Custom Buy BID StrategyThis Pine Script strategy is designed to identify and capitalize on upward trends in the market using the Average True Range (ATR) as a core component of the analysis. The script provides the following features:
Customizable ATR Calculation: Users can switch between different methods of ATR calculation (traditional or simple moving average).
Adjustable Parameters: The strategy allows for adjustable ATR periods, ATR multipliers, and custom time windows for executing trades.
Buy Signal Alerts: The strategy generates buy signals when the market shifts from a downtrend to an uptrend, based on ATR and price action.
Profit and Stop-Loss Management: Built-in take profit and stop-loss conditions are calculated as a percentage of the entry price, allowing for automatic position management.
Visual Enhancements: The script highlights the uptrend with green lines and optionally colors bars to help visualize market direction.
Flexible Timeframe: Users can configure a specific date range to activate the strategy, offering more control over when trades are executed.
This strategy is ideal for traders looking to automate their buy entries and manage risk with a straightforward trend-following approach. By utilizing customizable settings, it adapts to various market conditions and timeframes.
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Decline and Rise Detective [CHE]Decline and Rise Detective
TradingView Indicator (Best Timeframe: 1H or Higher)
1. Introduction
The "Decline and Rise Detective " is a TradingView indicator designed to identify the hours within a trading day that experience the largest price declines and rises. This indicator provides a visual representation of this data, offering traders valuable insights into the most frequent hours for significant price movements. It is most effective when used with a timeframe of 1 hour or greater.
2. Key Features of the Indicator
2.1. Display Options
Display Option: Users can choose between two display options:
Label: Displays the information as a text label directly on the chart.
Table: Displays the information in a table format in the top right corner of the chart.
2.2. Time Zone Settings
Time Zone: The indicator allows the user to manually set the time zone or use the exchange's time zone.
Time Zone Offset: Adjust the time zone via a UTC offset.
2.3. Day Change Detection
The indicator automatically detects the change between trading days to ensure data is correctly assigned.
3. Analysis of Price Declines and Rises
3.1. Calculation of Largest Declines and Rises
The indicator compares the high and low of each hour to determine the largest decline and rise within a trading day.
3.2. Frequency Counting
For each hour of the day, the number of times the largest declines and rises occur is counted to identify the hours with the most significant price movements.
3.3. Data Sorting
The hours are sorted by the number of occurrences of declines and rises to highlight the most frequent hours. This sorting was implemented using the MA Sorter function, inspired by Duyck's Array Sorter. Special thanks to Duyck for providing the Array Sorter on TradingView, which greatly influenced this feature
4. Interpretation and Trading Applications
4.1. Identifying High Volatility Periods
The hours identified by the indicator as having the most frequent and significant price movements are typically periods of high volatility. These periods are crucial for traders who seek to capitalize on market fluctuations.
4.2. Determining Optimal Trade Entries
Long Trades: The hours with the most significant price rises can be used to identify optimal times to enter long positions.
Short Trades: Conversely, the hours with the most significant price declines can indicate good opportunities for short trades.
4.3. Display of Top 5 Hours
The indicator shows the five hours with the most declines and rises.
Depending on the selected display option, this information is shown either as a text label or as a table in the chart.
4.4. Background Color
The background color of the chart changes at day change to clearly mark it.
5. Application of the Indicator
5.1. Trading Use
Traders can use the indicator to identify time windows with high volatility and adjust their trading strategies accordingly. This allows for more informed decisions on when to go long or short, depending on the market conditions during those hours.
5.2. Customization Options
Various input options allow the user to customize the indicator to fit personal needs and trading hours.
6. Summary
The "Decline and Rise Detective " indicator is a powerful tool for analyzing hourly price movements in the markets. By providing detailed information on the most frequent hours for significant price declines and rises, this indicator offers valuable insights into periods of high volatility. Traders can use this data to make more informed decisions on entering long or short trades. It is particularly effective when used with timeframes of 1 hour or greater.
Best regards and happy trading
Chervolino
Advanced Awesome Oscillator [CryptoSea]Advanced AO Analysis Indicator
The Advanced AO Analysis indicator is a sophisticated tool designed to evaluate the Awesome Oscillator (AO) in search of regular and hidden divergences that signal potential price reversals. By tracking the intensity and duration of the AO's movements, this indicator aids traders in pinpointing critical points in price action.
Key Features
Divergence Detection: Identifies both regular and hidden bullish and bearish divergences, providing early signs of potential market reversals.
Customizable Lookback Periods: Allows users to set specific lookback windows to define the strength and relevance of detected divergences.
Adaptive Oscillator Display: Features customizable display options for the AO, enabling users to view data in different modes suited to their analysis needs.
Alert System: Includes configurable alerts to notify users of potential divergence formations, helping traders respond promptly.
How it Works
AO Calculation: Computes the AO as the difference between short-term and long-term moving averages of the midpoints of bars, highlighting momentum shifts.
Pivot Point Analysis: Utilizes advanced algorithms to find low and high pivot points based on the oscillator values, crucial for spotting trend reversals.
Range Validation: Verifies that divergences occur within a predefined range from pivot points, ensuring their validity and strength.
Visualisation: Plots AO values and potential divergences directly on the chart, aiding in quick visual analysis.
Application
Strategic Decision-Making: Assists traders in making informed decisions by providing detailed analysis of AO movements and divergence.
Trend Confirmation: Reinforces trading strategies by confirming potential reversals with pivot point detection and divergence analysis.
Behavioural Insight: Offers insights into market dynamics and sentiment by analyzing the depth and duration of AO cycles above and below zero.
The Advanced AO Analysis indicator equips traders with a powerful analytical tool for studying the Awesome Oscillator in-depth, enhancing their ability to spot and act on divergence-based trading opportunities in the cryptocurrency markets.
Sector Rotation Hedging With Volatility Index [TradeDots]The "Sector Rotation Hedging Strategy With Volatility Index" is a comprehensive trading indicator developed to optimally leverage the S&P500 volatility index. It is designed to switch between distinct ETF sectors, strategically hedging to moderate risk exposure during harsh market volatility.
HOW DOES IT WORK
The core of this indicator is grounded on the S&P500 volatility index (VIX) close price and its 60-day moving average. This serves to determine whether the prevailing market volatility is above or below the quarterly average.
In periods of elevated market volatility, risk exposure escalates significantly. Traders retaining stocks in sectors with disproportionately high volatility face increased vulnerability to negative returns. To tackle this, our indicator employs a two-pronged approach utilizing two sequential candlestick close prices to confirm if volatility surpasses the average value.
Upon confirming above-average volatility, a hedging table is deployed to spotlight ETFs with low volatility, such as the Utilities Select Sector SPDR Fund (XLU), to derisk the overall portfolio.
Conversely, in low-volatility conditions, sectors yielding higher returns like the Technology Select Sector SPDR Fund (XLK) are preferred. The hedging table is utilized to earmark high-return sector ETFs.
Thus, during highly volatile market periods, the strategy recommends enhancing portfolio allocation to low-volatility ETFs. During low-volatility windows, the portfolio is calibrated towards high-volatility ETFs for heightened returns.
IMPORTANT CONSIDERATION
In real trading, additional considerations encompassing trading commissions, management fees, and ancillary rotation costs should be factored in. False signals may arise, potentially leading to losses from these fees.
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
itradesize /\ Silver Bullet x Macro x KillzoneThis indicator shows the best way to annotate ICT Killzones, Silver Bullet and Macro times on the chart. With the help of a new pane, it will not distract your chart and will not cause any distractions to your eye, or brain but you can see when will they happen.
The indicator also draws everything beforehand when a proper new day starts.
You can customize them how you want to show up.
Collapsed or full view?
You can hide any of them and keep only the ones you would like to.
All the colors can be customized, texts & sizes or just use shortened texts and you are also able to hide those drawings which are older than the actual day.
You should minimize the pane where the script has been automatically drawn to therefore you will have the best experience and not show any distractions.
The script automatically shows the time-based boxes, based on the New York timezone.
Killzone Time windows ( for indices ):
London KZ 02:00 - 05:00
New York AM KZ 07:00 - 10:00
New York PM KZ 13:30 - 16:00
Silver Bullet times:
03:00 - 04:00
10:00 - 11:00
14:00 - 15:00
Macro times:
02:33 - 03:00
04:03 - 04:30
08:50 - 0910
09:50 - 10:10
10:50 - 11:10
11:50 - 12:50
Advanced Technical Range and Expectancy Estimator [SS]Hello everyone,
This indicator is a from of momentum based probability modelling. It is derived from my own approaches to probability modelling but just simplified a bit.
How it works:
The indicator looks at various technical, including stochastics, RSI, MFI and Z-Score, to determine the likely sentiment. All of these, with the exception of Z-Score, are momentum based indicators and can alert us to likely sentiment. However, instead of us making the subjective determination ourselves as to whether the RSI or MFI or Stochastics are bullish, the indicator will look at previous instances of these occurrences, and tally the bullish and bearish follow throughs that happened. It will also calculate the average target price that was hit, under similar conditions, on the same timeframe.
The Z-Score is your "tie breaker". It is not a momentum based indicator and measures something a little different (the standard deviation and over-extension of the stock). For this reason, it provides an alternative assessment and tends to be a bit more reliable in times of low momentum.
Back-test Results:
The indicator back-tests itself over the previous 100 candles. I have limited it to 100 candles for pragmatic considerations (it has to back-test each technical individually and increasing the BT length will slow and potentially error out the indicator) as well as accuracy considerations.
One thing I have noticed in my years of trying to crack the code and develop probability models for tickers, is historical accuracy doesn't always matter because sentiment is always changing. You need to see what it has done over the most recent 100 to 200 candles.
There are two back-test windows, one for the price targets and the other for the sentiment accuracy. The most effective/most accurate will highlight green, the least effective/least accurate will highlight red:
In the image above, you can see that the most accurate predictor of sentiment is Z-Score, with a 90.32% accuracy rate over the past 100 candles.
The most accurate predictor of price is MFI, with a 60% (for bull targets) and 42% (for bear targets)accuracy rate.
Anchoring Points:
The indicator permits you to anchor by two points. The default setting is anchoring by previous candle. If you plan to use this as an oscillator, to see the current prediction for the current candle you are viewing, then you will need to leave this default setting. It will pull the data from the previous candle and give you the data for the current candle you are on.
If you are assess the likely sentiment for the next day after the day has closed off, you will want to anchor by current candle. This will take the current technicals that the day has closed off with and run the assessment for you.
Customizability
You can customize the technicals by source and length of assessment.
They are all defaulted to the traditional settings of these indicators, but if you want to customize your model to try and improve or enhance accuracy in one way or another, you are free and able to do so!
I do suggest leaving the defaults as they seem to work particular well :-).
Thresholds
Thresholds are the tolerance levels that we permit for our technical search range. If you want them to be exactly identical, then you can set it to 0. If you want it to be extremely similar, you can set it to 0.01. This will hone in on the ranges you are interest in and you can see how it affects your accuracy by reviewing the results in the back-test tables.
Keep Static Colour Option
I want to make a quick note on the "Keep Static Colour" option that is in your settings menu.
The primary table that shows you the probability and price targets change colours based on the accuracy of the assessment. This is so, if you are using a mobile device or smaller screen and can't have the back-test results open at the same time, you can see still which are the most reliable results. However, if you have the back-test tables open and you find these colour changes too distracted, you can toggle on the "Keep Static Colour" and it will resort the colour of the table to a solid white:
Show Technicals
The indicator can show you the current technical values if you are using it in place of an oscillator. Its less pivotal as its making the assessment for you, but just for your reference if you want to see what the current MFI, Z-Score or Stochastics etc. are, you have that option as well.
All Timeframes Permitted
You can view Weekly, Monthly, Hourly, 5 minute, 1 minute, its all supported!
That's the indicator in a nutshell.
Hope you enjoy and leave your questions below.
Safe trades everyone!
ICT Times [joshu]This TradingView indicator provides a comprehensive view of ICT killzones, Silver Bullet times, and ICT Macros, enhancing your trading experience.
In those time windows price either seeks liquidity or imbalances and you often find the most energetic price moves and turning points.
Features:
Automatic Adaptation: The ICT killzones intelligently adapt to the specific chart you are using. For Forex charts, it follows the ICT Forex times:
Asia: 2000-0000
London: 0200-0500
New York: 0700-1000
London Close: 1000-1200
For other charts, it uses the following session times:
Asia: 2000-0000
London: 0200-0500
New York AM: 0830-1100
New York PM: 1330-1600
Silver Bullet Times:
0300-0400
1000-1100
1400-1500
How to Use:
Simply apply the indicator to your chart, and the session boxes and Silver Bullet times will be plotted automatically.
Monthly beta (5Y monthly) with multi-timeframe supportThe PROPER way to calculate beta for a stock using monthly price returns . None of this nonsense using daily returns and sliding windows as done by other scripts...
Works on any timeframe.
This script has been checked against 100s of stocks on Yahoo finance and Zacks research data and matches 100% (some rounding error as this script is kept updated live on unconfirmed monthly bars).
You can check for yourself:
Zacks fundamentals - beta
The script calculates beta using the Variance-Covariance Method as described on Investopedia
How to calculate Beta
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!
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.
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.
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), "#.##")`
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.
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)
Ehlers Linear Extrapolation Predictor [Loxx]Ehlers Linear Extrapolation Predictor is a new indicator by John Ehlers. The translation of this indicator into PineScript™ is a collaborative effort between @cheatcountry and I.
The following is an excerpt from "PREDICTION" , by John Ehlers
Niels Bohr said “Prediction is very difficult, especially if it’s about the future.”. Actually, prediction is pretty easy in the context of technical analysis. All you have to do is to assume the market will behave in the immediate future just as it has behaved in the immediate past. In this article we will explore several different techniques that put the philosophy into practice.
LINEAR EXTRAPOLATION
Linear extrapolation takes the philosophical approach quite literally. Linear extrapolation simply takes the difference of the last two bars and adds that difference to the value of the last bar to form the prediction for the next bar. The prediction is extended further into the future by taking the last predicted value as real data and repeating the process of adding the most recent difference to it. The process can be repeated over and over to extend the prediction even further.
Linear extrapolation is an FIR filter, meaning it depends only on the data input rather than on a previously computed value. Since the output of an FIR filter depends only on delayed input data, the resulting lag is somewhat like the delay of water coming out the end of a hose after it supplied at the input. Linear extrapolation has a negative group delay at the longer cycle periods of the spectrum, which means water comes out the end of the hose before it is applied at the input. Of course the analogy breaks down, but it is fun to think of it that way. As shown in Figure 1, the actual group delay varies across the spectrum. For frequency components less than .167 (i.e. a period of 6 bars) the group delay is negative, meaning the filter is predictive. However, the filter has a positive group delay for cycle components whose periods are shorter than 6 bars.
Figure 1
Here’s the practical ramification of the group delay: Suppose we are projecting the prediction 5 bars into the future. This is fine as long as the market is continued to trend up in the same direction. But, when we get a reversal, the prediction continues upward for 5 bars after the reversal. That is, the prediction fails just when you need it the most. An interesting phenomenon is that, regardless of how far the extrapolation extends into the future, the prediction will always cross the signal at the same spot along the time axis. The result is that the prediction will have an overshoot. The amplitude of the overshoot is a function of how far the extrapolation has been carried into the future.
But the overshoot gives us an opportunity to make a useful prediction at the cyclic turning point of band limited signals (i.e. oscillators having a zero mean). If we reduce the overshoot by reducing the gain of the prediction, we then also move the crossing of the prediction and the original signal into the future. Since the group delay varies across the spectrum, the effect will be less effective for the shorter cycles in the data. Nonetheless, the technique is effective for both discretionary trading and automated trading in the majority of cases.
EXPLORING THE CODE
Before we predict, we need to create a band limited indicator from which to make the prediction. I have selected a “roofing filter” consisting of a High Pass Filter followed by a Low Pass Filter. The tunable parameter of the High Pass Filter is HPPeriod. Think of it as a “stone wall filter” where cycle period components longer than HPPeriod are completely rejected and cycle period components shorter than HPPeriod are passed without attenuation. If HPPeriod is set to be a large number (e.g. 250) the indicator will tend to look more like a trending indicator. If HPPeriod is set to be a smaller number (e.g. 20) the indicator will look more like a cycling indicator. The Low Pass Filter is a Hann Windowed FIR filter whose tunable parameter is LPPeriod. Think of it as a “stone wall filter” where cycle period components shorter than LPPeriod are completely rejected and cycle period components longer than LPPeriod are passed without attenuation. The purpose of the Low Pass filter is to smooth the signal. Thus, the combination of these two filters forms a “roofing filter”, named Filt, that passes spectrum components between LPPeriod and HPPeriod.
Since working into the future is not allowed in EasyLanguage variables, we need to convert the Filt variable to the data array XX . The data array is first filled with real data out to “Length”. I selected Length = 10 simply to have a convenient starting point for the prediction. The next block of code is the prediction into the future. It is easiest to understand if we consider the case where count = 0. Then, in English, the next value of the data array is equal to the current value of the data array plus the difference between the current value and the previous value. That makes the prediction one bar into the future. The process is repeated for each value of count until predictions up to 10 bars in the future are contained in the data array. Next, the selected prediction is converted from the data array to the variable “Prediction”. Filt is plotted in Red and Prediction is plotted in yellow.
The Predict Extrapolation indicator is shown above for the Emini S&P Futures contract using the default input parameters. Filt is plotted in red and Predict is plotted in yellow. The crossings of the Predict and Filt lines provide reliable buy and sell timing signals. There is some overshoot for the shorter cycle periods, for example in February and March 2021, but the only effect is a late timing signal. Further reducing the gain and/or reducing the BarsFwd inputs would provide better timing signals during this period.
ADDITIONS
Loxx's Expanded source types:
Library for expanded source types:
Explanation for expanded source types:
Three different signal types: 1) Prediction/Filter crosses; 2) Prediction middle crosses; and, 3) Filter middle crosses.
Bar coloring to color trend.
Signals, both Long and Short.
Alerts, both Long and Short.
SubCandleI created this script as POC to handle specific cases where not having tick data on historical bars create repainting. Happy to share if this serves purpose for other coders.
What is the function of this script?
Script plots a sub-candle which is remainder of candle after forming the latest peak.
Higher body of Sub-candle refers to strong retracement of price from its latest peak. Color of the sub-candle defines the direction of retracement.
Higher wick of Sub-candle refers to higher push in the direction of original candle. Meaning, after price reaching its peak, price retraced but could not hold.
Here is a screenshot with explanation to visualise the concept:
Settings
There is only one setting which is number of backtest bars. Lower timeframe resolution which is used for calculating the Sub-candle uses this number to automatically calculate maximum possible lower timeframe so that all the required backtest windows are covered without having any issue.
We need to keep in mind that max available lower timeframe bars is 100,000. Hence, with 5000 backtest bars, lower timeframe resolution can be about 20 (100000/5000) times lesser than that of regular chart timeframe. We need to also keep in mind that minimum resolution available as part of security_lower_tf is 1 minute. Hence, it is not advisable to use this script for chart timeframes less than 15 mins.
Application
I have been facing this issue in pattern recognition scripts where patterns are formed using high/low prices but entry and targets are calculated based on the opposite side (low/high). It becomes tricky during extreme bars to identify entry conditions based on just the opposite peak because, the candle might have originated from it before identifying the pattern and might have never reached same peak after forming the pattern. Due to lack of tick data on historical bars, we cannot use close price to measure such conditions. This leads to repaint and few unexpected results. I am intending to use this method to overcome the issue up-to some extent.
Growth Stock Arbitrage Indicator [@PierceARK]This indicator takes advantage of the fact that when the 10 and 5 year Treasury Constant Maturity Minus Federal Funds rates (T10YFF/T5YFF) go down sharply, investors tend to rotate into stocks. This arbitrage works great for growth stocks, since growth stocks are higher beta by virtue of their lower market cap and more speculative nature in general. This script identifies the moving-average convergence/divergence of the average of the 10y and 5y treasury rates and then finds the variance of that macd line. By averaging that variance with the macdline's inverse, an analog output of treasury -> stock rotation can be identified. The upper and lower thresholds bring buy and sell windows into focus.
STD-Stepped Fast Cosine Transform Moving Average [Loxx]STD-Stepped Fast Cosine Transform Moving Average is an experimental moving average that uses Fast Cosine Transform to calculate a moving average. This indicator has standard deviation stepping in order to smooth the trend by weeding out low volatility movements.
What is the Discrete Cosine Transform?
A discrete cosine transform (DCT) expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. The DCT, first proposed by Nasir Ahmed in 1972, is a widely used transformation technique in signal processing and data compression. It is used in most digital media, including digital images (such as JPEG and HEIF, where small high-frequency components can be discarded), digital video (such as MPEG and H.26x), digital audio (such as Dolby Digital, MP3 and AAC), digital television (such as SDTV, HDTV and VOD), digital radio (such as AAC+ and DAB+), and speech coding (such as AAC-LD, Siren and Opus). DCTs are also important to numerous other applications in science and engineering, such as digital signal processing, telecommunication devices, reducing network bandwidth usage, and spectral methods for the numerical solution of partial differential equations.
The use of cosine rather than sine functions is critical for compression, since it turns out (as described below) that fewer cosine functions are needed to approximate a typical signal, whereas for differential equations the cosines express a particular choice of boundary conditions. In particular, a DCT is a Fourier-related transform similar to the discrete Fourier transform (DFT), but using only real numbers. The DCTs are generally related to Fourier Series coefficients of a periodically and symmetrically extended sequence whereas DFTs are related to Fourier Series coefficients of only periodically extended sequences. DCTs are equivalent to DFTs of roughly twice the length, operating on real data with even symmetry (since the Fourier transform of a real and even function is real and even), whereas in some variants the input and/or output data are shifted by half a sample. There are eight standard DCT variants, of which four are common.
The most common variant of discrete cosine transform is the type-II DCT, which is often called simply "the DCT". This was the original DCT as first proposed by Ahmed. Its inverse, the type-III DCT, is correspondingly often called simply "the inverse DCT" or "the IDCT". Two related transforms are the discrete sine transform (DST), which is equivalent to a DFT of real and odd functions, and the modified discrete cosine transform (MDCT), which is based on a DCT of overlapping data. Multidimensional DCTs (MD DCTs) are developed to extend the concept of DCT to MD signals. There are several algorithms to compute MD DCT. A variety of fast algorithms have been developed to reduce the computational complexity of implementing DCT. One of these is the integer DCT (IntDCT), an integer approximation of the standard DCT, : ix, xiii, 1, 141–304 used in several ISO/IEC and ITU-T international standards.
Notable settings
windowper = period for calculation, restricted to powers of 2: "16", "32", "64", "128", "256", "512", "1024", "2048", this reason for this is FFT is an algorithm that computes DFT (Discrete Fourier Transform) in a fast way, generally in 𝑂(𝑁⋅log2(𝑁)) instead of 𝑂(𝑁2). To achieve this the input matrix has to be a power of 2 but many FFT algorithm can handle any size of input since the matrix can be zero-padded. For our purposes here, we stick to powers of 2 to keep this fast and neat. read more about this here: Cooley–Tukey FFT algorithm
smthper = smoothing count, this smoothing happens after the first FCT regular pass. this zeros out frequencies from the previously calculated values above SS count. the lower this number, the smoother the output, it works opposite from other smoothing periods
Included
Alerts
Signals
Loxx's Expanded Source Types
Additional reading
A Fast Computational Algorithm for the Discrete Cosine Transform by Chen et al.
Practical Fast 1-D DCT Algorithms With 11 Multiplications by Loeffler et al.
Cooley–Tukey FFT algorithm
Helme-Nikias Weighted Burg AR-SE Extra. of Price [Loxx]Helme-Nikias Weighted Burg AR-SE Extra. of Price is an indicator that uses an autoregressive spectral estimation called the Weighted Burg Algorithm, but unlike the usual WB algo, this one uses Helme-Nikias weighting. This method is commonly used in speech modeling and speech prediction engines. This is a linear method of forecasting data. You'll notice that this method uses a different weighting calculation vs Weighted Burg method. This new weighting is the following:
w = math.pow(array.get(x, i - 1), 2), the squared lag of the source parameter
and
w += math.pow(array.get(x, i), 2), the sum of the squared source parameter
This take place of the rectangular, hamming and parabolic weighting used in the Weighted Burg method
Also, this method includes Levinson–Durbin algorithm. as was already discussed previously in the following indicator:
Levinson-Durbin Autocorrelation Extrapolation of Price
What is Helme-Nikias Weighted Burg Autoregressive Spectral Estimate Extrapolation of price?
In this paper a new stable modification of the weighted Burg technique for autoregressive (AR) spectral estimation is introduced based on data-adaptive weights that are proportional to the common power of the forward and backward AR process realizations. It is shown that AR spectra of short length sinusoidal signals generated by the new approach do not exhibit phase dependence or line-splitting. Further, it is demonstrated that improvements in resolution may be so obtained relative to other weighted Burg algorithms. The method suggested here is shown to resolve two closely-spaced peaks of dynamic range 24 dB whereas the modified Burg schemes employing rectangular, Hamming or "optimum" parabolic windows fail.
Data inputs
Source Settings: -Loxx's Expanded Source Types. You typically use "open" since open has already closed on the current active bar
LastBar - bar where to start the prediction
PastBars - how many bars back to model
LPOrder - order of linear prediction model; 0 to 1
FutBars - how many bars you want to forward predict
Things to know
Normally, a simple moving average is calculated on source data. I've expanded this to 38 different averaging methods using Loxx's Moving Avreages.
This indicator repaints
Further reading
A high-resolution modified Burg algorithm for spectral estimation
Related Indicators
Levinson-Durbin Autocorrelation Extrapolation of Price
Weighted Burg AR Spectral Estimate Extrapolation of Price
[blackcat] L1 Vitali Apirine HHs & LLs StochasticsLevel 1
Background
This indicator was originally formulated by Vitali Apirine for TASC - February 2016 Traders Tips.
Function
According to Vitali Apirine, his momentum indicator–based system HHLLS (higher high lower low stochastic) can help to spot emerging trends, define correction periods, and anticipate reversals. As with many indicators, HHLLS signals can also be generated by looking for divergences and crossovers. Because the HHLLS is an oscillator, it can also be used to identify overbought & oversold levels.
Remarks
I changed EMA or SMA into hanning windowing function to reduce lag issue.
colorful area is bearish power.
colorful solid thick line is bull power.
Feedbacks are appreciated.
