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Developing Market Profile / TPO [Honestcowboy]The Developing Market Profile Indicator aims to broaden the horizon of Market Profile / TPO research and trading. While standard Market Profiles aim is to show where PRICE is in relation to TIME on a previous session (usually a day). Developing Market Profile will change bar by bar and display PRICE in relation to TIME for a user specified number of past bars. What is a market profile? "Market Profile is an intra-day charting technique (price vertical, time/activity horizontal) devised by J. Peter Steidlmayer. Steidlmayer was seeking a way to determine and to evaluate market value as it developed in the day time frame. The concept was to display price on a vertical axis against time on the horizontal, and the ensuing graphic generally is a bell shape--fatter at the middle prices, with activity trailing off and volume diminished at the extreme higher and lower prices." For education on market profiles I recommend you search the net and study some profitable traders who use it. Key Differences Does not have a value area but distinguishes each column in relation to the biggest column in percentage terms. Updates bar by bar Does not take sessions into account Shows historical values for each bar While there is an entire education system build around Market Profiles they usually focus on a daily profile and in some cases how the value area develops during the day (there are indicators showing the developing value area). The idea of trading based on a developing value area is what inspired me to build the Developing Market Profile. 🟦 CALCULATION Think of this Developing Market Profile the same way as you would think of a moving average. On each bar it will lookback 200 bars (or as user specified) and calculate a Market Profile from those bars (range). 🔹Market Profile gets calculated using these steps: Get the highest high and lowest low of the price range. Separate that range into user specified amount of price zones (all spaced evenly) Loop through the ranges bars and on each bar check in which price zones price was, then add +1 to the zones price was in (we do this using the OccurenceArray) After it looped through all bars in the range it will draw columns for each price zone (using boxes) and make them as wide as the OccurenceArray dictates in number of bars 🔹Coloring each column: The script will find the biggest column in the Profile and use that as a reference for all other columns. It will then decide for each column individually how big it is in % compared to the biggest column. It will use that percentage to decide which color to give it, top 20% will be red, top 40% purple, top 60% blue, top 80% green and all the rest yellow. The user is able to adjust these numbers for further customisation. The historical display of the profiles uses plotchar() and will not only use the color of the column at that time but the % rating will also decide transparancy for further detail when analysing how the profiles developed over time. Each of those historical profiles is calculated using its own 200 past bars. This makes the script very heavy and that is why it includes optimisation settings, more info below. 🟦 USAGE My general idea of the markets is that they are ever changing and that in studying that changing behaviour a good trader is able to distinguish new behaviour from old behaviour and adapt his approach before losing traders "weak hands" do. A Market Profile can visually show a trader what kind of market environment we currently are in. In training this visual feedback helps traders remember past market environments and how the market behaved during these times. Use the history shown using plotchars in colors to get an idea of how the Market Profile looked at each bar of the chart. This history will help in studying how price moves at different stages of the Market Profile development. I'm in no way an expert in trading Market Profiles so take this information with a grain of salt. Below an idea of how I would trade using this indicator: 🟦 SETTINGS 🔹MARKET PROFILING Lookback: The amount of bars the Market Profile will look in the past to calculate where price has been the most in that range Resolution: This is the amount of columns the Market Profile will have. These columns are calculated using the highest and lowest point price has been for the lookback period Resolution is limited to a maximum of 32 because of pinescript plotting limits (64). Each plotchar() because of using variable colors takes up 2 of these slots 🔹VISUAL SETTINGS Profile Distance From Chart: The amount of bars the market profile will be offset from the current bar Border width (MP): The line thickness of the Market Profile column borders Character: This is the character the history will use to show past profiles, default is a square. Color theme: You can pick 5 colors from biggest column of the Profile to smallest column of the profile. Numbers: these are for % to decide column color. So on default top 20% will be red, top 40% purple... Always use these in descending order Show Market Profile: This setting will enable/disable the current Market Profile (columns on right side of current bar) Show Profile History: This setting will enable/disable the Profile History which are the colored characters you see on each bar 🔹OPTIMISATION AND DEBUGGING Calculate from here: The Market Profile will only start to calculate bar by bar from this point. Setting is needed to optimise loading time and quite frankly without it the script would probably exceed tradingview loading time limits. Min Size: This setting is there to avoid visual bugs in the script. Scaling the chart there can be issues where the Market Profile extends all the way to 0. To avoid this use a minimum size bigger than the bugged bottom box
Pine Script® indicator
by Honestcowboy
20
Cycles AnalysisI strongly believe in cycles, so I wanted to create something that would give a visual representation of bull/bear markets and give a prediction based on the previous data. It's up to you how to decide what is a bull/bear cycle. There is no single rule for all assets because 20% drop in SP500 starts a bear market in traditional markets, while 35% drop for Bitcoin is a Tuesday. You have two options on how to decide when markets turn: either by a % change (traditional definition) or if there is no new high/low after X days. A softer version to show periods of no new highs/lows is to use the Stagnation option. Stagnation periods hava the same logic as the cycle change by X days: if there is no new high/low then we treat this period as a stagnation. The difference is that stagnation periods do not change cycle directions and do not participate in calculations. The script also draws a possible "predictions" zone where the current cycle might end up. There is no magic here, it just takes previous cycles' size to draw the possible boundaries. If you decide to use percentiles then the box area will be taken from the percentiles calculations, otherwise it will come from the full data. "x" in the predictions zone represents a target mean (average) value, "o" represents a target median value. A few things to keep in mind: - this script is not supposed to be used in trading. It was created for analysis. It repaints. And when I say "it repaints" - it might like repaint the last 6 months of data if a new low comes and we are in a stagnation period (aka not a financial advice). - it doesn't work with replays as it does calculations only once on the last candle. - you need at least 3 periods to be able to calculate percentiles. And after this it will remove at least 1 period on each side. Which means that 90 percentile will not be a real 90 percentile until you have enough periods for it to be (20 in this specific case). - it assumes that a year = 360 days, and a month = 30 days. So the duration presentation might not be exact, until you move to the day level. - I had macro analysis in mind when I created the script, but nothing stops you from using it in a 1m time frame for BTC. Just change the time duration presentation. - the last period is not finished, so it doesn't participate in calculations.
Pine Script® indicator
by Intetics
26
Liquidity Sentiment Profile [LuxAlgo]The Liquidity Sentiment Profile is an advanced charting tool that measures by combining PRICE and VOLUME data over specified anchored periods and highlights within a sequence of profiles the distribution of the liquidity and the market sentiment at specific price levels. The Liquidity Sentiment Profile allows traders to reveal significant price levels, dominant market sentiment, support and resistance levels, supply and demand zones, liquidity availability levels, liquidity gaps, consolidation zones, and more based on price and volume data. Liquidity refers to the availability of orders at specific price levels in the market, allowing transactions to occur smoothly. 🔶 USAGE A Liquidity Sentiment Profile is a combination of a liquidity and a sentiment profile, where the right part of the profile displays the distribution of the traded activity at different price levels and the left part displays the market sentiment at those price levels. The Liquidity Sentiment Profiles are visualized with different colors, where each color has a different meaning. The Liquidity Sentiment Profiles aim to present Value Areas based on the significance of price levels, thus allowing users to identify value areas that can be formed more than once within the range of a single profile. Level of Significance Line - displays the changes in the price levels with the highest traded activity (developing POC) 🔶 SETTINGS The script takes into account user-defined parameters and plots the profiles, where detailed usage for each user-defined input parameter in indicator settings is provided with the related input's tooltip. 🔹 Liquidity Sentiment Profiles Anchor Period: The indicator resolution is set by the input of the Anchor Period, the default option is AUTO. 🔹 Liquidity Profile Settings Liquidity Profile: Toggles the visibility of the Liquidity Profiles High Traded Nodes: Threshold and Color option for High Traded Nodes Average Traded Nodes: Color option for Average Traded Nodes Low Traded Nodes: Threshold and Color option for Low Traded Nodes 🔹 Sentiment Profile Settings Sentiment Profile: Toggles the visibility of the Sentiment Profiles Bullish Nodes: Color option for Bullish Nodes Bearish Nodes: Color option for Bearish Nodes 🔹 Other Settings Level of Significance: Toggles the visibility of the Level of Significance Line Profile Price Levels: Toggles the visibility of the Profile Price Levels Number of Rows: Specify how many rows each profile histogram will have. Caution, having it set to high values will quickly hit Pine Script™ drawing objects limit and fewer historical profiles will be displayed Profile Width %: Alters the width of the rows in the histogram, relative to the profile length Profile Range Background Fill: Toggles the visibility of the Profiles Range 🔶 LIMITATIONS The amount of drawing objects that can be used is limited, as such using a high number of rows can display fewer historical profiles and occasionally incomplete profiles. 🔶 RELATED SCRIPTS 🔹 Buyside-Sellside-Liquidity 🔹 ICT-Concepts 🔹 Swing-Volume-Profiles
Pine Script® indicator
by LuxAlgo
27
Trend Correlation HeatmapHello everyone! I am excited to release my trend correlation heatmap, or trend heatmap for short. Per usual, I think its important to explain the theory before we get into the use of the indicator, so let's get into the theory! The theory: So what is a correlation? Correlation is the relationship one variable has to another. Correlations are the basis of everything I do as a quantitative trader. From the correlation between the same variables (i.e. autocorrelation), the correlation between other variables (i.e. VIX and SPY, SPY High and SPY Low, DXY and ES1! close, etc.) and, as well, the correlation between price and time (time series correlation). This may sound very familiar to you, especially if you are a user, observer or follower of my ideas and/or indicators. Ninety-five percent of my indicators are a function of one of those three things. Whether it be a time series based indicator (i.e.my time series indicator), whether it be autocorrelation (my autoregressive cloud indicator or my autocorrelation oscillator) or whether it be regressive in nature (i.e. my SPY Volume weighted close, or even my expected move which uses averages in lieu of regressive approaches but is foundational in regression principles. Or even my VIX oscillator which relies on the premise of correlations between tickers.) So correlation is extremely important to me and while its true I am more of a regression trader than anything, I would argue that I am more of a correlation trader, because correlations are the backbone of how I develop math models of stocks. What I am trying to stress here is the importance of correlations. They really truly are foundational to any type of quantitative analysis for stocks. And as such, understanding the current relationship a stock has to time is pivotal for any meaningful analysis to be conducted. So what is correlation to time and what does it tell us? Correlation to time, otherwise known and commonly referred to as "Time Series", is the relationship a ticker's price has to the passing of time. It is displayed in the traditional Pearson Correlation Coefficient or R value and can be any value from -1 (strong negative relationship, i.e. a strong downtrend) to + 1 (i.e. a strong positive relationship, i.e. a strong uptrend). The higher or lower the value the stronger the up or downtrend is. As such, correlation to time tells us two very important things. These are: a) The direction of the stock; and b) The strength of the trend. Let's take a look at an example: Above we have a chart of QQQ. We can see a trendline that seems to fit well. The questions we ask as traders are: 1. What is the likelihood QQQ breaks down from this trendline? 2. What is the likelihood QQQ continues up? 3. What is the likelihood QQQ does a false breakdown? There are numerous mathematical approaches we can take to answer these questions. For example, 1 and 2 can be answered by use of a Cumulative Distribution Density analysis (CDDA) or even a linear or loglinear regression analysis and 3 can be answered, more or less, with a linear regression analysis and standard error ascertainment, or even just a general comparison using a data science approach (such as cosine similarity or Manhattan distance). But, the reality is, all 3 of these questions can be visualized, at least in some way, by simply looking at the correlation to time. Let's look at this chart again, this time with the correlation heatmap applied: If we look at the indicator we can see some pivotal things. These are: 1. We have 4, very strong uptrends that span both higher AND lower timeframes. We have a strong uptrend of 0.96 on the 5 minute, 50 candle period. We have a strong uptrend at the 300 candle lookback period on the 1 minute, we have a strong uptrend on the 100 day lookback on the daily timeframe period and we have a strong uptrend on the 5 minute on the 500 candle lookback period. 2. By comparison, we have 3 downtrends, all of which have correlations less than the 4 uptrends. All of the downtrends have a correlation above -0.8 (which we would want lower than -0.8 to be very strong), and all of the uptrends are greater than + 0.80. 3. We can also see that the uptrends are not confined to the smaller timeframes. We have multiple uptrends on multiple timeframes and both short term (50 to 100 candles) and long term (up to 500 candles). 4. The overall trend is strengthening to the upside manifested by a positive Max Change and a Positive Min change (to be discussed later more in-depth). With this, we can see that QQQ is actually very strong and likely will continue at least some upside. If we let this play out: We continued up, had one test and then bounced. Now, I want to specify, this indicator is not a panacea for all trading. And in relation to the 3 questions posed, they are best answered, at least quantitatively, not only by correlation but also by the aforementioned methods (CDDA, etc.) but correlation will help you get a feel for the strength or weakness present with a stock. What are some tangible applications of the indicator? For me, this indicator is used in many ways. Let me outline some ways I generally apply this indicator in my day and swing trading: 1. Gauging the strength of the stock: The indictor tells you the most prevalent behavior of the stock. Are there more downtrends than uptrends present? Are the downtrends present on the larger timeframes vs uptrends on the shorter indicating a possible bullish reversal? or vice versa? Are the trends strengthening or weakening? All of these things can be visualized with the indicator. 2. Setting parameters for other indicators: If you trade EMAs or SMAs, you may have a "one size fits all" approach. However, its actually better to adjust your EMA or SMA length to the actual trend itself. Take a look at this: This is QQQ on the 1 hour with the 200 EMA with 200 standard deviation bands added. If we look at the heatmap, we can see, yes indeed 200 has a fairly strong uptrend correlation of 0.70. But the strongest hourly uptrend is actually at 400 candles, with a correlation of 0.91. So what happens if we change the EMA length and standard deviation to 400? This: The exact areas are circled and colour coded. You can see, the 400 offers more of a better reference point of supports and resistances as well as a better overall trend fit. And this is why I never advocate for getting married to a specific EMA. If you are an EMA 200 lover or 21 or 51, know that these are not always the best depending on the trend and situation. Components of the indicator: Ah okay, now for the boring stuff. Let's go over the functionality of the indicator. I tried to keep it simple, so it is pretty straight forward. If we open the menu here are our options: We have the ability to toggle whichever timeframes we want. We also have the ability to toggle on or off the legend that displays the colour codes and the Max and Min highest change. Max and Min highest change: The max and min highest change simply display the change in correlation over the previous 14 candles. An increasing Max change means that the Max trend is strengthening. If we see an increasing Max change and an increasing Min change (the Min correlation is moving up), this means the stock is bullish. Why? Because the min (i.e. ideally a big negative number) is going up closer to the positives. Therefore, the downtrend is weakening. If we see both the Max and Min declining (red), that means the uptrend is weakening and downtrend is strengthening. Here are some examples: Final Thoughts: And that is the indicator and the theory behind the indicator. In a nutshell, to summarize, the indicator simply tracks the correlation of a ticker to time on multiple timeframes. This will allow you to make judgements about strength, sentiment and also help you adjust which tools and timeframes you are using to perform your analyses. As well, to make the indicator more user friendly, I tried to make the colours distinctively different. I was going to do different shades but it was a little difficult to visualize. As such, I have included a toggle-able legend with a breakdown of the colour codes! That's it my friends, I hope you find it useful! Safe trades and leave your questions, comments and feedback below!
Pine Script® indicator
by Steversteves
18
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!
Pine Script® indicator
by tradeforopp
312
120x ticker screener (composite tickers)In specific circumstances, it is possible to extract data, far above the 40 `request.*()` call limit for 1 single script . The following technique uses composite tickers . Changing tickers needs to be done in the code itself as will be explained further.           ⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯ 🔶 PRINCIPLE         Standard example:   c1 = request.security('MTLUSDT' , 'D', close)         This will give the close value from 1 ticker (MTLUSDT); c1 for example is 1.153         Now let's add 2 tickers to MTLUSDT; XMRUSDT and ORNUSDT with, for example, values of 1.153 (I), 143.4 (II) and 0.8242 (III) respectively.         Just adding them up 'MTLUSDT+XMRUSDT+ORNUSDT' would give 145.3772 as a result, which is not something we can use...         Let's multiply ORNUSDT by                  100 ->            14340         and   multiply MTLUSDT  by 1000000000 -> 1153000000                 (from now, 10e8 will be used instead of 1000000000)         Then we make the sum.         When we put this in a security call (just the close value) we get:   c1 = request.security('MTLUSDT*10e8+XMRUSDT*100+ORNUSDT', 'D', close)         'MTLUSDT*10e8+XMRUSDT*100+ORNUSDT' -> 1153000000 + 14340 + 0.8242 = 1153014340.8242 (a)         This (a) will be split later on, for example:         1153014330.8242 / 10e8 = 1.1530143408242 -> round -> in this case to 1.153 (I), multiply again by 10e8 -> 1153000000.00 (b)         We subtract this from the initial number:             1153014340.8242   (a)          - 1153000000.0000   (b)           –––––––––––––––––                        14340.8242   (c)         Then -> 14340.8242 / 100 = 143.408242 -> round -> 143.4 (II) -> multiply -> 14340.0000 (d)          -> subtract             14340.8242   (c)          - 14340.0000   (d)           ––––––––––––                      0.8242   (III)         Now we have split the number again into 3 tickers: 1.153 (I), 143.4 (II) and 0.8242 (III)           ⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯⎯ In this publication the function compose_3_() will make a composite ticker of 3 tickers, and the split_3_() function will split these 3 tickers again after passing 1 request.security() call. In this example: t46 = 'BINANCE:MTLUSDT', n46 = 10e8 , r46 = 3, t47 = 'BINANCE:XMRUSDT', n47 = 10e1, r47 = 1, t48 = 'BINANCE:ORNUSDT', r48 = 4 // T16 ••• T16= compose_3_(t48, t47, n47, t46, n46) ••• = request.security(T16, res, ) ••• = split_3_(c16, n46, r46, n47, r47, r48) 🔶 CHANGING TICKERS If you need to change tickers, you only have to change the first part of the script, USER DEFINED TICKERS Back to our example, at line 26 in the code, you'll find: t46 = 'BINANCE:MTLUSDT', n46 = 10e8 , r46 = 3, t47 = 'BINANCE:XMRUSDT', n47 = 10e1, r47 = 1, t48 = 'BINANCE:ORNUSDT', r48 = 4 // T16 ( t46 , T16 ,... will be explained later) You need to figure out how much you need to multiply each ticker, and the number for rounding, to get a good result. In this case: 'BINANCE:MTLUSDT', multiply number = 10e8, round number is 3 (example value 1.153) 'BINANCE:XMRUSDT', multiply number = 10e1, round number is 1 (example value 143.4) 'BINANCE:ORNUSDT', NO multiply number, round number is 4 (example value 0.8242) The value with most digits after the decimal point by preference is placed to the right side (ORNUSDT) If you want to change these 3, how would you do so? First pick your tickers and look for the round values, for example: 'MATICUSDT', example value =   0.5876 -> round -> 4 'LTCUSDT'    , example value = 77.47      -> round -> 2 'ARBUSDT'    , example value =   1.0231 -> round -> 4 Value with most digits after the decimal point -> MATIC or ARB, let's pick ARB to go on the right side, LTC at the left of ARB, and MATIC at the most left side. -> 'MATICUSDT', LTCUSDT', ARBUSDT' Then check with how much 'LTCUSDT' and 'MATICUSDT' needs to be multiplied to get this: 5876 0 7747 0 1.0231 'MATICUSDT' -> 10e10 'LTCUSDT'      -> 10e3 Replace: t46 = 'BINANCE:MTLUSDT', n46 = 10e8 , r46 = 3, t47 = 'BINANCE:XMRUSDT', n47 = 10e1, r47 = 1, t48 = 'BINANCE:ORNUSDT', r48 = 4 // T16 -> t46 = 'BINANCE:MATICUSDT', n46 = 10e10 , r46 = 4, t47 = 'BINANCE:LTCUSDT', n47 = 10e3, r47 = 2, t48 = 'BINANCE:ARBUSDT', r48 = 4 // T16 DO NOT change anything at t46, n46,... if you don't know what you're doing! Only  • tickers ('BINANCE:MTLUSDT', 'BINANCE:XMRUSDT', 'BINANCE:ORNUSDT', ...),  • multiply numbers (10e8, 10e1, ...) and  • round numbers (3, 1, 4, ...) should be changed. There you go! 🔶 LIMITATIONS 🔹 The composite ticker fails when 1 of the 3 isn't in market in the weekend, while the other 2 are.      That is the reason all tickers are crypto. I think it is possible to combine stock,... tickers, but they have to share the same market hours. 🔹 The number cannot be as large as you want, the limit lays around 15-16 digits.      This means when you have for example 123, 45.67 and 0.000000000089, you'll get issues when composing to this:      -> 123045670.000000000089 (21 digits)      Make sure the numbers are close to each other as possible, with 1 zero (or 2) in between:      -> 1.230045670089 (13 digits by doing -> (123 * 10e-3) + (45.67 * 10e-7) + 0.000000000089) 🔹 This script contains examples of calculated values, % change, SMA, RMA and RSI.      These values need to be calculated from HTF close data at current TF (timeframe).      This gives challenges. For example the SMA / %change is not a problem (same values at 1h TF from Daily data).       RMA , RSI is not so easy though...      Daily values are rather similar on a 2-3h TF, but 1h TF and lower is quite different.      At the moment I haven't figured out why, if someone has an idea, don't hesitate to share.      The main goal of this publication is 'composite tickers ~ request.security()' though. 🔹 When a ticker value changes substantially (x10, x100), the multiply number needs to be adjusted accordingly. 🔶 SETTINGS SHOW SETS SET    •  Length : length of SMA, RMA and RSI    •  HTF : Higher TimeFrame (default Daily) TABLE    •  Size table :                         \ _ Self-explanatory    •  Include exchange name :  /    •  Sort : If exchange names are shown, the exchanges will be sorted first COLOURS    •  CH%    •  RSI    •  SMA (RMA) DEBUG Remember t46 , T16 ,... ? This can be used for debugging/checking ALWAYS DISABLE " sort " when doing so. Example: Set string -> T1 (tickers FIL, CAKE, SOL) (Numbers are slightly different due to time passing by between screen captures) Placing your tickers at the side panel makes it easy to compare with the printed label below the table (right side, 332201415014.45 ), together with the line T1 in the script: t1 = 'BINANCE:FILUSDT' , n1 = 10e10, r1 = 4, t2 = 'BINANCE:CAKEUSDT' , n2 = 10e5 , r2 = 3, t3 = 'BINANCE:SOLUSDT' , r3 = 2 // T1 FIL     :    3.322 CAKE:    1.415 SOL   : 14.56 Now it is easy to check whether the tickers are placed close enough to each other, with 1-2 zero's in between. If you want to check a specific ticker, use " Show Ticker" , see out initial example: Set string     -> T16 Show ticker -> 46 (in the code -> t46 = 'BINANCE:MTLUSDT') (Set at 0 to disable " check string " and NONE to disable " Set string ") -> Debug/check/set away! 😀 🔶 OTHER TECHNIQUES  • REGEX ( Regular expression ) and str.match() is used to delete the exchange name from the ticker, in other words, everything before ":" is deleted by following regex: exch(t) => incl_exch ? t : str.match(t, "(?<=:) +")  • To sort, array.sort_indices() is used (line 675 in the code), just as in my first "sort" publication Sort array alphabetically - educational aSort = arrT.copy() sort_Indices = array.sort_indices(id= aSort, order= order.ascending)  • Numbers and text colour will adjust automatically when switching between light/dark mode by using chart.fg_color / chart.bg_color 🔹 DISCLAIMER Please don't ask me for custom screeners, thank you.
Pine Script® indicator
by fikira
80
Open Interest Chart [LuxAlgo]The Open Interest Chart displays Commitments of Traders %change of futures open interest , with a unique circular plotting technique, inspired from this publication Periodic Ellipses . 🔶 USAGE Open interest represents the total number of contracts that have been entered by market participants but have not yet been offset or delivered. This can be a direct indicator of market activity/liquidity, with higher open interest indicating a more active market. Increasing open interest is highlighted in green on the circular plot, indicating money coming into the market, while decreasing open interests highlighted in red indicates money coming out of the market. You can set up to 6 different Futures Open interest tickers for a quick follow up: 🔶 DETAILS Circles are drawn, using plot() , with the functions createOuterCircle() (for the largest circle) and createInnerCircle() (for inner circles). Following snippet will reload the chart, so the circles will remain at the right side of the chart: if ta.change(chart.left_visible_bar_time ) or ta.change(chart.right_visible_bar_time) n := bar_index Here is a snippet which will draw a 39-bars wide circle that will keep updating its position to the right. //@version=5 indicator("") n = bar_index barsTillEnd = last_bar_index - n if ta.change(chart.left_visible_bar_time ) or ta.change(chart.right_visible_bar_time) n := bar_index createOuterCircle(radius) => var int end = na var int start = na var basis = 0. barsFromNearestEdgeCircle = 0. barsTillEndFromCircleStart = radius startCylce = barsTillEnd % barsTillEndFromCircleStart == 0 // start circle bars = ta.barssince(startCylce) barsFromNearestEdgeCircle := barsTillEndFromCircleStart -1 basis := math.min(startCylce ? -1 : basis + 1 / barsFromNearestEdgeCircle * 2, 1) // 0 -> 1 shape = math.sqrt(1 - basis * basis) rad = radius / 2 isOK = barsTillEnd <= barsTillEndFromCircleStart and barsTillEnd > 0 hi = isOK ? (rad + shape * radius) - rad : na lo = isOK ? (rad - shape * radius) - rad : na start := barsTillEnd == barsTillEndFromCircleStart ? n -1 : start end := barsTillEnd == 0 ? start + radius : end = createOuterCircle(40) plot(h), plot(l) 🔶 LIMITATIONS Due to the inability to draw between bars, from time to time, drawings can be slightly off. Bar-replay can be demanding, since it has to reload on every bar progression. We don't recommend using this script on bar-replay. If you do, please choose the lowest speed and from time to time pause bar-replay for a second. You'll see the script gets reloaded. 🔶 SETTINGS 🔹 TICKERS Toggle :     • Enabled  -> uses the first column with a pre-filled list of Futures Open Interest tickers/symbols     • Disabled -> uses the empty field where you can enter your own ticker/symbol Pre-filled list : the first column is filled with a list, so you can choose your open interest easily, otherwise you would see COT:088691_F_OI aka Gold Futures Open Interest for example.      If applicable, you will see 3 different COT data:     • COT: Legacy Commitments of Traders report data     • COT2: Disaggregated Commitments of Traders report data     • COT3: Traders in Financial Futures report data Empty field : When needed, you can pick another ticker/symbol in the empty field at the right and disable the toggle. Timeframe : Commitments of Traders (COT) data is tallied by the Commodity ​Futures Trading Commission (CFTC) and is published weekly. Therefore data won't change every day.      Default set TF is Daily 🔹 STYLE From middle:     • Enabled (default): Drawings start from the middle circle -> towards outer circle is + %change , towards middle of the circle is - %change     • Disabled: Drawings start from the middle POINT of the circle, towards outer circle is + OR -     -> in both options, + %change will be coloured green , - %change will be coloured red .     -> 0 %change will be coloured blue , and when no data is available, this will be coloured gray . Size circle : options tiny, small, normal, large, huge. Angle : Only applicable if "From middle" is disabled!     -> sets the angle of the spike: Show Ticker : Name of ticker, as seen in table, will be added to labels. Text - fill     • Sets colour for +/- %change Table     • Sets 2 text colours, size and position Circles     • Sets the colour of circles, style can be changed in the Style section.        You can make it as crazy as you want:
Pine Script® indicator
by LuxAlgo
23
TTP VIX SpyTTP VIX Spy is an indicator that uses data from TVC:VIX to better time entries in the market. The assumption used is that when the VIX is coming down from the top of its range then the risk on assets can move to the upside and when the VIX is is pushing higher there's a high likelihood or risk on assets going down. This indicator observes the momentum of VIX using MACD. It offers two different signals both for longs and shorts: signal 1 and 2. Signal 1 is activate when the begging of a new trend for the VIX is confirmed. Signal 2 is activated when the VIX pulls back from an extreme value. You can configure the parameters of the internal super trend and the look back for the slope applied to price and RSIs. The indicator offers the following filter parameters: - Price RSI slope: it filters signals that have RSI slope pointing in the opposite direction of the signal. - Counter trend: it filters signals that are not counter trending super trend. - Wide BBW: it filters signals that happen when there hasn't been high price volatility - Price slope: it filters signals when the price is not pointing in the direction of the signal (buy: up, sell: down) - VIX RSI filter: it filters VIX RSI values overextended. MACD can be in the right range, but sometimes RSI contradicts it. By default is OFF since it can cause false negatives. - Working days only: it filters signals that occur in the weekend. The colours below the price action show how the VIX momentum is changing. Transitions from red into pink and then green show how the fear is fading which tends to lead to lead to bullish moves, and the opposite when the transitions are from green to red. Performance and initial thoughts. I have tried VIX Spy on both BINANCE:BTCUSDT.P and BINANCE:ETHUSDT.P and it seems to offer a decent win ratio. As you can see I had to add many filter to remove bad entries and left toggles available to decide which ones you want to use. I tried the signal in the 4H, 1H and 15min with mixed results. I tend to incline for the results in the 1H. VIX signal offers a backtestable stream and alerts both for signals 1 and 2.
Pine Script® indicator
by TheTradingParrot
11
Z-Score Probability IndicatorThis is the Z-Score Probability indicator. As many people like my original Z-Score indicator and have expressed more interest in the powers of the Z, I decided to make this indicator which shows additional powers of the Z-Score. Z-Score is not only useful for measuring a ticker or any other variable’s distance from the mean, it is also useful to calculate general probability in a normal distribution set. Not only can it calculate probability in a dataset, but it can also calculate the variables within said dataset by using the Standard Deviation and the Mean of the dataset. Using these 2 aspects of the Z-Score, you can, In principle, have an indicator that operates similar to Fibonacci retracement levels with the added bonus of being able to actually ascertain the realistic probability of said retracement. Let’s take a look at an example: This is a chart showing SPY on the daily timeframe. If we look at the current Z-Score level, we can see that SPY is pushing into the 2 to 3 Z-Score range. We can see two things from this: 1. We can see that a retracement to a Z-Score of 2 would correspond to a price of 425.26 based on the current dataset. And 2. We can see that the probability that SPY retraces to a Z-Score of 2 is around 0.9800 or 98%. To take it one step further, we can look at the various other variables in the distribution. If we were to bet on SPY retracing back to -1 SDs, that would correspond to a price of around 397.15, with a probability of around 0.1600 or 16% (see image below): Let’s say, we thought SPY would go to $440. Well, we can see that the probability SPY goes to 434.64 currently is pretty low. How do we know? Because the Z-Score table shows us the probability of values falling BELOW that Z-score level in the current distribution. So if we look at this example below: We can see that 0.9998 or roughly 99% of values in the current SPY distribution will fall below 434.64. Thus, it may be unrealistic, at this point in time, to target said value. So what is a Z-Score Table? Well, I need to disclose/clarify that the Z-Score Table being displayed in this indicator does Z-Score probability a HUGE injustice. However, with the constraints what is realistic to fit into an indicator, I had to make it far more succinct. Let’s take a look at an actual Z-Score Table below: Above is a look an the actual Z-Score table. How it works is you first identify you’re Z-Score and then find the corresponding value that relates to your score. The number displayed in the dataset represents the number of variables in the dataset/density distribution that fall BELOW that particular Z-score. So, for example, if we have a Z-Score of -2.31, we can consult that table, go to the -2.3 then scroll across to the 0.01 to represent -2.31. We would see that this Z-Score corresponds to a 0.0104 probability zone (or essentially 1%) indicating that the majority of the variables in the distribution fall below that mean Z-score. In terms of tickers and stocks, that would mean it would theoretically be “overbought”. So what does the indicator Z-Table tell us? I have averaged out the data for the purposes of this indicator. However, you can also reference a manual Z-Table to get the exact probability for the current precise Z-Score. However, the reality is it doesn’t necessarily matter to be exact when it comes to tickers. The reason being, ticker’s are in constant flux, and by the time you identify that probability, the ticker will already be at a different level. So generalizations are okay in these circumstances, you just need to get the “gist” of where the distribution lies. So how do I use the indicator? Using the indicator is pretty straightforward. Once launched, you will see the current Z-Score of the ticker, the current levels based on the distribution and the summarized Z-Table. The Z-Table will turn gray to indicate the zone the ticker is currently in. In this case, we can see that SPY currently is in the 2 SD Zone, meaning that 0.98 or 98% of the current dataset being shown falls below the price we are at: When we launch the settings, we can see a few inputs. Lookback Length: This determines the number of candles back we want to calculate the distribution for. It is defaulted to 75, but you can adjust it to whichever length you want. SMA Length: The SMA is optional but defaults to on. If you want to see the smoothed trend of the Z-Score, this will do the trick. It does not need to be set to the same length as the Z-Score lookback. Thus, if you want a more or less responsive SMA with, say, a larger dataset, then you can reduce the SMA length yourself. Distribution Probability Fills: This simply colour codes the distribution zones / probability zones on the indicator. Show Z-Table: This will display the summarized Z-Table. Show SMA: As I indicated, the SMA is optional, you can toggle it on or off to see the overall Z-Score trend. Concluding Remarks: And that my friends is the Z-Score Probability Indicator. I hope you all enjoy it and find it helpful. As always leave your comments, questions and suggestions below. Safe trades to all and take care!
Pine Script® indicator
by Steversteves
64
Interactive Motive Wave ChecklistHere is an interactive tool that can be used for learning a bit about Elliott Waves 🎲 How it works? The script upon load asks users to enter 6 pivots in an order. Once all 6 pivots are selected on the interactive chart, the script will calculate if the structure is a valid motive wave. When you load the script, you will see a prompt on the chart to select points on the chart to form 6 pivots. When you select the 6 pivots, the checklists are populated on the chart to notify users which conditions for qualifying the selection has passed and which of them are failed. 🎲 Conditions for Motive Wave Motive wave can be either Impulse or Diagonal Wave. Diagonal wave can be either expanding or contracting diagonals. To learn more about diagonal waves, please go through this idea. Rules for generic motive waves are as below Pivots in order - Checks wether the pivots selected are in progressive order. Directions in order - Checks if the pivot directions are correct - either PH, PL, PH, PL, PH, PL or PL, PH, PL, PH, PL, PH Wave 2 never moves beyond the start of wave 1 - Wave 2 retracement is less than 100% of wave1 Wave 3 always moves beyond the end of wave 1 - Wave 3 retracement is more than 100% of wave2 Wave 3 is never the shortest one - Checks if Wave 3 is bigger than either Wave 1 or wave 5 or both. Now, these are the specific rules for Impulse Waves on top of Motive Wave conditions Wave 4 never moves beyond the end of Wave 1 - meaning wave 1 and wave 4 never overlap on price scale. Wave 1, 3, 5 are all not extended. We check for retracement ratios of more than 200% to be considered as extended wave. Below are the conditions for Diagonal Waves on top of Motive Wave conditions Wave4 never moves beyond the start of Wave 3 - Wave 4 retracement is less than 100% Wave 4 always ends within the price territory of Wave 1 - Unlike impulse wave, wave 4 intersects with wave 1 in case of diagonal waves. This is the major difference between impulse and diagonal wave. Waves are progressively expanding or contracting - Wave1 > Wave3 > Wave5 and Wave2 > Wave4 to be contracting diagonal. Wave1 < Wave3 < Wave5 and Wave2 < Wave4 to be expanding diagonal wave. Here is an example of diagonal wave projection Here is an example of impulse wave projection
Pine Script® indicator
by Trendoscope
25
Anchored VWAP Pinch & Handoff, Intervals, and Signals"Anchored VWAP Pinch & Handoff, Intervals, and Signals" is an AVWAP toolbox for those who like to use various VWAP trading techniques. The indicator is currently comprised of the following three sections: • The Pinch & Handoff section (shown above on chart) allows manually setting an upper and lower AVWAP (Pinch) along with an additional AVWAP (Handoff) by entering dates or by dragging the vertical anchor lines to the desired significant events on chart. Each of these three AVWAPs can also be set to show zones above and/or below by a percentage or standard deviation amount. The theory behind this method is that the upper and lower AVWAPs may act as dynamic support and resistance levels, effectively creating a price range or channel. As price moves between these two VWAP levels, it becomes squeezed or consolidated within that range. Further conjecture is that the longer the price remains within the range of the two anchored VWAP values, the higher the potential for an explosive breakout. Traders using this strategy may interpret the prolonged consolidation as a period of price compression, with the expectation that a significant move in either direction is likely to occur. Traders employing the AVWAP Pinch strategy might look for specific chart patterns or additional confirmation signals to enter a trade. For example, a breakout above the upper anchored VWAP level could trigger a long trade, while a breakdown below the lower anchored VWAP level could signal a short trade. Stop-loss orders and profit targets are typically set based on the trader's risk tolerance and the volatility of the asset. The third AVWAP (Handoff) is typically set after price has broken through the Pinch, and is used as a new level of support or resistance. The "Pinch & Handoff" phrase is believed to have been coined by Brian Shannon, who has popularized this method. • The Intervals section (shown above on chart) is comprised of six periodic AVWAPs which cyclically reset. Their default settings are 1 Day, 2 Days, 1 Week, 1 Month, 1 Quarter, and 1 Year. They each may be set to desired period and when they are enabled the VWAPs whose periods are lower than the current chart timeframe are automatically hidden. For example a 1 Day AVWAP is not useful on a 1 Week chart so it would be hidden from that timeframe. When using AVWAPs from higher timeframes it may be helpful to set your chart to "Scale price chart only". This can be enabled by right clicking on your chart's price column and then left clicking "Scale price chart only" to enable that option. • The Auto section (shown above on chart) is comprised of two automatic Anchored VWAPs. There are choices for setting anchors automatically based upon Highest Source, Highest Volume, Lowest Source, Lowest Volume, Pivot High, and Pivot Low. Because these two VWAPs work retroactively they are drawn with lines instead of plots. There is currently a limitation of 500 lines that may be drawn at any given time and the logic within this indicator uses a line for every bar of VWAP that is drawn, so if the combined length of both of these VWAPs exceeds 500 bars the earliest lines would disappear. For typical use of looking for the highest high in the last 50 bars or the last fractal this limitation should not be an issue. --- All of the plots have been titled including hidden plots that are generated for the AVWAP line drawings. All of the various types of AVWAP within the indicator should be available as choices within the Alert creation dialog if use of alerts is desired. --- NOTICE: This is an example script and not meant to be used as an actual strategy. By using this script or any portion thereof, you acknowledge that you have read and understood that this is for research purposes only and I am not responsible for any financial losses you may incur by using this script!
Pine Script® indicator
by allanster
41
Kernel Regression ToolkitThis toolkit provides filters and extra functionality for non-repainting Nadaraya-Watson estimator implementations made by @jdehorty. For the sake of ease I have nicknamed it "kreg". Filters include a smoothing formula and zero lag formula. The purpose of this script is to help traders test, experiment and develop different regression lines. Regression lines are best used as trend lines and can be an invaluable asset for quickly locating first pullbacks and breaks of trends. Other features include two J lines and a blend line. J lines are featured in tools like Stochastic KDJ. The formula uses the distance between K and D lines to make the J line. The blend line adds the ability to blend two lines together. This can be useful for several tasks including finding a center/median line between two lines or for blending in the characteristics of a different line. Default is set to 50 which is a 50% blend of the two lines. This can be increased and decreased to taste. This tool can be overlaid on the chart or on top of another indicator if you set the source. It can even be moved into its own window to create a unique oscillator based on whatever sources you feed it. Below are the standard settings for the kernel estimation as documented by @jdehorty: Lookback Window: The number of bars used for the estimation. This is a sliding value that represents the most recent historical bars. Recommended range: 3-50 Weighting: Relative weighting of time frames. As this value approaches zero, the longer time frames will exert more influence on the estimation. As this value approaches infinity, the behavior of the Rational Quadratic Kernel will become identical to the Gaussian kernel. Recommended range: 0.25-25 Level: Bar index on which to start regression. Controls how tightly fit the kernel estimate is to the data. Smaller values are a tighter fit. Larger values are a looser fit. Recommended range: 2-25 Lag: Lag for crossover detection. Lower values result in earlier crossovers. Recommended range: 1-2 For more information on this technique refer to to the original open source indicator by @jdehorty located here:
Pine Script® indicator
by veryfid
14
Vector3Library "Vector3" Representation of 3D vectors and points. This structure is used to pass 3D positions and directions around. It also contains functions for doing common vector operations. Besides the functions listed below, other classes can be used to manipulate vectors and points as well. For example the Quaternion and the Matrix4x4 classes are useful for rotating or transforming vectors and points. ___ **Reference:** - github.com - github.com - github.com - www.movable-type.co.uk - docs.unity3d.com - referencesource.microsoft.com - github.com \ new(x, y, z)   Create a new `Vector3`.   Parameters:      x (float) : `float` Property `x` value, (optional, default=na).      y (float) : `float` Property `y` value, (optional, default=na).      z (float) : `float` Property `z` value, (optional, default=na).   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .new(1.1, 1, 1) ``` from(value)   Create a new `Vector3` from a single value.   Parameters:      value (float) : `float` Properties positional value, (optional, default=na).   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .from(1.1) ``` from_Array(values, fill_na)   Create a new `Vector3` from a list of values, only reads up to the third item.   Parameters:      values (float ) : `array` Vector property values.      fill_na (float) : `float` Parameter value to replace missing indexes, (optional, defualt=na).   Returns: `Vector3` Generated new vector. ___ **Notes:** - Supports any size of array, fills non available fields with `na`. ___ **Usage:** ``` .from_Array(array.from(1.1, fill_na=33)) .from_Array(array.from(1.1, 2, 3)) ``` from_Vector2(values)   Create a new `Vector3` from a `Vector2`.   Parameters:      values (Vector2 type from RicardoSantos/CommonTypesMath/1) : `Vector2` Vector property values.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .from:Vector2(.Vector2.new(1, 2.0)) ``` ___ **Notes:** - Type `Vector2` from CommonTypesMath library. from_Quaternion(values)   Create a new `Vector3` from a `Quaternion`'s `x, y, z` properties.   Parameters:      values (Quaternion type from RicardoSantos/CommonTypesMath/1) : `Quaternion` Vector property values.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .from_Quaternion(.Quaternion.new(1, 2, 3, 4)) ``` ___ **Notes:** - Type `Quaternion` from CommonTypesMath library. from_String(expression, separator, fill_na)   Create a new `Vector3` from a list of values in a formated string.   Parameters:      expression (string) : `array` String with the list of vector properties.      separator (string) : `string` Separator between entries, (optional, default=`","`).      fill_na (float) : `float` Parameter value to replace missing indexes, (optional, defualt=na).   Returns: `Vector3` Generated new vector. ___ **Notes:** - Supports any size of array, fills non available fields with `na`. - `",,"` Empty fields will be ignored. ___ **Usage:** ``` .from_String("1.1", fill_na=33)) .from_String("(1.1,, 3)") // 1.1 , 3.0, NaN // empty field will be ignored!! ``` back()   Create a new `Vector3` object in the form `(0, 0, -1)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .back() ``` front()   Create a new `Vector3` object in the form `(0, 0, 1)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .front() ``` up()   Create a new `Vector3` object in the form `(0, 1, 0)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .up() ``` down()   Create a new `Vector3` object in the form `(0, -1, 0)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .down() ``` left()   Create a new `Vector3` object in the form `(-1, 0, 0)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .left() ``` right()   Create a new `Vector3` object in the form `(1, 0, 0)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .right() ``` zero()   Create a new `Vector3` object in the form `(0, 0, 0)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .zero() ``` one()   Create a new `Vector3` object in the form `(1, 1, 1)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .one() ``` minus_one()   Create a new `Vector3` object in the form `(-1, -1, -1)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .minus_one() ``` unit_x()   Create a new `Vector3` object in the form `(1, 0, 0)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .unit_x() ``` unit_y()   Create a new `Vector3` object in the form `(0, 1, 0)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .unit_y() ``` unit_z()   Create a new `Vector3` object in the form `(0, 0, 1)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .unit_z() ``` nan()   Create a new `Vector3` object in the form `(na, na, na)`.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .nan() ``` random(max, min)   Generate a vector with random properties.   Parameters:      max (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Maximum defined range of the vector properties.      min (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Minimum defined range of the vector properties.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .random(.from(math.pi), .from(-math.pi)) ``` random(max)   Generate a vector with random properties (min set to 0.0).   Parameters:      max (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Maximum defined range of the vector properties.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` .random(.from(math.pi)) ``` method copy(this)   Copy a existing `Vector3`   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .one().copy() ``` method i_add(this, other)   Modify a instance of a vector by adding a vector to it.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other Vector.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_add(.up()) ``` method i_add(this, value)   Modify a instance of a vector by adding a vector to it.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      value (float) : `float` Value.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_add(3.2) ``` method i_subtract(this, other)   Modify a instance of a vector by subtracting a vector to it.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other Vector.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_subtract(.down()) ``` method i_subtract(this, value)   Modify a instance of a vector by subtracting a vector to it.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      value (float) : `float` Value.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_subtract(3) ``` method i_multiply(this, other)   Modify a instance of a vector by multiplying a vector with it.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other Vector.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_multiply(.left()) ``` method i_multiply(this, value)   Modify a instance of a vector by multiplying a vector with it.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      value (float) : `float` value.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_multiply(3) ``` method i_divide(this, other)   Modify a instance of a vector by dividing it by another vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other Vector.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_divide(.forward()) ``` method i_divide(this, value)   Modify a instance of a vector by dividing it by another vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      value (float) : `float` Value.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_divide(3) ``` method i_mod(this, other)   Modify a instance of a vector by modulo assignment with another vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other Vector.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_mod(.back()) ``` method i_mod(this, value)   Modify a instance of a vector by modulo assignment with another vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      value (float) : `float` Value.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_mod(3) ``` method i_pow(this, exponent)   Modify a instance of a vector by modulo assignment with another vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      exponent (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Exponent Vector.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_pow(.up()) ``` method i_pow(this, exponent)   Modify a instance of a vector by modulo assignment with another vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      exponent (float) : `float` Exponent Value.   Returns: `Vector3` Updated source vector. ___ **Usage:** ``` a = .from(1) , a.i_pow(2) ``` method length_squared(this)   Squared length of the vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1)   Returns: `float` The squared length of this vector. ___ **Usage:** ``` a = .one().length_squared() ``` method magnitude_squared(this)   Squared magnitude of the vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `float` The length squared of this vector. ___ **Usage:** ``` a = .one().magnitude_squared() ``` method length(this)   Length of the vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `float` The length of this vector. ___ **Usage:** ``` a = .one().length() ``` method magnitude(this)   Magnitude of the vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `float` The Length of this vector. ___ **Usage:** ``` a = .one().magnitude() ``` method normalize(this, magnitude, eps)   Normalize a vector with a magnitude of 1(optional).   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      magnitude (float) : `float` Value to manipulate the magnitude of normalization, (optional, default=1.0).      eps (float)   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .new(33, 50, 100).normalize() // (x=0.283, y=0.429, z=0.858) a = .new(33, 50, 100).normalize(2) // (x=0.142, y=0.214, z=0.429) ``` method to_String(this, precision)   Converts source vector to a string format, in the form `"(x, y, z)"`.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      precision (string) : `string` Precision format to apply to values (optional, default='').   Returns: `string` Formated string in a `"(x, y, z)"` format. ___ **Usage:** ``` a = .one().to_String("#.###") ``` method to_Array(this)   Converts source vector to a array format.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `array` List of the vector properties. ___ **Usage:** ``` a = .new(1, 2, 3).to_Array() ``` method to_Vector2(this)   Converts source vector to a Vector2 in the form `x, y`.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector2` Generated new vector. ___ **Usage:** ``` a = .from(1).to_Vector2() ``` method to_Quaternion(this, w)   Converts source vector to a Quaternion in the form `x, y, z, w`.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Sorce vector.      w (float) : `float` Property of `w` new value.   Returns: `Quaternion` Generated new vector. ___ **Usage:** ``` a = .from(1).to_Quaternion(w=1) ``` method add(this, other)   Add a vector to source vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).add(.unit_z()) ``` method add(this, value)   Add a value to each property of the vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      value (float) : `float` Value.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).add(2.0) ``` add(value, other)   Add each property of a vector to a base value as a new vector.   Parameters:      value (float) : `float` Value.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(2) , b = .add(1.0, a) ``` method subtract(this, other)   Subtract vector from source vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).subtract(.left()) ``` method subtract(this, value)   Subtract a value from each property in source vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      value (float) : `float` Value.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).subtract(2.0) ``` subtract(value, other)   Subtract each property in a vector from a base value and create a new vector.   Parameters:      value (float) : `float` Value.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .subtract(1.0, .right()) ``` method multiply(this, other)   Multiply a vector by another.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).multiply(.up()) ``` method multiply(this, value)   Multiply each element in source vector with a value.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      value (float) : `float` Value.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).multiply(2.0) ``` multiply(value, other)   Multiply a value with each property in a vector and create a new vector.   Parameters:      value (float) : `float` Value.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .multiply(1.0, .new(1, 2, 1)) ``` method divide(this, other)   Divide a vector by another.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).divide(.from(2)) ``` method divide(this, value)   Divide each property in a vector by a value.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      value (float) : `float` Value.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).divide(2.0) ``` divide(value, other)   Divide a base value by each property in a vector and create a new vector.   Parameters:      value (float) : `float` Value.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .divide(1.0, .from(2)) ``` method mod(this, other)   Modulo a vector by another.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).mod(.from(2)) ``` method mod(this, value)   Modulo each property in a vector by a value.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      value (float) : `float` Value.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).mod(2.0) ``` mod(value, other)   Modulo a base value by each property in a vector and create a new vector.   Parameters:      value (float) : `float` Value.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .mod(1.0, .from(2)) ``` method negate(this)   Negate a vector in the form `(zero - this)`.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .one().negate() ``` method pow(this, other)   Modulo a vector by another.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(2).pow(.from(3)) ``` method pow(this, exponent)   Raise the vector elements by a exponent.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      exponent (float) : `float` The exponent to raise the vector by.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).pow(2.0) ``` pow(value, exponent)   Raise value into a vector raised by the elements in exponent vector.   Parameters:      value (float) : `float` Base value.      exponent (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` The exponent to raise the vector of base value by.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .pow(1.0, .from(2)) ``` method sqrt(this)   Square root of the elements in a vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).sqrt() ``` method abs(this)   Absolute properties of the vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).abs() ``` method max(this)   Highest property of the vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `float` Highest value amongst the vector properties. ___ **Usage:** ``` a = .new(1, 2, 3).max() ``` method min(this)   Lowest element of the vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `float` Lowest values amongst the vector properties. ___ **Usage:** ``` a = .new(1, 2, 3).min() ``` method floor(this)   Floor of vector a.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .new(1.33, 1.66, 1.99).floor() ``` method ceil(this)   Ceil of vector a.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .new(1.33, 1.66, 1.99).ceil() ``` method round(this)   Round of vector elements.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .new(1.33, 1.66, 1.99).round() ``` method round(this, precision)   Round of vector elements to n digits.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      precision (int) : `int` Number of digits to round the vector elements.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .new(1.33, 1.66, 1.99).round(1) // 1.3, 1.7, 2 ``` method fractional(this)   Fractional parts of vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1.337).fractional() // 0.337 ``` method dot_product(this, other)   Dot product of two vectors.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other vector.   Returns: `float` Dot product. ___ **Usage:** ``` a = .from(2).dot_product(.left()) ``` method cross_product(this, other)   Cross product of two vectors.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).cross_produc(.right()) ``` method scale(this, scalar)   Scale vector by a scalar value.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      scalar (float) : `float` Value to scale the the vector by.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).scale(2) ``` method rescale(this, magnitude)   Rescale a vector to a new magnitude.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      magnitude (float) : `float` Value to manipulate the magnitude of normalization.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(20).rescale(1) ``` method equals(this, other)   Compares two vectors.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      other (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Other vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).equals(.one()) ``` method sin(this)   Sine of vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).sin() ``` method cos(this)   Cosine of vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).cos() ``` method tan(this)   Tangent of vector.   Namespace types: TMath.Vector3   Parameters:      this (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .from(1).tan() ``` vmax(a, b)   Highest elements of the properties from two vectors.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .vmax(.one(), .from(2)) ``` vmax(a, b, c)   Highest elements of the properties from three vectors.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.      c (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .vmax(.new(0.1, 2.5, 3.4), .from(2), .from(3)) ``` vmin(a, b)   Lowest elements of the properties from two vectors.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .vmin(.one(), .from(2)) ``` vmin(a, b, c)   Lowest elements of the properties from three vectors.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.      c (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .vmin(.one(), .from(2), .new(3.3, 2.2, 0.5)) ``` distance(a, b)   Distance between vector `a` and `b`.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Target vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = distance(.from(3), .unit_z()) ``` clamp(a, min, max)   Restrict a vector between a min and max vector.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      min (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Minimum boundary vector.      max (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Maximum boundary vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .clamp(a=.new(2.9, 1.5, 3.9), min=.from(2), max=.new(2.5, 3.0, 3.5)) ``` clamp_magnitude(a, radius)   Vector with its magnitude clamped to a radius.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.object, vector with properties that should be restricted to a radius.      radius (float) : `float` Maximum radius to restrict magnitude of vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .clamp_magnitude(.from(21), 7) ``` lerp_unclamped(a, b, rate)   `Unclamped` linearly interpolates between provided vectors by a rate.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Target vector.      rate (float) : `float` Rate of interpolation, range(0 > 1) where 0 == source vector and 1 == target vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .lerp_unclamped(.from(1), .from(2), 1.2) ``` lerp(a, b, rate)   Linearly interpolates between provided vectors by a rate.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Target vector.      rate (float) : `float` Rate of interpolation, range(0 > 1) where 0 == source vector and 1 == target vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = lerp(.one(), .from(2), 0.2) ``` herp(start, start_tangent, end, end_tangent, rate)   Hermite curve interpolation between provided vectors.   Parameters:      start (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Start vector.      start_tangent (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Start vector tangent.      end (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` End vector.      end_tangent (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` End vector tangent.      rate (int) : `float` Rate of the movement from `start` to `end` to get position, should be range(0 > 1).   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` s = .new(0, 0, 0) , st = .new(0, 1, 1) e = .new(1, 2, 2) , et = .new(-1, -1, 3) h = .herp(s, st, e, et, 0.3) ``` ___ **Reference:** en.m.wikibooks.org herp_2(a, b, rate)   Hermite curve interpolation between provided vectors.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Target vector.      rate (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Rate of the movement per component from `start` to `end` to get position, should be range(0 > 1).   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` h = .herp_2(.one(), .new(0.1, 3, 2), 0.6) ``` noise(a)   3D Noise based on Morgan McGuire @morgan3d   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = noise(.one()) ``` ___ **Reference:** - thebookofshaders.com - www.shadertoy.com rotate(a, axis, angle)   Rotate a vector around a axis.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      axis (string) : `string` The plane to rotate around, `option="x", "y", "z"`.      angle (float) : `float` Angle in radians.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .rotate(.from(3), 'y', math.toradians(45.0)) ``` rotate_x(a, angle)   Rotate a vector on a fixed `x`.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      angle (float) : `float` Angle in radians.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .rotate_x(.from(3), math.toradians(90.0)) ``` rotate_y(a, angle)   Rotate a vector on a fixed `y`.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      angle (float) : `float` Angle in radians.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .rotate_y(.from(3), math.toradians(90.0)) ``` rotate_yaw_pitch(a, yaw, pitch)   Rotate a vector by yaw and pitch values.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      yaw (float) : `float` Angle in radians.      pitch (float) : `float` Angle in radians.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .rotate_yaw_pitch(.from(3), math.toradians(90.0), math.toradians(45.0)) ``` project(a, normal, eps)   Project a vector off a plane defined by a normal.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      normal (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` The normal of the surface being reflected off.      eps (float) : `float` Minimum resolution to void division by zero (default=0.000001).   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .project(.one(), .down()) ``` project_on_plane(a, normal, eps)   Projects a vector onto a plane defined by a normal orthogonal to the plane.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      normal (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` The normal of the surface being reflected off.      eps (float) : `float` Minimum resolution to void division by zero (default=0.000001).   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .project_on_plane(.one(), .left()) ``` project_to_2d(a, camera_position, camera_target)   Project a vector onto a two dimensions plane.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      camera_position (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Camera position.      camera_target (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Camera target plane position.   Returns: `Vector2` Generated new vector. ___ **Usage:** ``` a = .project_to_2d(.one(), .new(2, 2, 3), .zero()) ``` reflect(a, normal)   Reflects a vector off a plane defined by a normal.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      normal (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` The normal of the surface being reflected off.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .reflect(.one(), .right()) ``` angle(a, b, eps)   Angle in degrees between two vectors.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Target vector.      eps (float) : `float` Minimum resolution to void division by zero (default=1.0e-15).   Returns: `float` Angle value in degrees. ___ **Usage:** ``` a = .angle(.one(), .up()) ``` angle_signed(a, b, axis)   Signed angle in degrees between two vectors.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Target vector.      axis (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Axis vector.   Returns: `float` Angle value in degrees. ___ **Usage:** ``` a = .angle_signed(.one(), .left(), .down()) ``` ___ **Notes:** - The smaller of the two possible angles between the two vectors is returned, therefore the result will never be greater than 180 degrees or smaller than -180 degrees. - If you imagine the from and to vectors as lines on a piece of paper, both originating from the same point, then the /axis/ vector would point up out of the paper. - The measured angle between the two vectors would be positive in a clockwise direction and negative in an anti-clockwise direction. ___ **Reference:** - github.com angle2d(a, b)   2D angle between two vectors.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      b (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Target vector.   Returns: `float` Angle value in degrees. ___ **Usage:** ``` a = .angle2d(.one(), .left()) ``` transform_Matrix(a, M)   Transforms a vector by the given matrix.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      M (matrix) : `matrix` A 4x4 matrix. The transformation matrix.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` mat = matrix.new(4, 0) mat.add_row(0, array.from(0.0, 0.0, 0.0, 1.0)) mat.add_row(1, array.from(0.0, 0.0, 1.0, 0.0)) mat.add_row(2, array.from(0.0, 1.0, 0.0, 0.0)) mat.add_row(3, array.from(1.0, 0.0, 0.0, 0.0)) b = .transform_Matrix(.one(), mat) ``` transform_M44(a, M)   Transforms a vector by the given matrix.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      M (M44 type from RicardoSantos/CommonTypesMath/1) : `M44` A 4x4 matrix. The transformation matrix.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .transform_M44(.one(), .M44.new(0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0)) ``` ___ **Notes:** - Type `M44` from `CommonTypesMath` library. transform_normal_Matrix(a, M)   Transforms a vector by the given matrix.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      M (matrix) : `matrix` A 4x4 matrix. The transformation matrix.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` mat = matrix.new(4, 0) mat.add_row(0, array.from(0.0, 0.0, 0.0, 1.0)) mat.add_row(1, array.from(0.0, 0.0, 1.0, 0.0)) mat.add_row(2, array.from(0.0, 1.0, 0.0, 0.0)) mat.add_row(3, array.from(1.0, 0.0, 0.0, 0.0)) b = .transform_normal_Matrix(.one(), mat) ``` transform_normal_M44(a, M)   Transforms a vector by the given matrix.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector.      M (M44 type from RicardoSantos/CommonTypesMath/1) : `M44` A 4x4 matrix. The transformation matrix.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .transform_normal_M44(.one(), .M44.new(0,0,0,1,0,0,1,0,0,1,0,0,1,0,0,0)) ``` ___ **Notes:** - Type `M44` from `CommonTypesMath` library. transform_Array(a, rotation)   Transforms a vector by the given Quaternion rotation value.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector. The source vector to be rotated.      rotation (float ) : `array` A 4 element array. Quaternion. The rotation to apply.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .transform_Array(.one(), array.from(0.2, 0.2, 0.2, 1.0)) ``` ___ **Reference:** - referencesource.microsoft.com transform_Quaternion(a, rotation)   Transforms a vector by the given Quaternion rotation value.   Parameters:      a (Vector3 type from RicardoSantos/CommonTypesMath/1) : `Vector3` Source vector. The source vector to be rotated.      rotation (Quaternion type from RicardoSantos/CommonTypesMath/1) : `array` A 4 element array. Quaternion. The rotation to apply.   Returns: `Vector3` Generated new vector. ___ **Usage:** ``` a = .transform_Quaternion(.one(), .Quaternion.new(0.2, 0.2, 0.2, 1.0)) ``` ___ **Notes:** - Type `Quaternion` from `CommonTypesMath` library. ___ **Reference:** - referencesource.microsoft.com
Pine Script® library
by RicardoSantos
7
Intraday Intensity ModesIntraday Intensity Index was created by David Bostian and its use was later featured by John Bollinger in his book "Bollinger on Bollinger Bands" . It is categorically a volume indicator and considered to be a useful tool for analyzing supply and demand dynamics in the market. By measuring the level of buying and selling pressure within a given trading session it attempts to provide insights into the strength of market participants' interest and their aggressiveness in executing trades throughout the day. It can be used in conjunction with Bollinger Bands® or other envelope type indicators as a complimentary indicator to aid in trying to identify potential turning points or trends. Intraday intensity is calculated based upon the relationship between the price change and the volume of shares traded during each daily interval. It aims to capture the level of buying or selling activity relative to the overall volume. A high intraday intensity value suggests a higher level of buying or selling pressure, indicating a more active and potentially volatile market. Conversely, a low intraday intensity value indicates less pronounced trading activity and a potentially quieter market. Overall, intraday intensity provides a concise description of the intensity of trading activity during a particular trading session, giving traders an additional perspective on market dynamics. Note that because the calculation uses volume this indicator will only work on symbols where volume is available. While there are pre-existing versions within community scripts, none were found to have applied the calculations necessary for the various modes that are presented within this version, which are believed to be operating in the manner originally intended when first described by Bostian and again later by Bollinger. When operating in default modes on daily or lower chart timeframes the logic used within this script tracks the intraday high, low, close and volume for the day with each progressing intraday bar. The BB indicator was included on the top main chart to help illustrate example usage as described below. The Intraday Intensity Modes indicator is pictured operating in three different modes beneath the main chart: • The top pane beneath the main chart shows the indicator operating as a normalized 21 day II% oscillator. A potential use while in this mode would be to look for positive values as potential confirmation of strength when price tags the upper or lower Bollinger bands, and to look for negative values as potential confirmation of weakness when price tags the upper or lower Bollinger bands. • The middle pane shows the indicator operating as an "open ended" cumulative sum of II. A potential use while in this mode would be to look for convergence or divergence of trend when price is making new highs or lows, or while price is walking the upper or lower Bollinger bands. • The bottom pane shows the indicator operating in standard III mode, which provides independent values per session. Indicator Settings: Inputs tab: Osc Length : Set to 1 disables oscillation, values greater than 1 enables oscillation for II% (Intraday Intensity percent) mode. Tootip : Hover mouse over (i) to show recommended example Settings for various modes. Cumulative : When enabled values are cumulatively summed for the entire chart and indicator operates in II mode. Normalized : When enabled a rolling window of Osc Length values are summed and normalized to the rolling window's volume. Intrabar : When enabled price range and volume are evaluated for intensity per bar instead of per day which is a departure from the original concept. Whenever this setting is enabled the indicator should be regarded as operating in an experimental mode. Colors For Up Down : Sets the plot colors used, may be overridden in Settings:Style tab. Styles / Width : Sets the plot style and width used, may be overridden in Settings:Style tab. This indicator is designed to work with any chart timeframe, with the understanding that when used on timeframes higher than daily the indicator becomes "IntraPeriod" intensity, for example on weekly bars it would be "IntraWeek" intensity. On Daily or lower timeframes the indicator operates as "IntraDay" intensity and is being updated on each bar as each day progresses. If the experimental setting Intrabar is enabled then the indicator operates as "IntraBar" intensity and is no longer constrained to daily or higher evaluations, for example with Intrabar enabled on a 4H timeframe the indicator would operate as "Intra4H" intensity. NOTICE: This is an example script and not meant to be used as an actual strategy. By using this script or any portion thereof, you acknowledge that you have read and understood that this is for research purposes only and I am not responsible for any financial losses you may incur by using this script!
Pine Script® indicator
by allanster
14
[MAD] Position starter & calculatorThe tool you're using is a financial instrument trading planner and analyzer. Here is how to use it: Trade Planning: You can plan your trade entries and exits, calculating potential profits, losses, and their ratio (P/L ratio). You can define up to five target closing prices with varying volumes, which can be individually activated or deactivated (volume set to 0%). Risk Management: There's a stop-loss function to calculate and limit potential losses. Additionally, it includes a liquidation pre-calculation for adjustable leverages and position maintenance(subject to exchange variation). Customization: You can customize the tool's appearance with five adjustable color schemes, light and dark. ----------------- Initiation: This tool functions as an indicator. To start, add it as an indicator. Once added, you can close the indicator window. Now wait, till you'll see a blue box at the bottom of the input window. Parameter Input: Enter your parameters (SL, box left, box right, TP1, TP2, TP3, TP4, TP5) in the direction of the desired trade. Click from top to bottom for a short trade or bottom to top for a long trade. Adjustment: If you want to move the box in the future, adjust the times in the indicator settings directly as click input is not yet platform-supported. This tool functions as a ruler and doesn't offer alerts (for now). Here is another examples of how to set up a Position-calculation but here for a short: Have fun trading
Pine Script® indicator
by djmad
16
Seasonality [TFO]This Seasonality indicator is meant to provide insight into an asset's average performance over specified periods of time (Daily, Monthly, and Quarterly). It is based on a 252 trading day calendar, not a 365 day calendar. Therefore, some estimations are used in order to aggregate the Daily data into higher timeframes, as we assume every Month to be 21 trading days, and every Quarter to be 63 trading days. Instead of collecting data on the 1st day of a given month, we are actually treating it as the "nth" trading day of the year. Some years exceed 252 trading days, some fall short; however 252 is the average that we are working with for US stocks and indices. Results may vary for non-US markets. Main features: - Statistics Table - Performance Analysis - Seasonal Pivots The Statistics Table provides a summarized view of the current seasonality: whether the average Day/Month/Quarter tends to be bullish or bearish, what the average percent change is, and what the current (actual) change is relative to the historical value. It is shown in the top right of this chart. The Performance Analysis shows a histogram of the average percentage performance for the selected timeframe. Here we have options for Daily, Monthly, and Quarterly. The previous chart showed the Monthly timeframe, here we have the Daily and Quarterly. Lastly, Seasonal Pivots show where highs and lows tend to be created throughout the year, based on an aggregation of the Daily performance data collected over the available years. If we anchor our data to the beginning of the current year, and then manually offset it by ~252 (depending on the year), we can line this data up with the previous years' data and observe how well these Seasonal Pivots lined up with major Daily highs and lows. Styling options are available for every major component of this indicator. Please consider sharing if you find it useful!
Pine Script® indicator
by tradeforopp
33
Cleaner Screeners LibraryLibrary "cleanscreens" Screener Panel. This indicator displays a panel with a list of symbols and their indications. It can be used as a screener for multiple timess and symbols in any timeframe and with any indication in any combination. #### Features Multiple timeframes Multiple symbols Multiple indications per group Vertical or horizontal layouts Acceepts External Inputs Customizable colors with 170 presets included (dark and light) Customizable icons Customizable text size and font Customizable cell size width and height Customizable frame width and border width Customizable position Customizable strong and weak values Accepts any indicator as input Only 4 functions to call, easy to use #### Usage Initialize the panel with _paneel = cleanscreens.init() Add groupd with _screener = cleanscreens.Screener(_paneel, "Group Name") Add indicators to screeener groups with cleanscreens.Indicator(_screener, "Indicator Name", _source) Update the panel with cleanscreens.display(_paneel) Thanks @ PineCoders , and the Group members for setting the bar high. # local setup for methods on our script import kaigouthro/cleanscreen/1 method Screener ( panel p, string _name) => cleanscreens.Screener ( p, _name) method Indicator ( screener s , string _tf, string name, float val) => cleanscreens.Indicator ( s , _tf, name, val) method display ( panel p ) => cleanscreens.display ( p ) init(_themein, loc)   # Panel init > init a panel for all the screens   Parameters:      _themein (string) : string: Theme Preset Name      loc (int) : int : 1 = left top, 2 = middle top, 3 = right top, 4 = left middle, 5 = middle middle, 6 = right middle, 7 = left bottom, 8 = middle bottom, 9 = right bottom   Returns: panel method Screener(p, _name)   # Screener - Create a new screener ### Example: cleanscreens.new(panel, 'Crpyto Screeners')   Namespace types: panel   Parameters:      p (panel)      _name (string) method Indicator(s, _tf, name, val)   # Indicator - Create a new Indicator ### Example: cleanscreens.Inidcator('1h', 'RSI', ta.rsi(close, 14))   Namespace types: screener   Parameters:      s (screener)      _tf (string)      name (string)      val (float) method display(p)   # Display - Display the Panel ### Example: cleanscreens.display(panel)   Namespace types: panel   Parameters:      p (panel) indication   single indication for a symbol screener   Fields:      name (series string)      icon (series string)      rating (series string)      value (series float)      col (series color)      tf (series string)      tooltip (series string)      normalized (series float)      init (series bool) screener   single symbol screener   Fields:      ticker (series string)      icon (series string)      rating (series string)      value (series float)      bg (series color)      fg (series color)      items (indication )      init (series bool) config   screener configuration   Fields:      strong (series float)      weak (series float)      theme (series string)      vert (series bool)      cellwidth (series float)      cellheight (series float)      textsize (series string)      font (series int)      framewidth (series int)      borders (series int)      position (series string) icons   screener Icons   Fields:      buy (series string)      sell (series string)      strong (series string) panel   screener panel object   Fields:      items (screener )      table (series table)      config (config)      theme (theme type from kaigouthro/theme_engine/1)      icons (icons)
Pine Script® library
by kaigouthro
13
Recursive Reversal Chart Patterns [Trendoscope]Caution: This algorithm is very heavy and bound to cause timeouts. If that happens, there are few settings you can change to reduce the load. (Will explain them in the description below) 🎲 Recursive Reversal Chart Patterns Indicator Welcome to another exploration of Zigzag and Pattern ecosystem components. Previously we derived Pitchfork and Recursive Zigzag indicators. This indicator is designed to scan and highlight few popular "Reversal Chart Patterns". Similar to other indicators in the ecosystem, this too is built on recursive zigzags. Double Taps Triple Taps Cup and Handles Head and Shoulders Indicator however names the patterns separately for bullish and bearish formations. So, the actual names you see on the screen are Double Top Double Bottom Triple Top Triple Bottom Cup and Handle Inverted Cup and Handle Head and Shoulders Inverse Head and Shoulders Here is a snapshot on how each category of patterns look on the chart. 🎲 Architecture Many of you may be cursing me for publishing too many libraries. But, these are all preparations for something big. Like other indicators in the Zigzag and Patterns Ecosystem, this too uses a bunch of libraries. 🎯Major direct dependencies ZigzagTypes ZigzagMethods ReversalChartPatternLibrary 🎯Indirect dependencies DrawingTypes DrawingMethods 🎯Minor dependencies Utils TradeTracker 🎲 Indicator Settings 🎯Generic and Zigzag Settings. Note: In case of timeout, please decrease the value of depth parameter 🎯Pattern Selection Having all the patterns selected on chart may also cause timeouts and will make the chart look messy. It is better to limit one or two patterns on the chart to have clear picture. 🎯Scanner Settings 🎯Indicators. These are options to use indicators as secondary confirmation to calculate divergence. If selected, the patterns are shown only if the divergence data is inline. Option also available to plugin external indicator. These calculations are not straightforward and not easy to measure by manual inspection. This feature at present is purely experimental. Have a go and let me know how you feel :)
Pine Script® indicator
by Trendoscope
47
Simple Trendlines📈 Trendlines, made easy. Simple Trendlines is a carefully made library that provides an easy and accessible way to draw trendlines on the chart. Containing only 10 properties and 2 methods, the implementation is designed to be understandable through an object-oriented structure and provides developers the opportunity to expand without having to deal with slope calculation while also ensuring that there's no leakage between the trendlines before they're drawn. Developers only need to provide 5 expressions to get everything up in running. This includes the following but is not limited to The x-axis Point A (Y1 Value) Point B (Y2 Value) A condition to draw the line A condition to keep the trendline under continuation Automatic x-axis calculation is not a built-in feature due to the inconsistency it could bring. 📕 Quick Example import HoanGhetti/SimpleTrendlines/1 as tl input_len = input.int(defval = 10) pivotLow = fixnan(ta.pivotlow(input_len, input_len)) xAxis = ta.valuewhen(ta.change(pivotLow), bar_index, 0) - ta.valuewhen(ta.change(pivotLow), bar_index, 1) prevPivot = ta.valuewhen(ta.change(pivotLow), pivotLow, 1) pivotCondition = ta.change(pivotLow) and pivotLow > prevPivot plData = tl.new(x_axis = xAxis, offset = input_len) plData.drawLine(pivotCondition, prevPivot, pivotLow) plData.drawTrendline(close > 0) plData.lines.trendline.set_style(line.style_dashed) plData.lines.trendline.set_width(2) plData.lines.startline.set_width(2) Excluding the styling at the bottom, that was only 8 lines of code which yields the following result. ⏳ Before continuing The library does not support block-scoped execution. Conditions must be declared before and integrated as a parameter. This doesn't limit any capabilities and only involves thinking logically about precedence. It was made this way for code readability and to keep things organized. The offset value inside the TrendlineSettings object can potentially affect performance (although very minimal) if you're using strict mode. When using strict mode, it loops through historical values to then do backend calculations. 🔽 Getting Started 🔽 Creating trendlines without a library isn't a hard task. However, the library features a built-in system called strict mode. We'll dive further into this below. Creating an Instance You can create an instance of the library by calling the new() function. Passing an identifier is conventionally mandatory in this case so you can reference properties and methods. import HoanGhetti/SimpleTrendlines/2 as tl lineData = tl.new(int x_axis, int offset, bool strictMode, int strictType) ___ int x_axis (Required) The distance between point A and point B provided by the user. int offset (Optional) The offset from x2 and the current bar_index. Used in situations where conditions execute ahead of where the x2 location is such as pivót events. bool strictMode (Optional) Strict mode works in the backend of things to ensure that the price hasn't closed below the trendline before the trendline is drawn. int strictType (Optional) Only accepts 0 and 1, 0 ensures that the price during slope calculation is above the line, and 1 ensures that the price during slope calculation is below the line. The Initial Line After instantiating the library, we can go ahead use the identifer we made above and create an instance of our initial line by calling the drawLine() method. lineData.drawLine(bool condition, float y1, float y2, float src) ___ bool condition (Required) The condition in order to draw a new line. float y1 (Required) The y-value of point A. float y2 (Required) The y-value of point B. float src (Optional) Determines which value strict mode will actively check for leakage before a trendline is drawn. Typically used if you're not referencing OHLC values for your y-values, or you want to check for another value to exceed the line besides using the close value. The Trendline The trendline that gets drawn solely uses the values of the initial line and can be called using the drawTrendline() method. The library enforces a condition as a parameter in order to maintain simplicity. lineData.drawTrendline(bool condition) ___ bool condition (Required) The condition in order to maintain and continue drawing the trendline. ⚙️ Features 🔹 Automatic Slope Calculation In the background, the library calculates the next Y2 and X2 values on every tick for the trendline. Preventing the developer from having to do such a process themself. 🔹 Object-Oriented Each object contains manipulative properties that allow the developer to debug and have the freedom they want. 🔹 Enforced Error Checking Runtime errors have been put in place to ensure you're doing things correctly. 🔹 Strict Mode & Offset Strict mode can only be used when the offset value is over 0. It's a feature that's only meant to function under scenarios where a condition executes further than where the X2 is relative to the current bar_index value. Let's think about pivot systems. As you're aware, pivot events are detected based on historical factors. If a swing low occurred nth bars ago, then the pivot condition will execute at the current bar_index instead of executing nth bars back. Now because of this, what if you wanted to draw a trendline when the pivot event is executed? The offset value takes care of this just as you would when developing your other scripts, basically how we always do bar_index - n. However, what does this mean for strict mode? The photo below represents the logic behind the execution. When looking at this image, imagine this just happened, the event just executed and the trendline is now drawn. Pay attention to all the values inside the surrounding box. As you can see there are some candles that closed below the trendline before the trendline was drawn. From what I can see 5-6 candles closed below the trendline during slope calculation. The goal of strict mode is to be a provisional system that prevents such occurrences from happening. Here's a photo with strict mode on. 🔹 Strict Type A parameter used in the new() function that acts as a representation of what strict mode should calculate for. It accepts only two values, 0 and 1. 0 - Ensures that all candles have closed above the trendline before the trendline is drawn. 1 - Ensures that all candles have closed below the trendline before the trendline is drawn. In the most recent photo above, I used 0 for strict type, since I was wanting to have a clean trendline and ensure that not a single candlestick closed below. If you want to reference something else besides the close value during strict mode calculation, you can change it in the drawLine() method. If it's still difficult to understand, think 0 for pivot lows, and 1 for pivot highs. 📕 Methods and Property Inheritance The library isn't crazy, but hopefully, it helps. That is all.👍
Pine Script® library
by HoanGhetti
24
Sniffer ╭━━━╮╱╱╱╱╭━╮╭━╮ ┃╭━╮┃╱╱╱╱┃╭╯┃╭╯ ┃╰━━┳━╮╭┳╯╰┳╯╰┳━━┳━╮ ╰━━╮┃╭╮╋╋╮╭┻╮╭┫┃━┫╭╯ ┃╰━╯┃┃┃┃┃┃┃╱┃┃┃┃━┫┃ ╰━━━┻╯╰┻╯╰╯╱╰╯╰━━┻╯ Overview A vast majority of modern data analysis & modelling techniques rely upon the idea of hidden patterns, wether it is some type of visualisation tool or some form of a complex machine learning algorithm, the one thing that they have in common is the belief, that patterns tell us what’s hidden behind plain numbers. The same philosophy has been adopted by many traders & investors worldwide, there’s an entire school of thought that operates purely based on chart patterns. This is where Sniffer comes in, it is a tool designed to simplify & quantify the job of pattern recognition on any given price chart, by combining various factors & techniques that generate high-quality results. This tool analyses bars selected by the user, and highlights bar clusters on the chart that exhibit similar behaviour across multiple dimensions. It can detect a single candle pattern like hammers or dojis, or it can handle multiple candles like morning/evening stars or double tops/bottoms, and many more. In fact, the tool is completely independent of such specific candle formations, instead, it works on the idea of vector similarity and generates a degree of similarity for every single combination of candles. Only the top-n matches are highlighted, users get to choose which patterns they want to analyse and to what degree, by customising the feature-space. Background In the world of trading, a common use-case is to scan a price chart for some specific candlestick formations & price structures, and then the chart is further analysed in reference to these events. Traders are often trying to answer questions like, when was the last time price showed similar behaviour, what are the instances similar to what price is doing right now, what happens when price forms a pattern like this, what were some of other indicators doing when this happened last(RSI, CCI, ADX etc), and many other abstract ideas to have a stronger confluence or to confirm a bias.Having such a context can be vital in making better informed decisions, but doing this manually on a chart that has thousands of candles can have many disadvantages. It’s tedious, human errors are rather likely, and even if it’s done with pin-point accuracy, chances are that we’ll miss out on many pieces of information. This is the thought that gave birth to Sniffer . Sniffer tries to provide a general solution for pattern-based analysis by deploying vector-similarity computation techniques, that cover the full-breadth of a price chart and generate a list of top-n matches based on the criteria selected by the user. Most of these techniques come from the data science space, where vector similarity is often implemented to solve classification & clustering problems. Sniffer uses same principles of vector comparison, and computes a degree of similarity for every single candle formation within the selected range, and as a result generates a similarity matrix that captures how similar or dissimilar a set of candles is to the input set selected by the user. How It Works A brief overview of how the tool is implemented: - Every bar is processed, and a set of features are mapped to it. - Bars selected by the user are captured, and saved for later use. - Once the all the bars have been processed, candles are back-tracked and degree of similarity is computed for every single bar(max-limit is 5000 bars). - Degree of similarity is computed by comparing attributes like price range, candle breadth & volume etc. - Similarity matrix is sorted and top-n results are highlighted on the chart through boxes of different colors. A brief overview of the features space for bars: - Range: Difference between high & low - Body: Difference between close & open - Volume: Traded volume for that candle - Head: Upper wick for green candles & lower wick for red candles - Tail: Lower wick for green candles & upper wick for red candles - BTR: Body to Range ratio - HTR: Head to Range ratio - TTR: Tail to Range ratio - HTB: Head to Body ratio - TTB: Tail to Body ratio - ROC: Rate of change for HL2 for four different periods - RSI: Relative Strength Index - CCI: Commodity Channel Index - Stochastic: Stochastic Index - ADX: DMI+, DMI- & ADX A brief overview of how degree of similarity is calculated: - Each bar set is compared to the inout bar set within the selected feature space - Features are represented as vectors, and distance between the vectors is calculated - Shorter the distance, greater the similarity - Different distance calculation methods are available to choose from, such as Cosine, Euclidean, Lorentzian, Manhattan, & Pearson - Each method is likely to generate slightly different results, users are expected to select the method & the feature space that best fits their use-case How To Use It - Usage of this tool is relatively straightforward, users can add this indicator to their chart and similar clusters will be highlighted automatically - Users need to select a time range that will be treated as input, and bars within that range become the input formation for similarity calculations - Boxes will be draw around the clusters that fit the matching criteria - Boxes are color-coded, green color boxes represent the top one-third of the top-n matches, yellow boxes represent the middle third, red boxes are for bottom third, and white box represents user-input - Boxes colors will be adjusted as you adjust input parameters, such as number of matches or look-back period User Settings Users can configure the following options: - Select the time-range to set input bars - Select the look-back period, number of candles to backtrack for similarity search - Select the number of top-n matches to show on the chart - Select the method for similarity calculation - Adjust the feature space, this enables addition of custom features, such as pattern recognition, technical indicators, rate of change etc - Toggle verbosity, shows degree of similarity as a percentage value inside the box Top Features - Pattern Agnostic: Designed to work with variable number of candles & complex patterns - Customisable Feature Space: Users get to add custom features to each bar - Comprehensive Comparison: Generates a degree of similarity for all possible combinations Final Note - Similarity matches will be shown only within last 4500 bars. - In theory, it is possible to compute similarity for any size candle formations, indicator has been tested with formations of 50+ candles, but it is recommended to select smaller range for faster & cleaner results. - As you move to smaller time frames, selected time range will provide a larger number of candles as input, which can produce undesired results, it is advised to adjust your selection when you change time frames. Seeking suggestions on how to directly receive bars as user input, instead of time range. - At times, users may see array index out of bound error when setting up this indicator, this generally happens when the input range is not properly configured. So, it should disappear after you select the input range, still trying to figure out where it is coming from, suggestions are welcome. Credits - @HeWhoMustNotBeNamed for publishing such a handy PineScript Logger, it certainly made the job a lot easier.
Pine Script® indicator
by sudoMode
47
ATR OSC and Volume Screener (ATROSCVS)In today's world of trading, having the right tools and indicators can make all the difference. With the vast number of cryptocurrencies available, I've found it challenging to keep track of the market's overall direction and make informed decisions. That's where the ATR OSC and Volume Screener comes in, a powerful Pine Script that I use to identify potential trading opportunities across multiple cryptocurrencies, all in one convenient place. This script combines two essential components: the ATR Oscillator (ATR OSC) and a Volume Screener. It is designed to work with the TradingView platform. Let me explain how this script works and how it benefits my trading. Firstly, the ATR Oscillator is an RSI-like oscillator that performs better under longer lookback periods. Unlike traditional RSI, the ATR OSC doesn't lose its min and max ranges with a long lookback period, as the scale remains intact. It calculates the true range by considering the high, low, open, and close prices of a financial instrument, and uses this true range instead of the standard deviation in a modified z-score calculation. This unique approach helps provide a more precise assessment of the market's volatility. The Volume Screener, on the other hand, helps me identify unusual trading volumes across various cryptocurrencies. It employs a normalized volume calculation method, effectively filtering out outliers and highlighting potentially significant trading opportunities. One feature I find particularly impressive about the ATR OSC and Volume Screener is its versatility and the way it displays information using color gradients. With support for over 30 different cryptocurrencies, including popular options like Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Dogecoin (DOGE), I can monitor a wide range of markets simultaneously. The color gradient on the grid is visually appealing and makes it easy to identify the strength of the indicators for each cryptocurrency, allowing me to make quick comparisons and spot potential trading opportunities. The customizable input options allow me to fine-tune the script to suit my individual trading preferences and strategies. In summary, the ATR OSC and Volume Screener has been an invaluable tool for me as I navigate the ever-evolving world of cryptocurrencies. By combining the power of the ATR Oscillator with a robust Volume Screener, this Pine Script makes it easier than ever to identify promising trading opportunities and stay ahead of the game. The color gradient in the ATR OSC and Volume Screener is essential for visually representing the data on the heatmap. It uses a range of colors to indicate the strength of the indicators for each cryptocurrency, making it easier to understand the market dynamics at a glance. In the heatmap, the color gradient typically starts from a cooler color, such as blue or green, at the lower extremes (low ATR OSC values) and progresses towards warmer colors, like yellow, orange, or red, as the ATR OSC values approach the upper extremes (high ATR OSC values). This color-coding system enables me to quickly identify and interpret the data without having to examine individual numerical values. For example, cooler colors (blue or green) might represent lower values of the ATR Oscillator, suggesting oversold conditions in the respective cryptocurrencies. On the other hand, warmer colors (yellow, orange, or red) indicate higher ATR OSC values, signaling overbought market conditions. This visual representation allows me to make rapid comparisons between different cryptocurrencies and spot potential trading opportunities more efficiently. By utilizing the color gradient in the heatmap, the ATR OSC and Volume Screener simplifies the analysis of multiple cryptocurrencies, helping me to quickly identify market trends and make better-informed trading decisions. I highly recommend testing the ATR OSC and Volume Screener and seeing the difference it can make in your trading decisions. Happy trading!
Pine Script® indicator
by The_Peaceful_Lizard
26
DataChartLibrary "DataChart" Library to plot scatterplot or heatmaps for your own set of data samples draw(this)   draw contents of the chart object   Parameters:      this : Chart object   Returns: current chart object init(this)   Initialize Chart object.   Parameters:      this : Chart object to be initialized   Returns: current chart object addSample(this, sample, trigger)   Add sample data to chart using Sample object   Parameters:      this : Chart object      sample : Sample object containing sample x and y values to be plotted      trigger : Samples are added to chart only if trigger is set to true. Default value is true   Returns: current chart object addSample(this, x, y, trigger)   Add sample data to chart using x and y values   Parameters:      this : Chart object      x : x value of sample data      y : y value of sample data      trigger : Samples are added to chart only if trigger is set to true. Default value is true   Returns: current chart object addPriceSample(this, priceSampleData, config)   Add price sample data - special type of sample designed to measure price displacements of events   Parameters:      this : Chart object      priceSampleData : PriceSampleData object containing event driven displacement data of x and y      config : PriceSampleConfig object containing configurations for deriving x and y from priceSampleData   Returns: current chart object Sample   Sample data for chart   Fields:      xValue : x value of the sample data      yValue : y value of the sample data ChartProperties   Properties of plotting chart   Fields:      title : Title of the chart      suffix : Suffix for values. It can be used to reference 10X or 4% etc. Used only if format is not format.percent      matrixSize : size of the matrix used for plotting      chartType : Can be either scatterplot or heatmap. Default is scatterplot      outliersStart : Indicates the percentile of data to filter out from the starting point to get rid of outliers      outliersEnd : Indicates the percentile of data to filter out from the ending point to get rid of outliers.      backgroundColor      plotColor : color of plots on the chart. Default is color.yellow. Only used for scatterplot type      heatmapColor : color of heatmaps on the chart. Default is color.red. Only used for heatmap type      borderColor : border color of the chart table. Default is color.yellow.      plotSize : size of scatter plots. Default is size.large      format : data representation format in tooltips. Use mintick.percent if measuring any data in terms of percent. Else, use format.mintick      showCounters : display counters which shows totals on each quadrants. These are single cell tables at the corners displaying number of occurences on each quadrant.      showTitle : display title at the top center. Uses the title string set in the properties      counterBackground : background color of counter table cells. Default is color.teal      counterTextColor : text color of counter table cells. Default is color.white      counterTextSize : size of counter table cells. Default is size.large      titleBackground : background color of chart title. Default is color.maroon      titleTextColor : text color of the chart title. Default is color.white      titleTextSize : text size of the title cell. Default is size.large      addOutliersToBorder : If set, instead of removing the outliers, it will be added to the border cells.      useCommonScale : Use common scale for both x and y. If not selected, different scales are calculated based on range of x and y values from samples. Default is set to false.      plotchar : scatter plot character. Default is set to ascii bullet. ChartDrawing   Chart drawing objects collection   Fields:      properties : ChartProperties object which determines the type and characteristics of chart being plotted      titleTable : table containing title of the chart.      mainTable : table containing plots or heatmaps.      quadrantTables : Array of tables containing counters of all 4 quandrants Chart   Chart type which contains all the information of chart being plotted   Fields:      properties : ChartProperties object which determines the type and characteristics of chart being plotted      samples : Array of Sample objects collected over period of time for plotting on chart.      displacements : Array containing displacement values. Both x and y values      displacementX : Array containing only X displacement values.      displacementY : Array containing only Y displacement values.      drawing : ChartDrawing object which contains all the drawing elements PriceSampleConfig   Configs used for adding specific type of samples called PriceSamples   Fields:      duration : impact duration for which price displacement samples are calculated.      useAtrReference : Default is true. If set to true, price is measured in terms of Atr. Else is measured in terms of percentage of price.      atrLength : atrLength to be used for measuring the price based on ATR. Used only if useAtrReference is set to true. PriceSampleData   Special type of sample called price sample. Can be used instead of basic Sample type   Fields:      trigger : consider sample only if trigger is set to true. Default is true.      source : Price source. Default is close      highSource : High price source. Default is high      lowSource : Low price source. Default is low      tr : True range value. Default is ta.tr
Pine Script® library
by Trendoscope
20