vector2 array.
Returns: Vector2.Vector2, vector2 object.
-> usage:
`a = Vector2.from(1.0) , b = Vector2.from(2.0), c = Vector2.from(3.0), d = center(array.from(a, b, c)) , plot(d.x)`
rotate(vectors, center, degree)
Rotate Array vectors around origin vector by a angle.
Parameters:
vectors : array, ID of the vector2 array.
center : Vector2.Vector2 , Vector2 object. Center of the rotation.
degree : float , Angle value.
Returns: rotated points array.
-> usage:
`a = Vector2.from(1.0) , b = Vector2.from(2.0), c = Vector2.from(3.0), d = rotate(array.from(a, b, c), b, 45.0)`
scale(vectors, center, rate)
Scale Array vectors based on a origin vector perspective.
Parameters:
vectors : array, ID of the vector2 array.
center : Vector2.Vector2 , Vector2 object. Origin center of the transformation.
rate : float , Rate to apply transformation.
Returns: rotated points array.
-> usage:
`a = Vector2.from(1.0) , b = Vector2.from(2.0), c = Vector2.from(3.0), d = scale(array.from(a, b, c), b, 1.25)`
move(vectors, center, rate)
Move Array vectors by a rate of the distance to center position (LERP).
Parameters:
vectors : array, ID of the vector2 array.
center
rate
Returns: Moved points array.
-> usage:
`a = Vector2.from(1.0) , b = Vector2.from(2.0), c = Vector2.from(3.0), d = move(array.from(a, b, c), b, 1.25)`
to_string(id, separator)
Reads a array of vectors into a string, of the form ` `.
Parameters:
id : array, ID of the vector2 array.
separator : string separator for cell splitting.
Returns: string Translated complex array into string.
-> usage:
`a = Vector2.from(1.0) , b = Vector2.from(2.0), c = Vector2.from(3.0), d = to_string(array.from(a, b, c))`
to_string(id, format, separator)
Reads a array of vectors into a string, of the form ` `.
Parameters:
id : array, ID of the vector2 array.
format : string , Format to apply transformation.
separator : string , Separator for cell splitting.
Returns: string Translated complex array into string.
-> usage:
`a = Vector2.from(1.234) , b = Vector2.from(2.23), c = Vector2.from(3.1234), d = to_string(array.from(a, b, c), "#.##")`
EMA CO AlertEMAs play an important role in identifying the mood of the market.
Frequently used short term EMA is 5 and long term EMA is 50.
This script detects the crossover (+ve and -ve) and generates alerts accordingly.
Steps to apply:
1) Open the script on a desired timeframe.
2) Add this indicator on the chart
3) Choose the values of the 2 EMAs from settings
4) Go to the alert window.
5) Select this indicator from the 'Condition' dropdown
6) Create the alert.
This alert will then run in the background and notify you.
Need to apply a one time alert on the scripts.
In addition to above, you can also add this indicator on the chart and it will show green/red lines on the chart for signals.
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.
RSI Impact Heat Map [Trendoscope]Here is a simple tool to measure and display outcome of certain RSI event over heat map.
🎲 Process
🎯Event
Event can be either Crossover or Crossunder of RSI on certain value.
🎯Measuring Impact
Impact of the event after N number of bars is measured in terms of highest and lowest displacement from the last close price. Impact can be collected as either number of times of ATR or percentage of price. Impact for each trigger is recorded separately and stored in array of custom type.
🎯Plotting Heat Map
Heat map is displayed using pine tables. Users can select heat map size - which can vary from 10 to 90. Selecting optimal size is important in order to get right interpretation of data. Having higher number of cells can give more granular data. But, chart may not fit into the window. Having lower size means, stats are combined together to get less granular data which may not give right picture of the results. Default value for size is 50 - meaning data is displayed in 51X51 cells.
Range of the heat map is adjusted automatically based on min and max value of the displacement. In order to filter out or merge extreme values, range is calculated based on certain percentile of the values. This will avoid displaying lots of empty cells which can obscure the actual impact.
🎲 Settings
Settings allow users to define their event, impact duration and reference, and few display related properties. The description of these parameters are as below:
🎲 Use Cases
In this script, we have taken RSI as an example to measure impact. But, we can do this for any event. This can be price crossing over/under upper/lower bollinger bands, moving average crossovers or even complex entry or exit conditions. Overall, we can use this to plot and evaluate our trade criteria.
🎲 Interpretation
Q1 - If more coloured dots appear on the top right corner of the table, then the event is considered to trigger high volatility and high risk environment.
Q2 - If more coloured dots appear on the top left corner, then the events are considered to trigger bearish environment.
Q3 - If more coloured dots appear on the bottom left corner of the chart, then the events are considered insignificant as they neither generate higher displacement in positive or negative side. You can further alter outlier percentage to reduce the bracket and hence have higher distribution move towards
Q4 - If more coloured dots appear on the bottom right corner, then the events are considered to trigger bullish environment.
Will also look forward to implement this as library so that any conditions or events can be plugged into it.
Harmonic Patterns Based Trend FollowerEarlier this week, published an idea on how harmonic patterns can be used for trend following. This script is an attempt to implement the same.
🎲 Process
🎯 Derive Zigzag and scan harmonic patterns for last 5 confirmed pivots
🎯 If a pattern is found, highest point of pattern will become the bullish zone and lower point of the pattern will become bearish zone.
🎯 Since it is trend following method, when price reaches bullish zone, then the trend is considered as bullish and when price reaches bearish zone, the trend is considered as bearish.
🎯 If price does not touch both regions, then trend remains unchanged.
🎯 Bullish and bearish zone will change as and when new patterns are formed.
🎲 Note
Patterns are not created on latest pivot as last pivot will be unconfirmed and moving. Due to this, patterns appear after certain delay - patterns will not be real time. But, this is expected and does not impact the overall process.
When new pattern formed
When price breaks over the zones
🎲 Output
🎯 Patterns formed are drawn in blue coloured lines. Due to pine limitation of max 500 lines, older patterns automatically get deleted when new ones come.
🎯 Bullish Zone and Bearish Zone are plotted in green and red colours and the zone will change whenever new pattern comes along.
🎯 Bar colors are changed according to calculated trend. Trend value can be 1 or -1 based on the current trend. You can also find the value in data window.
🎯 For simplicity purpose, input option for selection of specific patterns are not provided and also pattern names are not displayed on the chart.
Band Based Trend FilterSimilar to RelativeBandwidthFilter , this script is also a simple trend filter which can be used to define your trading zone.
🎲 Concept
On contrary to reversal mindset, we define trend when price hits either side of the band. If close price hits upper band then it is considered as bullish and if close price hits lower band, then it is considered bearish. Further, trend strength is measured in terms of how many times the price hits one side of the band without hitting other side. Hit is counted only if price has touched middle line in between the touches. This way price walks on the bands are considered as just one hit.
🎲 Settings
Settings are minimal and details can be found in the tooltips against each parameters
🎲 Usage
This can be used with your own strategy to filter your trading/non-trading zones based on trend . Script plots a variable called "Trend" - which is not shown on chart pane. But, it is available in the data window. This can be used in another script as external input and apply logic.
Trend values can be
1 : Allow only Long
-1 : Allow only short
0 : Do not allow any trades
Root Mean Square (RMS)The Root Mean Square (RMS) is a statistical measure of the magnitude of a set of numbers. It is a type of mean, or average, that is calculated by taking the square root of the sum of the squares of a set of numbers, divided by the number of items in the set. The RMS is often used to measure the magnitude of a time-varying signal, such as a waveform or a time series data.
The indicator takes in two input parameters: the source data and the length of the RMS window. The source data can be any time series data, such as the closing price of a security, and the length parameter determines the number of data points used in the RMS calculation.
The script begins by declaring the RMS indicator function and specifying that it should be plotted as an overlay on the chart. The function takes in two parameters: source and length. The source parameter is the time series data that will be used in the RMS calculation, and the length parameter determines the number of data points to include in the calculation.
Next, the script defines the RMS function using a single line of code. The function calculates the RMS by taking the square root of the sum of the squares of the source data, divided by the length. This is done using the built-in math.sqrt, math.sum, and math.pow functions, which respectively calculate the square root, sum, and power of a set of numbers.
Finally, the script defines the source and length input parameters using the input.source and input.int functions. The source parameter is defined as the closing price of the security, and the length parameter is defined as an integer with a default value of 20.
The RMS indicator implemented in this script can be used to measure the magnitude of a time-varying signal. By adjusting the length parameter, users can control the number of data points included in the RMS calculation and fine-tune the indicator to their specific needs.
Minervini QualifierThe Minervini Qualifier indicator calculates the qualifying conditions from Mark Minervini’s book “Trade like a Stock Market Wizard”.
The condition matching is been shown as fill color inside an SMA 20day envelope curve.
If the envelope color is red, current close price is below the SMA20 and when blue, current close price is above the SMA20. The fill color can be transparent (not matching qualifying conditions), yellow (matching all conditions except close is still below SMA50), green (all conditions match, SMA200 trending for at least one month up) or blue (all conditions match, SMA200 trending up for at least 5 months)
As I wanted also to see which of the qualifying conditions match over time, I’ve added add. lines, each representing one conditions. If it matches, line color is blue, or red if not. Use the data windows (right side), so you know what line represents which condition. Can be turned on/off (default:on)
In addition, a relative strength is been calculated, to compare the stock to a reference index. It is just one possible way to calculate it, might be different to what Mark Minervini is using. If the shown value (top right) is above 100, stock performs better compared to reference index (can be set in settings), when below 100, stock performs worse compared to reference index. Can be turned on/off (default:on)
How to use it:
For more details, read Mark’s book and watch his videos.
Limitations:
It gives only useful information on daily timeframe
(No financial advise, for testing purposes only)
Volume DeltaThis script is meant to only show you the most significant volume moves. The way it works is it takes the cumulative sum of the delta of the volume. You can go from current all the way to ten bars back in your delta window.
Review on what volume is: The Volume indicator measures how much of a given financial asset has traded in a specific period of time. Volume is measured by shares traded for stocks, whereas for futures, it is based on the number of contracts.
Nadaraya-Watson non repainting [LPWN]// ENGLISH
The problem of the wonderfuls Nadaraya-Watson indicators is that they repainting, @jdehorty made an aproximation of the Nadaraya-Watson Estimator using raational Quadratic Kernel so i used this indicator as inspiration i just added the Upper and lower band using ATR with this we get an aproximation of Nadaraya-Watson Envelope without repainting
Settings:
Bandwidth. This is the number of bars that the indicator will use as a lookback window.
Relative Weighting Parameter. The alpha parameter for the Rational Quadratic Kernel function. This is a hyperparameter that controls the smoothness of the curve. A lower value of alpha will result in a smoother, more stretched-out curve, while a lower value will result in a more wiggly curve with a tighter fit to the data. As this parameter approaches 0, the longer time frames will exert more influence on the estimation, and as it approaches infinity, the curve will become identical to the one produced by the Gaussian Kernel.
Color Smoothing. Toggles the mechanism for coloring the estimation plot between rate of change and cross over modes.
ATR Period. Period to calculate the ATR (upper and lower bands)
Multiplier. Separation of the bands
// SPANISH
El problema de los maravillosos indicadores de Nadaraya-Watson es que repintan, @jdehorty hizo una aproximación delNadaraya-Watson Estimator usando un Kernel cuadrático racional, así que usé este indicador como inspiración y solo agregamos la banda superior e inferior usando ATR con esto obtenemos una aproximación de Nadaraya-Watson Envelope sin volver a pintar
Configuración:
Banda ancha. Este es el número de barras que el indicador utilizará como ventana retrospectiva.
Parámetro de ponderación relativa. El parámetro alfa para la función Rational Quadratic Kernel. Este es un hiperparámetro que controla la suavidad de la curva. Un valor más bajo de alfa dará como resultado una curva más suave y estirada, mientras que un valor más bajo dará como resultado una curva más ondulada con un ajuste más ajustado a los datos. A medida que este parámetro se acerque a 0, los marcos de tiempo más largos ejercerán más influencia en la estimación y, a medida que se acerque al infinito, la curva será idéntica a la que produce el Gaussian Kernel.
Suavizado de color. Alterna el mecanismo para colorear el gráfico de estimación entre la tasa de cambio y los modos cruzados.
Período ATR. Periodo para calcular el ATR (bandas superior e inferior)
Multiplicador. Separación de las bandas
