NOMMO AUTOMATE🖖 Hi all!
Check out my NOMMO AUTOMATE indicator for trend detection, trend change points, hedging opposite trend impulses.
What the script do:
☑️ Detecting local and global trends and trend change points, detecting opposite to current trend impulses.
How the script do it:
☑️ The indicator compares RSI indicators on chosen by user Trend TF1 and Trend TF2 and marks trend change points.
☑️ The indicator compares different length HMA indicators on chosen by user Hedge TF to detect opposite to current trend impulses.
How to use it:
☑️ There are 4 states in the indicator: Long, Short, Flat, Hedge, marked by corresponding (adjustable) color zones, where Long = uptrend, Short = downtrend, Flat = sideways movement, Hedge = possible impulse in the opposite trend direction.
☑️ Select Trend TF1 and Trend TF2 and RSI length to determine the trend, depending on how a big picture you want to see, the more major TF you choose the more global picture of the trend change you get.
☑️ Select Hedge TF to determine the possible impulses opposite to the current trend (does not work in detected Flat movement).
☑️ For each trading pair you need to try individual settings, the default settings I use for BTC swing trading, to reduce the noise level of hedging put Hedge TF the same as the smaller Trend TF.
☑️ Try different settings, experiment and you will find the most suitable settings for your trading pair.
How magic works:
☑️ RSI Trend TF1 > 50 + RSI Trend TF2 > 50 = Long
☑️ RSI Trend TF1 > 50 + RSI Trend TF2 < 50 = Flat
☑️ RSI Trend TF1 < 50 + RSI Trend TF2 > 50 = Flat
☑️ RSI Trend TF1 < 50 + RSI Trend TF2 < 50 = Short
☑️ Long + Hedge TF (HMA 10 < HMA 70 < HMA 200) = Hedge
☑️ Short + Hedge TF (HMA 10 > HMA 70 > HMA 200) = Hedge
For example:
☑️ Try Trend TF1 = 1D, Trend TF2 = 1D and Hedge TF = 1D, with RSI period = 21, to check mid-term trend on BTCUSD
May the trade force be with you.
Cari dalam skrip untuk "大位科技同行业可替代股票的技术面分析数据（如5日均线、10日均线、支撑位、压力位）"
Rounded Forex Levels: Big-Figure, Mid-Figure, 80-20 levels, BFRNSimple indicator to show Rounded levels in typical Forex pairs: Big figure, Mid-figure, 80-20 Insitutional Levels, 10pip levels, 5pip levels
Wrote this indicator because other ones out there seem to clutter the chart. This is simple, low-clutter and can be set by user to have arbitrary start and end points for the lines.
I wanted the ability for lines to plot discreetly to the right hand side of price as in the chart above, since in my opinion, these are only of secondary consideration to a trader, an extra confirmation/confluence to an existing idea.
//Purpose & Usage:
-Big-figure levels (100pips) & Mid-figure levels (50pips) will have a senstitivity to price, that can be an additional tool when looking for confluence for a target or an entry.
-As well as BF levels and MF levels; Institutional levels (20pips below and 20 pips above a Big Figure level) and standard 10pip or 5pip levels, can be useful in the right context (i.e added confirmation that of a minor sweep target; added conviction in an idea if the level aligns with another seperately derived level).
//User inputs:
-Toggle on/off each of the types of level.
-Line spacing: choose pip spacing of lines .
-Number of lines above/below (i.e. setting of 5 will be 11 lines. One central, 5 above, 5 below).
-Formatting: for each class of lines, code by color, style and width (as per the example chart below).
-Line start offset and line end offset: bars forward/back for each of start point and end point. So lines could be spread right across chart; or neatly pushed to the far right or left of the chart.
//Notes:
-Designed for typical Forex pairs with units close to 1.0 (like Eur/Usd, Usd/Cad, Aud/Usd, Gbp/Usd, Eur/Gbp, Nzd/Usd).
-Lines are based from the rounded close of the previous bar, Updating (if applicable) on each new bar.
Demo Plotting levels in the past; Dialog box example display:
Acceleration-Based MA Slope PredictionHello traders,
I developed this indicator while working on a trading strategy using moving average slope and acceleration, and I found the concept interesting enough to share it.
Let me briefly explain this indicator.
----About White Plot----
1. Calculate the first derivative approximation at the current point of the Moving Average, and then calculate the second derivative approximation to obtain the 'Acceleration'.
2. Where the acceleration is 0, it signifies a change in the force of the moving average.
3. Therefore, by drawing a parabola based on the acceleration at that time, can depict the parabolic shape of the moving average.
This is represented as a white circle on the indicator.
4. These circles are reset at the next point where the acceleration is 0, indicating a change in the parabolic force.
If the moving average rises more sharply than the predicted value of the rising parabola, a more drastic increase is expected.
5. In this case, you can start risk management around the time the drawn parabola breaks.
(The actual MA is represented by green/red lines)
6. Before the trend changes, i.e., before the direction of the moving average changes, there is a section where the acceleration is 0, and this is represented on the chart as follows.
(The lower indicator shows the acceleration of the corresponding parabola)
----About Red Plot----
1. Calculate the first derivative approximation of the moving average value, the 'slope'.
2. Where the slope is 0, it represents the extreme point of the parabola.
3. Therefore, by using the acceleration at that point as the coefficient of the quadratic function and setting the extreme point as a vertex, we can draw a quadratic function. This is represented as a red circle on the indicator.
(Keep in mind that the actual moving average is not a quadratic function; this is a "forced" quadratic function assuming the parabola is maintained)
4. These circles are reset at the next extreme point where the slope is 0, and a new quadratic function is created.
Based on the formula obtained in the above process, you can predict the future moving average through 'offset'.
5. That is, if the x value at the current point is 'k', you can predict the moving average one candle ahead by substituting (k+1) into the quadratic function.
The predicted value at the past position is shown as a red circle.
6. The smoother the chosen moving average, the fewer extreme points will appear, and the higher the likelihood of the parabola fitting.
For the T3 set as the default value, it shows very high accuracy even when predicting about 20 candles ahead.
On the other hand, rough moving averages like SMA have limited prediction value.
(SMA 60, offset = 10)
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The moving average with a very high level of accuracy is JMA (Jurik Moving Average). However, since the code for this moving average is not public, I recommend those interested to check it through my code.
Additionally, I believe the code of this indicator I've uploaded has significant utility.
As an example, you can use the breaking point of the parabola predicted by the acceleration to determine when the force changes again for entries/losses. There are many other possible applications as well.
I look forward to seeing more excellent results from this indicator.
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안녕하세요 트레이더여러분.
이 지표는, 제가 이동평균선의 기울기와 가속도를 이용하여 매매를 하기 위한 지표를 개발하다가, 흥미로운 내용이라고 판단하여 만들게 되었습니다.
이 지표에 대해 간단히 설명드리겠습니다.
----하얀색 플롯에 대해----
1. 이동평균선이 진행되는 현재 시점에서 미분의 근사값을 구하고, 다시 한 번 미분의 근사값을 구해서 '가속도'를 얻습니다.
2. 가속도가 0이 되는 곳은, 곧 해당 이동평균선의 힘이 바뀌는 곳을 의미합니다.
3. 따라서, 그 당시 시점 기준으로 포물선을 그려낸다면, 가속도를 이용하여 해당 이동평균선의 포물선을 그려낼 수 있습니다. 이것은 지표의 하얀색점로서 표기됩니다.
4. 이 때, 이러한 점들은 다음의 가속도가 0이 되는 지점, 즉 포물선의 힘이 바뀌는 곳에서 다시 초기화됩니다.
5. 올라가고 있던 포물선에서의 예측치보다 이동평균선이 더 급하게 올라간다면, 더욱 급격한 상승이 예상됩니다. 이 경우, 그려지고있는 포물선이 깨질 때쯤부터 리스크 관리를 시작할 수 있습니다.
(녹색/빨간색의 선으로 실제 MA를 표현했습니다. 거슬리시면 '모습'가셔서 끄셔도 좋습니다. )
6. 추세가 변경되기 전, 즉 이동평균선의 방향이 바뀌기 전에는 가속도가 0이 되는 구간이 존재하고, 그것이 차트 위에 다음과같이 표현됩니다.
(하단의 지표는, 해당 포물선의 가속도을 나타냅니다)
----붉은색 플롯에 대해----
1. 이동평균선 값을 미분 근사값 즉, '기울기'를 구합니다.
2. 기울기가 0이 되는 곳은, 포물선이 극점이 되는 곳을 뜻합니다.
3. 따라서, 해당 시점의 가속도를 2차함수의 계수로 하여, 또한 해당 극점을 하나의 꼭지점으로 설정하여,이차함수를 그려낼 수 있습니다. 이것은 지표의 빨간색점으로서 표현됩니다.
(실제 이동평균선은 2차함수가 아니기에, 포물선이 유지된다는 가정 하에 "억지로"만들어낸 이차함수입니다)
4. 이 때, 이러한 점은 다음 극점이 0이 되는 곳에서 초기화되고 이차함수가 만들어집니다.
5. 위의 과정에서 얻은 식을 바탕으로 'offset'을 통해 미래의 이동평균선을 예측할 수 있습니다.
즉, 현재시점의 x값을 'k'라고 한다면, (k+1)을 이차함수에 대입하여 1캔들 앞의 이동평균선을 예측할 수 있습니다.
해당 예측치가 지나간 자리는, 빨간색점을 통해 보여집니다.
6. 선택한 이동평균선이 스무스할수록 극점은 덜 등장하게되고, 포물선의 위치가 맞아들어갈 가능성이 높습니다.
현재 디폴트값으로 설정된 T3의 경우, 약 20캔들 앞을 예측해도 매우 높은 정확도를 보여줍니다.
반면에, SMA와 같이 울퉁불퉁한 이동평균선은 가능한 예측치가 크지 않습니다.
(SMA 60, offset=10)
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매우 높은 수준의 정확도를 보여준 이동평균선은 JMA(Jurik Moving Average)입니다. 다만 이 이동평균선은 코드가 공개되지 않았기때문에, 관심있으신 분은 저의 코드를 통해 한번 확인해보시길 권장드립니다.
추가로, 제가 올린 이 지표의 코드는 이용가치가 높다고 생각합니다.
하나의 예시로서, 가속도로 예측한 포물선이 깨지는 곳을 기준으로, 힘이 다시 한 번 바뀌는 것을 이용해 진입/로스를 할 수 있습니다. 그 외에도 매우 다양한 활용이 가능합니다.
이 지표를 통해 더욱 좋은 새로운 결과물이 나오길 기대해봅니다.
Adaptive Candlestick Pattern Recognition System█ INTRODUCTION
Nearly three years in the making, intermittently worked on in the few spare hours of weekends and time off, this is a passion project I undertook to flesh out my skills as a computer programmer. This script currently recognizes 85 different candlestick patterns ranging from one to five candles in length. It also performs statistical analysis on those patterns to determine prior performance and changes the coloration of those patterns based on that performance. In searching TradingView's script library for scripts similar to this one, I had found a handful. However, when I reviewed the ones which were open source, I did not see many that truly captured the power of PineScrypt or leveraged the way it works to create efficient and reliable code; one of the main driving factors for releasing this 5,000+ line behemoth open sourced.
Please take the time to review this description and source code to utilize this script to its fullest potential.
█ CONCEPTS
This script covers the following topics: Candlestick Theory, Trend Direction, Higher Timeframes, Price Analysis, Statistic Analysis, and Code Design.
Candlestick Theory - This script focuses solely on the concept of Candlestick Theory: arrangements of candlesticks may form certain patterns that can potentially influence the future price action of assets which experience those patterns. A full list of patterns (grouped by pattern length) will be in its own section of this description. This script contains two modes of operation for identifying candlestick patterns, 'CLASSIC' and 'BREAKOUT'.
CLASSIC: In this mode, candlestick patterns will be identified whenever they appear. The user has a wide variety of inputs to manipulate that can change how certain patterns are identified and even enable alerts to notify themselves when these patterns appear. Each pattern selected to appear will have their Profit or Loss (P/L) calculated starting from the first candle open succeeding the pattern to a candle close specified some number of candles ahead. These P/L calculations are then collected for each pattern, and split among partitions of prior price action of the asset the script is currently applied to (more on that in Higher Timeframes ).
BREAKOUT: In this mode, P/L calculations are held off until a breakout direction has been confirmed. The user may specify the number of candles ahead of a pattern's appearance (from one to five) that a pattern has to confirm a breakout in either an upward or downward direction. A breakout is constituted when there is a candle following the appearance of the pattern that closes above/at the highest high of the pattern, or below/at its lowest low. Only then will percent return calculations be performed for the pattern that's been identified, and these percent returns are broken up not only by the partition they had appeared in but also by the breakout direction itself. Patterns which do not breakout in either direction will be ignored, along with having their labels deleted.
In both of these modes, patterns may be overridden. Overrides occur when a smaller pattern has been detected and ends up becoming one (or more) of the candles of a larger pattern. A key example of this would be the Bearish Engulfing and the Three Outside Down patterns. A Three Outside Down necessitates a Bearish Engulfing as the first two candles in it, while the third candle closes lower. When a pattern is overridden, the return for that pattern will no longer be tracked. Overrides will not occur if the tail end of a larger pattern occurs at the beginning of a smaller pattern (Ex: a Bullish Engulfing occurs on the third candle of a Three Outside Down and the candle immediately following that pattern, the Three Outside Down pattern will not be overridden).
Important Functionality Note: These patterns are only searched for at the most recently closed candle, not on the currently closing candle, which creates an offset of one for this script's execution. (SEE LIMITATIONS)
Trend Direction - Many of the patterns require a trend direction prior to their appearance. Noting TradingView's own publication of candlestick patterns, I utilize a similar method for determining trend direction. Moving Averages are used to determine which trend is currently taking place for candlestick patterns to be sought out. The user has access to two Moving Averages which they may individually modify the following for each: Moving Average type (list of 9), their length, width, source values, and all variables associated with two special Moving Averages (Least Squares and Arnaud Legoux).
There are 3 settings for these Moving Averages, the first two switch between the two Moving Averages, and the third uses both. When using individual Moving Averages, the user may select a 'price point' to compare against the Moving Average (default is close). This price point is compared to the Moving Average at the candles prior to the appearance of candle patterns. Meaning: The close compared to the Moving Average two candles behind determines the trend direction used for Candlestick Analysis of one candle patterns; three candles behind for two candle patterns and so on. If the selected price point is above the Moving Average, then the current trend is an 'uptrend', 'downtrend' otherwise.
The third setting using both Moving Averages will compare the lengths of each, and trend direction is determined by the shorter Moving Average compared to the longer one. If the shorter Moving Average is above the longer, then the current trend is an 'uptrend', 'downtrend' otherwise. If the lengths of the Moving Averages are the same, or both Moving Averages are Symmetrical, then MA1 will be used by default. (SEE LIMITATIONS)
Higher Timeframes - This script employs the use of Higher Timeframes with a few request.security calls. The purpose of these calls is strictly for the partitioning of an asset's chart, splitting the returns of patterns into three separate groups. The four inputs in control of this partitioning split the chart based on: A given resolution to grab values from, the length of time in that resolution, and 'Upper' and 'Lower Limits' which split the trading range provided by that length of time in that resolution that forms three separate groups. The default values for these four inputs will partition the current chart by the yearly high-low range where: the 'Upper' partition is the top 20% of that trading range, the 'Middle' partition is 80% to 33% of the trading range, and the 'Lower' partition covers the trading range within 33% of the yearly low.
Patterns which are identified by this script will have their returns grouped together based on which partition they had appeared in. For example, a Bullish Engulfing which occurs within a third of the yearly low will have its return placed separately from a Bullish Engulfing that occurred within 20% of the yearly high. The idea is that certain patterns may perform better or worse depending on when they had occurred during an asset's trading range.
Price Analysis - Price Analysis is a major part of this script's functionality as it can fundamentally change how patterns are shown to the user. The settings related to Price Analysis include setting the number of candles ahead of a pattern's appearance to determine the return of that pattern. In 'BREAKOUT' mode, an additional setting allows the user to specify where the P/L calculation will begin for a pattern that had appeared and confirmed. (SEE LIMITATIONS)
The calculation for percent returns of patterns is illustrated with the following pseudo-code (CLASSIC mode, this is a simplified version of the actual code):
type patternObj
int ID
int partition
type returnsArray
float returns
// No pattern found = na returned
patternObj TEST_VAL = f_FindPattern()
priorTestVal = TEST_VAL
if not na( priorTestVal )
pnlMatrixRow = priorTestVal.ID
pnlMatrixCol = priorTestVal.partition
matrixReturn = matrix.get(PERCENT_RETURNS, pnlMatrixRow, pnlMatrixCol)
percentReturn = ( (close - open ) / open ) * 100%
array.push(matrixReturn.returns, percentReturn)
Statistic Analysis - This script uses Pine's built-in array functions to conduct the Statistic Analysis for patterns. When a pattern is found and its P/L calculation is complete, its return is added to a 'Return Array' User-Defined-Type that contains numerous fields which retain information on a pattern's prior performance. The actual UDT is as follows:
type returnArray
float returns = na
int size = 0
float avg = 0
float median = 0
float stdDev = 0
int polarities = na
All values within this UDT will be updated when a return is added to it (some based on user input). The array.avg , array.median and array.stdev will be ran and saved into their respective fields after a return is placed in the 'returns' array. The 'polarities' integer array is what will be changed based on user input. The user specifies two different percentages that declare 'Positive' and 'Negative' returns for patterns. When a pattern returns above, below, or in between these two values, different indices of this array will be incremented to reflect the kind of return that pattern had just experienced.
These values (plus the full name, partition the pattern occurred in, and a 95% confidence interval of expected returns) will be displayed to the user on the tooltip of the labels that identify patterns. Simply scroll over the pattern label to view each of these values.
Code Design - Overall this script is as much of an art piece as it is functional. Its design features numerous depictions of ASCII Art that illustrate what is being attempted by the functions that identify patterns, and an incalculable amount of time was spent rewriting portions of code to improve its efficiency. Admittedly, this final version is nearly 1,000 lines shorter than a previous version (one which took nearly 30 seconds after compilation to run, and didn't do nearly half of what this version does). The use of UDTs, especially the 'patternObj' one crafted and redesigned from the Hikkake Hunter 2.0 I published last month, played a significant role in making this script run efficiently. There is a slight rigidity in some of this code mainly around pattern IDs which are responsible for displaying the abbreviation for patterns (as well as the full names under the tooltips, and the matrix row position for holding returns), as each is hard-coded to correspond to that pattern.
However, one thing I would like to mention is the extensive use of global variables for pattern detection. Many scripts I had looked over for ideas on how to identify candlestick patterns had the same idea; break the pattern into a set of logical 'true/false' statements derived from historically referencing candle OHLC values. Some scripts which identified upwards of 20 to 30 patterns would reference Pine's built-in OHLC values for each pattern individually, potentially requesting information from TradingView's servers numerous times that could easily be saved into a variable for re-use and only requested once per candle (what this script does).
█ FEATURES
This script features a massive amount of switches, options, floating point values, detection settings, and methods for identifying/tailoring pattern appearances. All modifiable inputs for patterns are grouped together based on the number of candles they contain. Other inputs (like those for statistics settings and coloration) are grouped separately and presented in a way I believe makes the most sense.
Not mentioned above is the coloration settings. One of the aims of this script was to make patterns visually signify their behavior to the user when they are identified. Each pattern has its own collection of returns which are analyzed and compared to the inputs of the user. The user may choose the colors for bullish, neutral, and bearish patterns. They may also choose the minimum number of patterns needed to occur before assigning a color to that pattern based on its behavior; a color for patterns that have not met this minimum number of occurrences yet, and a color for patterns that are still processing in BREAKOUT mode.
There are also an additional three settings which alter the color scheme for patterns: Statistic Point-of-Reference, Adaptive coloring, and Hard Limiting. The Statistic Point-of-Reference decides which value (average or median) will be compared against the 'Negative' and 'Positive Return Tolerance'(s) to guide the coloration of the patterns (or for Adaptive Coloring, the generation of a color gradient).
Adaptive Coloring will have this script produce a gradient that patterns will be colored along. The more bullish or bearish a pattern is, the further along the gradient those patterns will be colored starting from the 'Neutral' color (hard lined at the value of 0%: values above this will be colored bullish, bearish otherwise). When Adaptive Coloring is enabled, this script will request the highest and lowest values (these being the Statistic Point-of-Reference) from the matrix containing all returns and rewrite global variables tied to the negative and positive return tolerances. This means that all patterns identified will be compared with each other to determine bullish/bearishness in Adaptive Coloring.
Hard Limiting will prevent these global variables from being rewritten, so patterns whose Statistic Point-of-Reference exceed the return tolerances will be fully colored the bullish or bearish colors instead of a generated gradient color. (SEE LIMITATIONS)
Apart from the Candle Detection Modes (CLASSIC and BREAKOUT), there's an additional two inputs which modify how this script behaves grouped under a "MASTER DETECTION SETTINGS" tab. These two "Pattern Detection Settings" are 'SWITCHBOARD' and 'TARGET MODE'.
SWITCHBOARD: Every single pattern has a switch that is associated with its detection. When a switch is enabled, the code which searches for that pattern will be run. With the Pattern Detection Setting set to this, all patterns that have their switches enabled will be sought out and shown.
TARGET MODE: There is an additional setting which operates on top of 'SWITCHBOARD' that singles out an individual pattern the user specifies through a drop down list. The names of every pattern recognized by this script will be present along with an identifier that shows the number of candles in that pattern (Ex: " (# candles)"). All patterns enabled in the switchboard will still have their returns measured, but only the pattern selected from the "Target Pattern" list will be shown. (SEE LIMITATIONS)
The vast majority of other features are held in the one, two, and three candle pattern sections.
For one-candle patterns, there are:
3 — Settings related to defining 'Tall' candles:
The number of candles to sample for previous candle-size averages.
The type of comparison done for 'Tall' Candles: Settings are 'RANGE' and 'BODY'.
The 'Tolerance' for tall candles, specifying what percent of the 'average' size candles must exceed to be considered 'Tall'.
When 'Tall Candle Setting' is set to RANGE, the high-low ranges are what the current candle range will be compared against to determine if a candle is 'Tall'. Otherwise the candle bodies (absolute value of the close - open) will be compared instead. (SEE LIMITATIONS)
Hammer Tolerance - How large a 'discarded wick' may be before it disqualifies a candle from being a 'Hammer'.
Discarded wicks are compared to the size of the Hammer's candle body and are dependent upon the body's center position. Hammer bodies closer to the high of the candle will have the upper wick used as its 'discarded wick', otherwise the lower wick is used.
9 — Doji Settings, some pulled from an old Doji Hunter I made a while back:
Doji Tolerance - How large the body of a candle may be compared to the range to be considered a 'Doji'.
Ignore N/S Dojis - Turns off Trend Direction for non-special Dojis.
GS/DF Doji Settings - 2 Inputs that enable and specify how large wicks that typically disqualify Dojis from being 'Gravestone' or 'Dragonfly' Dojis may be.
4 Settings related to 'Long Wick Doji' candles detailed below.
A Tolerance for 'Rickshaw Man' Dojis specifying how close the center of the body must be to the range to be valid.
The 4 settings the user may modify for 'Long Legged' Dojis are: A Sample Base for determining the previous average of wicks, a Sample Length specifying how far back to look for these averages, a Behavior Setting to define how 'Long Legged' Dojis are recognized, and a tolerance to specify how large in comparison to the prior wicks a Doji's wicks must be to be considered 'Long Legged'.
The 'Sample Base' list has two settings:
RANGE: The wicks of prior candles are compared to their candle ranges and the 'wick averages' will be what the average percent of ranges were in the sample.
WICKS: The size of the wicks themselves are averaged and returned for comparing against the current wicks of a Doji.
The 'Behavior' list has three settings:
ONE: Only one wick length needs to exceed the average by the tolerance for a Doji to be considered 'Long Legged'.
BOTH: Both wick lengths need to exceed the average of the tolerance of their respective wicks (upper wicks are compared to upper wicks, lower wicks compared to lower) to be considered 'Long Legged'.
AVG: Both wicks and the averages of the previous wicks are added together, divided by two, and compared. If the 'average' of the current wicks exceeds this combined average of prior wicks by the tolerance, then this would constitute a valid 'Long Legged' Doji. (For Dojis in general - SEE LIMITATIONS)
The final input is one related to candle patterns which require a Marubozu candle in them. The two settings for this input are 'INCLUSIVE' and 'EXCLUSIVE'. If INCLUSIVE is selected, any opening/closing variant of Marubozu candles will be allowed in the patterns that require them.
For two-candle patterns, there are:
2 — Settings which define 'Engulfing' parameters:
Engulfing Setting - Two options, RANGE or BODY which sets up how one candle may 'engulf' the previous.
Inclusive Engulfing - Boolean which enables if 'engulfing' candles can be equal to the values needed to 'engulf' the prior candle.
For the 'Engulfing Setting':
RANGE: If the second candle's high-low range completely covers the high-low range of the prior candle, this is recognized as 'engulfing'.
BODY: If the second candle's open-close completely covers the open-close of the previous candle, this is recognized as 'engulfing'. (SEE LIMITATIONS)
4 — Booleans specifying different settings for a few patterns:
One which allows for 'opens within body' patterns to let the second candle's open/close values match the prior candles' open/close.
One which forces 'Kicking' patterns to have a gap if the Marubozu setting is set to 'INCLUSIVE'.
And Two which dictate if the individual candles in 'Stomach' patterns need to be 'Tall'.
8 — Floating point values which affect 11 different patterns:
One which determines the distance the close of the first candle in a 'Hammer Inverted' pattern must be to the low to be considered valid.
One which affects how close the opens/closes need to be for all 'Lines' patterns (Bull/Bear Meeting/Separating Lines).
One that allows some leeway with the 'Matching Low' pattern (gives a small range the second candle close may be within instead of needing to match the previous close).
Three tolerances for On Neck/In Neck patterns (2 and 1 respectively).
A tolerance for the Thrusting pattern which give a range the close the second candle may be between the midpoint and close of the first to be considered 'valid'.
A tolerance for the two Tweezers patterns that specifies how close the highs and lows of the patterns need to be to each other to be 'valid'.
The first On Neck tolerance specifies how large the lower wick of the first candle may be (as a % of that candle's range) before the pattern is invalidated. The second tolerance specifies how far up the lower wick to the close the second candle's close may be for this pattern. The third tolerance for the In Neck pattern determines how far into the body of the first candle the second may close to be 'valid'.
For the remaining patterns (3, 4, and 5 candles), there are:
3 — Settings for the Deliberation pattern:
A boolean which forces the open of the third candle to gap above the close of the second.
A tolerance which changes the proximity of the third candle's open to the second candle's close in this pattern.
A tolerance that sets the maximum size the third candle may be compared to the average of the first two candles.
One boolean value for the Two Crows patterns (standard and Upside Gapping) that forces the first two candles in the patterns to completely gap if disabled (candle 1's close < candle 2's low).
10 — Floating point values for the remaining patterns:
One tolerance for defining how much the size of each candle in the Identical Black Crows pattern may deviate from the average of themselves to be considered valid.
One tolerance for setting how close the opens/closes of certain three candle patterns may be to each other's opens/closes.*
Three floating point values that affect the Three Stars in the South pattern.
One tolerance for the Side-by-Side patterns - looks at the second and third candle closes.
One tolerance for the Stick Sandwich pattern - looks at the first and third candle closes.
A floating value that sizes the Concealing Baby Swallow pattern's 3rd candle wick.
Two values for the Ladder Bottom pattern which define a range that the third candle's wick size may be.
* This affects the Three Black Crows (non-identical) and Three White Soldiers patterns, each require the opens and closes of every candle to be near each other.
The first tolerance of the Three Stars in the South pattern affects the first candle body's center position, and defines where it must be above to be considered valid. The second tolerance specifies how close the second candle must be to this same position, as well as the deviation the ratio the candle body to its range may be in comparison to the first candle. The third restricts how large the second candle range may be in comparison to the first (prevents this pattern from being recognized if the second candle is similar to the first but larger).
The last two floating point values define upper and lower limits to the wick size of a Ladder Bottom's fourth candle to be considered valid.
█ HOW TO USE
While there are many moving parts to this script, I attempted to set the default values with what I believed may help identify the most patterns within reasonable definitions. When this script is applied to a chart, the Candle Detection Mode (along with the BREAKOUT settings) and all candle switches must be confirmed before patterns are displayed. All switches are on by default, so this gives the user an opportunity to pick which patterns to identify first before playing around in the settings.
All of the settings/inputs described above are meant for experimentation. I encourage the user to tweak these values at will to find which set ups work best for whichever charts they decide to apply these patterns to.
Refer to the patterns themselves during experimentation. The statistic information provided on the tooltips of the patterns are meant to help guide input decisions. The breadth of candlestick theory is deep, and this was an attempt at capturing what I could in its sea of information.
█ LIMITATIONS
DISCLAIMER: While it may seem a bit paradoxical that this script aims to use past performance to potentially measure future results, past performance is not indicative of future results . Markets are highly adaptive and often unpredictable. This script is meant as an informational tool to show how patterns may behave. There is no guarantee that confidence intervals (or any other metric measured with this script) are accurate to the performance of patterns; caution must be exercised with all patterns identified regardless of how much information regarding prior performance is available.
Candlestick Theory - In the name, Candlestick Theory is a theory , and all theories come with their own limits. Some patterns identified by this script may be completely useless/unprofitable/unpredictable regardless of whatever combination of settings are used to identify them. However, if I truly believed this theory had no merit, this script would not exist. It is important to understand that this is a tool meant to be utilized with an array of others to procure positive (or negative, looking at you, short sellers ) results when navigating the complex world of finance.
To address the functionality note however, this script has an offset of 1 by default. Patterns will not be identified on the currently closing candle, only on the candle which has most recently closed. Attempting to have this script do both (offset by one or identify on close) lead to more trouble than it was worth. I personally just want users to be aware that patterns will not be identified immediately when they appear.
Trend Direction - Moving Averages - There is a small quirk with how MA settings will be adjusted if the user inputs two moving averages of the same length when the "MA Setting" is set to 'BOTH'. If Moving Averages have the same length, this script will default to only using MA 1 regardless of if the types of Moving Averages are different . I will experiment in the future to alleviate/reduce this restriction.
Price Analysis - BREAKOUT mode - With how identifying patterns with a look-ahead confirmation works, the percent returns for patterns that break out in either direction will be calculated on the same candle regardless of if P/L Offset is set to 'FROM CONFIRMATION' or 'FROM APPEARANCE'. This same issue is present in the Hikkake Hunter script mentioned earlier. This does not mean the P/L calculations are incorrect , the offset for the calculation is set by the number of candles required to confirm the pattern if 'FROM APPEARANCE' is selected. It just means that these two different P/L calculations will complete at the same time independent of the setting that's been selected.
Adaptive Coloring/Hard Limiting - Hard Limiting is only used with Adaptive Coloring and has no effect outside of it. If Hard Limiting is used, it is recommended to increase the 'Positive' and 'Negative' return tolerance values as a pattern's bullish/bearishness may be disproportionately represented with the gradient generated under a hard limit.
TARGET MODE - This mode will break rules regarding patterns that are overridden on purpose. If a pattern selected in TARGET mode would have otherwise been absorbed by a larger pattern, it will have that pattern's percent return calculated; potentially leading to duplicate returns being included in the matrix of all returns recognized by this script.
'Tall' Candle Setting - This is a wide-reaching setting, as approximately 30 different patterns or so rely on defining 'Tall' candles. Changing how 'Tall' candles are defined whether by the tolerance value those candles need to exceed or by the values of the candle used for the baseline comparison (RANGE/BODY) can wildly affect how this script functions under certain conditions. Refer to the tooltip of these settings for more information on which specific patterns are affected by this.
Doji Settings - There are roughly 10 or so two to three candle patterns which have Dojis as a part of them. If all Dojis are disabled, it will prevent some of these larger patterns from being recognized. This is a dependency issue that I may address in the future.
'Engulfing' Setting - Functionally, the two 'Engulfing' settings are quite different. Because of this, the 'RANGE' setting may cause certain patterns that would otherwise be valid under textbook and online references/definitions to not be recognized as such (like the Upside Gap Two Crows or Three Outside down).
█ PATTERN LIST
This script recognizes 85 patterns upon initial release. I am open to adding additional patterns to it in the future and any comments/suggestions are appreciated. It recognizes:
15 — 1 Candle Patterns
4 Hammer type patterns: Regular Hammer, Takuri Line, Shooting Star, and Hanging Man
9 Doji Candles: Regular Dojis, Northern/Southern Dojis, Gravestone/Dragonfly Dojis, Gapping Up/Down Dojis, and Long-Legged/Rickshaw Man Dojis
White/Black Long Days
32 — 2 Candle Patterns
4 Engulfing type patterns: Bullish/Bearish Engulfing and Last Engulfing Top/Bottom
Dark Cloud Cover
Bullish/Bearish Doji Star patterns
Hammer Inverted
Bullish/Bearish Haramis + Cross variants
Homing Pigeon
Bullish/Bearish Kicking
4 Lines type patterns: Bullish/Bearish Meeting/Separating Lines
Matching Low
On/In Neck patterns
Piercing pattern
Shooting Star (2 Lines)
Above/Below Stomach patterns
Thrusting
Tweezers Top/Bottom patterns
Two Black Gapping
Rising/Falling Window patterns
29 — 3 Candle Patterns
Bullish/Bearish Abandoned Baby patterns
Advance Block
Collapsing Doji Star
Deliberation
Upside/Downside Gap Three Methods patterns
Three Inside/Outside Up/Down patterns (4 total)
Bullish/Bearish Side-by-Side patterns
Morning/Evening Star patterns + Doji variants
Stick Sandwich
Downside/Upside Tasuki Gap patterns
Three Black Crows + Identical variation
Three White Soldiers
Three Stars in the South
Bullish/Bearish Tri-Star patterns
Two Crows + Upside Gap variant
Unique Three River Bottom
3 — 4 Candle Patterns
Concealing Baby Swallow
Bullish/Bearish Three Line Strike patterns
6 — 5 Candle Patterns
Bullish/Bearish Breakaway patterns
Ladder Bottom
Mat Hold
Rising/Falling Three Methods patterns
█ WORKS CITED
Because of the amount of time needed to complete this script, I am unable to provide exact dates for when some of these references were used. I will also not provide every single reference, as citing a reference for each individual pattern and the place it was reviewed would lead to a bibliography larger than this script and its description combined. There were five major resources I used when building this script, one book, two websites (for various different reasons including patterns, moving averages, and various other articles of information), various scripts from TradingView's public library (including TradingView's own source code for *all* candle patterns ), and PineScrypt's reference manual.
Bulkowski, Thomas N. Encyclopedia of Candlestick Patterns . Hoboken, New Jersey: John Wiley & Sons Inc., 2008. E-book (google books).
Various. Numerous webpages. CandleScanner . 2023. online. Accessed 2020 - 2023.
Various. Numerous webpages. Investopedia . 2023. online. Accessed 2020 - 2023.
█ AKNOWLEDGEMENTS
I want to take the time here to thank all of my friends and family, both online and in real life, for the support they've given me over the last few years in this endeavor. My pets who tried their hardest to keep me from completing it. And work for the grit to continue pushing through until this script's completion.
This belongs to me just as much as it does anyone else. Whether you are an institutional trader, gold bug hedging against the dollar, retail ape who got in on a squeeze, or just parents trying to grow their retirement/save for the kids. This belongs to everyone.
Private Beta for new features to be tested can be found here .
Vires In Numeris
Fundamental ScreenerThis indicator is designed to compare the year-over-year earnings and sales growth, as well as the P/E ratio of up to 10 stocks simultaneously .
This provides valuable insights into the fundamental performance of multiple stocks at the same time, allowing traders to quickly identify which stocks are outperforming or underperforming their peers.
The earnings and sales growth figures are calculated on a year-over-year basis , comparing the most recent quarter to the same quarter 1 year ago.
The P/E ratio is a valuation metric that measures a company's stock price relative to its trailing twelve months earnings per share.
By comparing these three key metrics across multiple stocks, traders can quickly identify which stock in a group has superior fundamentals.
Customization
Chose to compare 5 or 10 symbols
Table position, color, and size
Economic Calendar EventsThis indicator provides an overlay of Events on the main chart, where each Event is visually represented by a Label and vertical Line, placed at the specified time interval for each Event.
Events are defined by user data as an input string on the settings widget panel for the indicator. The event data is a string (semicolon delimited) whose grammar is a representation of a collection of Event records, where each Event record is a comma-separated list of fields, which correspond to:
The name of the event.
The symbol or ticker to which the Event applies (or `*` if it should apply to all ticklers).
The timezone and then the year, month, day, hour, and minute of the event, respectively.
Each Event record is separated by the semicolon ";" character.
As an example , assume `evantData` is the string:
"SVB,*,UTC,2023,03,10,00,00;US CPI,*,UTC,2023,04,12,08,30;ETH Shanghai,ETHUSD,UTC,2023,04,12,08,30"
In the above case, there are 4 Events defined, three of which apply to all tickers and one applies only to ETHUSD, as follows:
The first event is named SVB and applies to all tickers at UTC time on March 10, 2023 at 12:00:00.
The second event is named US CPI and applies to all tickers at UTC time on April 12, 2023 at 08:30:00.
The third event is named ETH Shanghai and applies to the ETHUSD ticker at UTC time on April 12, 2023 at 08:30:00.
The fourth event is named FOMC Rates and applies to all tickers at UTC time on May 3, 2023 at 14:00:00.
The following is a BNF for defining event data:
market-events ::= event-record | event-record ";" market-events
event-record ::= event-name "," ticker ”,” event-timezone "," event-time
event-name ::= string
event-time>::= year "," month "," day "," hour "," minute
event-timezone ::= string
ticker ::= "*" | string
string ::= +
year ::= {4}
month ::= {2}
day ::= {2}
hour ::= {2}
minute ::= {2}
Grospector DCA V.3This is system for DCA with strategy.
This has 5 zone Extreme high , high , normal , low , Extreme low. You can dynamic set min - max percent every zone.
Extreme zone is derivative short and long which It change Extreme zone to Normal zone all position will be closed.
Every Zone is splitted 10 channel. and this strategy calculate contribution.
and now can predict price in future.
Idea : Everything has average in its life. For bitcoin use 4 years for halving. I think it will be interesting price.
Default : I set MA is 365*4 days and average it again with 365 days.
Input :
len: This input represents the length of the moving average.
strongLen: This input represents the length of the moving average used to calculate the strong buy and strong sell zone.
shortMulti: This input represents the multiplier * moveing average used to calculate the short zone.
strongSellMulti: This input represents the multiplier used to calculate the strong sell signal.
sellMulti: This input represents the multiplier * moveing average used to calculate the sell zone.
strongBuyMulti: This input represents the multiplier used to calculate the strong sell signal.
longMulti: This input represents the multiplier * moveing average used to calculate the long zone.
*Diff sellMulti and strongBuyMulti which is normal zone.
useDerivative: This input is a boolean flag that determines whether to use the derivative display zone. If set to true, the derivative display zone will be used, otherwise it will be hidden.
zoneSwitch: This input determines where to display the channel signals. A value of 1 will display the signals in all zones, a value of 2 will display the signals in the chart pane, a value of 3 will display the signals in the data window, and a value of 4 will hide the signals.
price: Defines the price source used for the indicator calculations. The user can select from various options, with the default being the closing price.
labelSwitch: Defines whether to display assistive text on the chart. The user can select a boolean value (true/false), with the default being true.
zoneSwitch: Defines which areas of the chart to display assistive zones. The user can select from four options: 1 = all, 2 = chart only, 3 = data only, 4 = none. The default value is 2.
predictFuturePrice: Defines whether to display predicted future prices on the chart. The user can select a boolean value (true/false), with the default being true.
DCA: Defines the dollar amount to use for dollar-cost averaging (DCA) trades. The user can input an integer value, with a default value of 5.
WaitingDCA: Defines the amount of time to wait before executing a DCA trade. The user can input a float value, with a default value of 0.
Invested: Defines the amount of money invested in the asset. The user can input an integer value, with a default value of 0.
strategySwitch: Defines whether to turn on the trading strategy. The user can select a boolean value (true/false), with the default being true.
seperateDayOfMonth: Defines a specific day of the month on which to execute trades. The user can input an integer value from 1-31, with the default being 28.
useReserve: Defines whether to use a reserve amount for trading. The user can select a boolean value (true/false), with the default being true.
useDerivative: Defines whether to use derivative data for the indicator calculations. The user can select a boolean value (true/false), with the default being true.
useHalving: Defines whether to use halving data for the indicator calculations. The user can select a boolean value (true/false), with the default being true.
extendHalfOfHalving: Defines the amount of time to extend the halving date. The user can input an integer value, with the default being 200.
Every Zone : It calculate percent from top to bottom which every zone will be splited 10 step.
To effectively make the DCA plan, I recommend adopting a comprehensive strategy that takes into consideration your mindset as the best indicator of the optimal approach. By leveraging your mindset, the task can be made more manageable and adaptable to any market
Dollar-cost averaging (DCA) is a suitable investment strategy for sound money and growth assets which It is Bitcoin, as it allows for consistent and disciplined investment over time, minimizing the impact of market volatility and potential risks associated with market timing
JZ_Chaikin HTF Volatility BreakoutFirst off, all credit to Harry Potter as this is a minor customization of his indicator.
Basic additions:
-- Added a Higher Timeframe that is set to Daily but can be changed. Timeframe does wait until barstate.isconfirmed so won't repaint.
-- Added HMA smoothing line to both Chart and HTF. Can be used as it's own signal, as confirmation or in combination with faster signal line -- Breakout signal & Range Highlight use both.
-- Added optional coloring of HMA based on whether increasing or decreasing.
-- Added a low volatility option that highlights Range/No Trade zones. Defval is off so needs to be selected from inputs.
Breakout Signals are very simple and both take the HTF signal and HMA. When the faster Volatility line rises from below zero and comes within the range of -10 to 0, AND the HMA signals is increasing (and also below zero) for confirmation, generates a breakout signal of an incoming big move. You can alter the breakout threshold to be greater or less than -10, I just found that works best for filtering out the noise and false signals. Won't catch everything, but pretty reliable when it does.
Tested mostly on BTC so can't vouch for other assets and would likely need modification.
I've JUST taught myself coding from scratch (and to say I'm an amateur is an understatement), so apologies in advance if anything is unclear or could be coded better. Open to any suggestions.
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.👍
Volume+ (Time of Day)This volume indicator measures the volume for each bar at the time of day and displays the average and +1 standard deviation above that average.
The color intensity of the bar is increased when the volume exceeds +1 standard deviation.
See how on the chart that other than the early morning and late day volume, there are clearly key moments in the day at 7am, 10am, 10:30am, and 11am. See how on the second day, there were volume spikes before 10am, 10:30am and then more obviously just before 11am.
Typical Sweeps: Pivot high/low boxes. Grade sweeps, Handles/PipsTool to show typical pip-grade/ handle-grade sweep distance above pivot highs and pivot lows
-In consolidation/ranging periods (i.e. most of the time); Highs/Lows may by swept by fairly consistent distances in typical stop raids.
-Idea is from ICT teaching on typical Pip-grade sweeps in FX (10,20,30pips). Designed to work on FX, Indices, Commodities, Bitcoin.
-Above chart shows S&P; sweeping below and then above by 5 handles.
///inputs///
~choose sweep distance handles ($) or pips: will auto-calculate depending on the asset: FX= pips; Indices/stocks/commodities = handles ($)
--(2,5,10,20,30,50,100, 500, 1000)
~choose pivot lookback: larger number for more significant swing highs/lows
~choose number of historical boxes to display
~toggle on/off Pivot high boxes and Pivot low boxes independently
~extend boxes fully to the right (default is not extend)
~toggle on/off text
~text & box formatting options
Bitcoin, hourly chart; Pivot lookback = 15; $100 sweep boxes:
Eur/Usd; 15m chart; Pivot lookback = 30; 10pip sweep boxes; Boxes extended fully to the right:
Elliott Wave [LuxAlgo]The Elliott Wave indicator allows users to detect Elliott Wave (EW) impulses as well as corrective segments automatically on the chart. These are detected and displayed serially, allowing users to keep track of the evolution of an impulse or corrective wave.
Fibonacci retracements constructed from detected impulse waves are also included.
This script additionally allows users to get alerted on a wide variety of trigger conditions (see the ALERTS section below).
🔶 SETTINGS
🔹 Source
• "high" -> options high, close, maximum of open/close
• "low" -> options low, close, minimum of open/close
🔹 ZigZag
• The source and length are used to check whether a new Pivot Point is found.
Example:
• source = high/low, length = 10:
• There is a new pivot high when:
- previous high is higher than current high
- the highs of 10 bars prior to previous high are all lower
• These pivot points are used to form the ZigZag lines, which in their turn are used for pattern recognition
🔶 USAGE
The basic principles we use to identify Elliott Wave impulses are:
• A movement in the direction of the trend ( Motive/Impulse wave ) is divided in 5 waves (Wave 1 -> 5)
• The Corrective Wave (against the trend) is divided in 3 waves (Wave A -> C)
• The waves can be subdivided in smaller waves
• Wave 2 can’t retrace more than the beginning of Wave 1
• Wave 4 does not overlap with the price territory of Wave 1
Here we see an example:
Let's look at the development:
• 1 bar after point (5) a confirmed 5 Motive Wave pattern is found (1 -> 5; The 5 Waves can also be seen as one large Wave 1 ).
• Next, the script draws a set of Fibonacci lines, which are area's where the Corrective Wave potentially will bounce.
Here we see the fifth wave is getting larger, the previous highest point is updated, and the Wave 5 is larger than Wave 3 :
(At this point, the pattern is invalidated, and it display as dotted)
Further progression in time:
At this point, a confirmed " 3 Corrective Wave pattern " is found (a -> c)
When a new high has developed, a circle is drawn (in the same color of the lines)
However, when the bottom of the drawn box has breached, a red cross will be visualized.
Further progression:
Later on, a bearish confirmed " 5 Motive Wave pattern " is found (1 -> 5):
When a Corrective Wave becomes invalidated, the ABC pattern will display as dashed (not dotted):
🔶 TECHNIQUES
Pine Script™ introduces methods!
• More information can be found here:
• Pine Script™ v5 User Manual 👉 Methods
• Pine Script™ language reference manual 👉 method
🔶 ALERTS
Dynamic alerts are included in the script, you only need to set 1 alert to receive following messages:
• When a new EW Motive Pattern is found (Bullish/Bearish )
• When a new EW Corrective Pattern is found (Bullish/Bearish )
• When an EW Motive Pattern is invalidated (Bullish/Bearish )
• When an EW Corrective Pattern is invalidated (Bullish/Bearish )
• When possible, a start of a new EW Motive Wave is found (Bullish/Bearish )
• Here is information how you can set these alerts()
[Uhokang] Bollinger Band BB EMA SMMA SMA Multy timeframeYou can view indicators from the specified upper timeframe together.
( Bollinger Bands, SMMA, EMA, SMA )
If it is based on a 1-hour bar, you can see indicators for 4-hour bars and 1-day bars at the same time.
=> =>
Minutes
1 => 5 => 30
2 => 10 => 60
3 => 15 => 90
4 => 20 => 120
5 => 30 => 120
6 => 30 => 120
10 => 60 => 240
15 => 60 => 240
30 => 120 => 480
45 => 180 => 450
over Hours
1 => 4 => D
2 => 8 => 2D
3 => 12 => 3D
4 => D => W
D => W => M
W => M => Y
Correlation Matrix + Heatmap - By LeviathanA quick and easy way to visualize the correlation between 10 different symbols over a custom period of time, in the form of a matrix with a heatmap visualization and additional tools such as price increase/decrease %, multi-timeframe function, customizable appearance and more.
The indicator displays correlation coefficients for each pair of 10 assets in a matrix format, where the rows and columns represent the assets being compared between each other. The color of each cell corresponds with the strength of the correlation coefficient, allowing you to quickly identify which assets are strongly correlated, and which are not, and use that information to adjust risk or even get trade ideas. Coupled with the "Price Change" function, the script will help you find trade opportunities based on eg. the long-term correlation strength and short-term price direction differences between two assets. The "Correlation Length" input defines the number of bars used for calculating the correlation, while "Price Change Length" defines the bar to which the current price is compared when calculating price change (eg. the input 20 means that the script will compare the price of the candle close that occurred 20 bars ago and the current price).
Correlation coefficient:
The correlation coefficient is a statistical measure that quantifies the degree of linear relationship between two variables. The correlation coefficient ranges from -1 to 1, with 1 being perfect positive correlation and -1 being perfect negative correlation.
• A correlation coefficient of 1 indicates a perfect positive linear relationship between two variables. This means that when one variable increases, the other variable also increases in a proportional manner.
• A correlation coefficient of 0 indicates no linear relationship between two variables. This means that changes in one variable do not affect the other variable.
• A correlation coefficient of -1 indicates a perfect negative linear relationship between two variables. This means that when one variable increases, the other variable decreases in a proportional manner.
Inspired by @RicardoSantos's script
Flare🔶 METHODS
• Pine Script™ introduces methods ( 1 , 2 )! Much kuddos for the developers, Tradingview, and all who has worked on it!
• This educational script will show the simplified way of writing built-in methods, not to create a new method.
🔹 Simplified way of writing built-in methods:
· Instead of:
newArray = array.new()
array.unshift(newArray, 1)
lin = line.new(na, na, na, na)
line.set_xy1(lin, bar_index , close)
line.set_xy2(lin, bar_index + 10, close)
label newLabel = label.new(bar_index, high)
if barstate.islast
label.delete(newLabel)
· We now can write it like this:
newArray = array.new()
newArray.unshift(1)
lin = line.new(na, na, na, na)
lin.set_xy1(bar_index , close)
lin.set_xy2(bar_index + 10, close)
label newLabel = label.new(bar_index, high)
if barstate.islast
newLabel.delete()
——————————————————————————————————————————————————————————
· When using sometimes brackets are necessary:
label lab = label.new(bar_index, high)
if barstate.islast
label.set_color(lab, color.red)
label.delete(lab )
· ->
label lab = label.new(bar_index, high)
if barstate.islast
lab.set_color(color.red)
(lab ).delete() // lab .delete() doesn't compile at the moment
——————————————————————————————————————————————————————————
🔶 OVERVIEW OF SCRIPT
• The basic principles are:
· Find 1 point ( close ) x bars back from current bar ( settings: 'x close back').
· Create a 'Flare' shaped object from that point to current bar or further (dependable of "Width of Flare").
· Calculate where current close is located versus the Flare lines.
· On that bases, change colour and draw plotshapes.
· Below bar if current close is located in the upper part of the Flare
· Above bar if current close is located in the lower part of the Flare
· Above & Below if located in the middle part of the Flare
-> Above & Below colours has 3 different colours (adjustable), dependable on the position
🔶 EXAMPLES
· Neutral zone:
· Light Bullish zone:
· Bullish zone:
· Very Bullish / Overbought zone:
· Light Bearish zone:
· Bearish zone:
· Very Bearish / Oversold zone:
🔶 TECHNIQUES
🔹 I. Make a User Defined Type (UDT) Flare, with:
· 5x linefill - array of linefill
· int iDir, which captures the direction (current location of close in Flare)
· color cCol, this is a colour variable in relation to the direction.
🔹 II. Different functions will add a new Flare object, and update the values on each bar.
· Explanation of each function can be found in the script.
🔶 EXTRA's
· The input.color() is located in the function set_flare_B(flare obj)
· Best to put the inputs at the beginning of the script, I included this alternative just to show it is possible (but mostly not ideal)
· Background colour (settings: Bgcolor) can be enabled for better visibility of colours
Futures All List Candle Analysis - FALCAIn this command; There is an alphabetical list of USDS-M coins with the USDT PERP extension on the Binance Futures side.
There are 13 lists in total. Each list contains 39 data. Due to data limitation, 13 lists are formed. There are 13 coins in the first 11 of the lists. The 12th list contains 3 coins. The last list (FAVORITE LIST) is CRYPTOCAP:TOTAL, BINANCE:BTCUSDTPERP, BINANCE:BTCDOMUSDTPERP as standard. You must add 10 coins to the final list.
The lists show data for the time period you selected.
Explanation of the (C/H) header: Close /High takes a maximum value of 1. As long as this value is 1, a price increase is observed.
Explanation of the (C/O) header:
Close /Open can be greater than ,1. In this case, a price increase is observed.
Close /Open can be less than 1. In this case, a price decrease is observed.
The value Close /Open can be equal to 1. In this case, price stability is observed.
Explanation of the (C/L) header: Close /Low takes a minimum value of 1. As long as this value is 1, a price decrease is observed.
Coins with a price decrease are shown in red.
Coins with a price increase will turn green.
***NOTE: For this command to work, you must first add 10 favorite coins to the "FAVORITE LIST".
Trend crossierHello there!
I would like to share my script.
This is S/R levels indicator based on custom range for each step used for computing minimum/maximum of the range.
For example - we predefined step with 50 candles as one, after we have got the minimum and maximum of it - we come to the next 50 candles and do the same to it.
Finaly - we have got 20 lines for 10 steps.
After that - we set the ( SMA * mult) to have step between lines and if N of lines in range of step from eachother(if a+step > b and b+step > c ...) we call it a strong level.
It is not my idea(but my code), so I do not claim the uniqueness of this idea (the customer asked me to code it, but I also read about this method in open sources before).
The second thing - vectors of current trend direction on different timeframes.
For this task I took the same ranges, but ema () of each range. After first step it has an array with N values.
The next thing it does is take out "chain" one by one on each iteration. How does it do that? We take new AVG with neighbor values.
If we have 10 length at start - after iteration it becomes 9. So it repeats computing till length is 2.
After we have 2 points left - we can plot the line using indexes.
This way we can see the trend direction on different timeframes.
Feel free to use, change, put into another scripts. You can even never use it!
The right to publish this particular code was issued by the customer.
Have fun!
Global Money Supply USD-AdjustedGlobal Money Supply Aggregates
- US, China, EU, Japan, UK
- Korea, India, Canada, HK, Australia
- Taiwan, Brazil, Swiss, Russia, Mexico
- Thailand, Indonesia, Saudi, Singapore, Vietnam
Unfortunately, TV Pine can only take in 40 tickers, Each country has Money Supply Data adjusted against its FX to USD.
The formula in TV only can contain 10 tickers. So would recommend doing it up yourself on the first big 5 countries,
Anyway US and China constitute close to 50% of the global money supply.
However, one can argue whether money supply data (broad money) is valid nowadays. But I would bring up that this is just for comparison and trend purposes. Yes, M3 was removed from the US Fed data releases in end-2005.
Aggregating all of the above tickers yield me around 120tn of money supply, USD-adjusted.
There's pretty much other countries that should be included but due to the lack of data and small size of the country, it is omitted.
Examples:
- Heavily Sanctioned: Iran (400-500bn) and Iraq (200-300bn)
- No Data/Bad Data: Algeria (100bn), Bangladesh (110bn)
- Fallen Angels: Venezuela (100bn?), Argentina (120bn)
- 400-500bn club: Israel, Turkey, Poland, Sweden, Ireland, UAE, Msia, Chile
- 200-300bn club: Norway, Czech, Philippines, S.Africa, New Zealand, Egypt, Denmark, Qatar
- 100bn club: Colombia, Lebanon, Pakistan, Morocco, Romania, Hungary, Nigeria, Kuwait
- GDP 1bn club: Peru, Kazakhstan, Angola, Sudan, Ukraine, Ecuador
All these could add up to 10-15tn money supply, but with currencies with bad adoption on some. End of the day, we still living in a dollarised world, with the big 5 nations taking up to 80% of the officially published money supply. The unfortunate issue is that the money supply data isn't that forward-looking. A simple linear extrapolation of historical 3-month rolling average for next month estimates can be decent, with possibility of manual meddling to add adjustments on huge macro-events eg QE infinity.
Perhaps, additional tweaking would be inflation-adjusting this. Against SPX, some housing index, crude oil, gold.
howmuch.net
Machine Learning: Lorentzian Classification█ OVERVIEW
A Lorentzian Distance Classifier (LDC) is a Machine Learning classification algorithm capable of categorizing historical data from a multi-dimensional feature space. This indicator demonstrates how Lorentzian Classification can also be used to predict the direction of future price movements when used as the distance metric for a novel implementation of an Approximate Nearest Neighbors (ANN) algorithm.
█ BACKGROUND
In physics, Lorentzian space is perhaps best known for its role in describing the curvature of space-time in Einstein's theory of General Relativity (2). Interestingly, however, this abstract concept from theoretical physics also has tangible real-world applications in trading.
Recently, it was hypothesized that Lorentzian space was also well-suited for analyzing time-series data (4), (5). This hypothesis has been supported by several empirical studies that demonstrate that Lorentzian distance is more robust to outliers and noise than the more commonly used Euclidean distance (1), (3), (6). Furthermore, Lorentzian distance was also shown to outperform dozens of other highly regarded distance metrics, including Manhattan distance, Bhattacharyya similarity, and Cosine similarity (1), (3). Outside of Dynamic Time Warping based approaches, which are unfortunately too computationally intensive for PineScript at this time, the Lorentzian Distance metric consistently scores the highest mean accuracy over a wide variety of time series data sets (1).
Euclidean distance is commonly used as the default distance metric for NN-based search algorithms, but it may not always be the best choice when dealing with financial market data. This is because financial market data can be significantly impacted by proximity to major world events such as FOMC Meetings and Black Swan events. This event-based distortion of market data can be framed as similar to the gravitational warping caused by a massive object on the space-time continuum. For financial markets, the analogous continuum that experiences warping can be referred to as "price-time".
Below is a side-by-side comparison of how neighborhoods of similar historical points appear in three-dimensional Euclidean Space and Lorentzian Space:
This figure demonstrates how Lorentzian space can better accommodate the warping of price-time since the Lorentzian distance function compresses the Euclidean neighborhood in such a way that the new neighborhood distribution in Lorentzian space tends to cluster around each of the major feature axes in addition to the origin itself. This means that, even though some nearest neighbors will be the same regardless of the distance metric used, Lorentzian space will also allow for the consideration of historical points that would otherwise never be considered with a Euclidean distance metric.
Intuitively, the advantage inherent in the Lorentzian distance metric makes sense. For example, it is logical that the price action that occurs in the hours after Chairman Powell finishes delivering a speech would resemble at least some of the previous times when he finished delivering a speech. This may be true regardless of other factors, such as whether or not the market was overbought or oversold at the time or if the macro conditions were more bullish or bearish overall. These historical reference points are extremely valuable for predictive models, yet the Euclidean distance metric would miss these neighbors entirely, often in favor of irrelevant data points from the day before the event. By using Lorentzian distance as a metric, the ML model is instead able to consider the warping of price-time caused by the event and, ultimately, transcend the temporal bias imposed on it by the time series.
For more information on the implementation details of the Approximate Nearest Neighbors (ANN) algorithm used in this indicator, please refer to the detailed comments in the source code.
█ HOW TO USE
Below is an explanatory breakdown of the different parts of this indicator as it appears in the interface:
Below is an explanation of the different settings for this indicator:
General Settings:
Source - This has a default value of "hlc3" and is used to control the input data source.
Neighbors Count - This has a default value of 8, a minimum value of 1, a maximum value of 100, and a step of 1. It is used to control the number of neighbors to consider.
Max Bars Back - This has a default value of 2000.
Feature Count - This has a default value of 5, a minimum value of 2, and a maximum value of 5. It controls the number of features to use for ML predictions.
Color Compression - This has a default value of 1, a minimum value of 1, and a maximum value of 10. It is used to control the compression factor for adjusting the intensity of the color scale.
Show Exits - This has a default value of false. It controls whether to show the exit threshold on the chart.
Use Dynamic Exits - This has a default value of false. It is used to control whether to attempt to let profits ride by dynamically adjusting the exit threshold based on kernel regression.
Feature Engineering Settings:
Note: The Feature Engineering section is for fine-tuning the features used for ML predictions. The default values are optimized for the 4H to 12H timeframes for most charts, but they should also work reasonably well for other timeframes. By default, the model can support features that accept two parameters (Parameter A and Parameter B, respectively). Even though there are only 4 features provided by default, the same feature with different settings counts as two separate features. If the feature only accepts one parameter, then the second parameter will default to EMA-based smoothing with a default value of 1. These features represent the most effective combination I have encountered in my testing, but additional features may be added as additional options in the future.
Feature 1 - This has a default value of "RSI" and options are: "RSI", "WT", "CCI", "ADX".
Feature 2 - This has a default value of "WT" and options are: "RSI", "WT", "CCI", "ADX".
Feature 3 - This has a default value of "CCI" and options are: "RSI", "WT", "CCI", "ADX".
Feature 4 - This has a default value of "ADX" and options are: "RSI", "WT", "CCI", "ADX".
Feature 5 - This has a default value of "RSI" and options are: "RSI", "WT", "CCI", "ADX".
Filters Settings:
Use Volatility Filter - This has a default value of true. It is used to control whether to use the volatility filter.
Use Regime Filter - This has a default value of true. It is used to control whether to use the trend detection filter.
Use ADX Filter - This has a default value of false. It is used to control whether to use the ADX filter.
Regime Threshold - This has a default value of -0.1, a minimum value of -10, a maximum value of 10, and a step of 0.1. It is used to control the Regime Detection filter for detecting Trending/Ranging markets.
ADX Threshold - This has a default value of 20, a minimum value of 0, a maximum value of 100, and a step of 1. It is used to control the threshold for detecting Trending/Ranging markets.
Kernel Regression Settings:
Trade with Kernel - This has a default value of true. It is used to control whether to trade with the kernel.
Show Kernel Estimate - This has a default value of true. It is used to control whether to show the kernel estimate.
Lookback Window - This has a default value of 8 and a minimum value of 3. It is used to control the number of bars used for the estimation. Recommended range: 3-50
Relative Weighting - This has a default value of 8 and a step size of 0.25. It is used to control the relative weighting of time frames. Recommended range: 0.25-25
Start Regression at Bar - This has a default value of 25. It is used to control the bar index on which to start regression. Recommended range: 0-25
Display Settings:
Show Bar Colors - This has a default value of true. It is used to control whether to show the bar colors.
Show Bar Prediction Values - This has a default value of true. It controls whether to show the ML model's evaluation of each bar as an integer.
Use ATR Offset - This has a default value of false. It controls whether to use the ATR offset instead of the bar prediction offset.
Bar Prediction Offset - This has a default value of 0 and a minimum value of 0. It is used to control the offset of the bar predictions as a percentage from the bar high or close.
Backtesting Settings:
Show Backtest Results - This has a default value of true. It is used to control whether to display the win rate of the given configuration.
█ WORKS CITED
(1) R. Giusti and G. E. A. P. A. Batista, "An Empirical Comparison of Dissimilarity Measures for Time Series Classification," 2013 Brazilian Conference on Intelligent Systems, Oct. 2013, DOI: 10.1109/bracis.2013.22.
(2) Y. Kerimbekov, H. Ş. Bilge, and H. H. Uğurlu, "The use of Lorentzian distance metric in classification problems," Pattern Recognition Letters, vol. 84, 170–176, Dec. 2016, DOI: 10.1016/j.patrec.2016.09.006.
(3) A. Bagnall, A. Bostrom, J. Large, and J. Lines, "The Great Time Series Classification Bake Off: An Experimental Evaluation of Recently Proposed Algorithms." ResearchGate, Feb. 04, 2016.
(4) H. Ş. Bilge, Yerzhan Kerimbekov, and Hasan Hüseyin Uğurlu, "A new classification method by using Lorentzian distance metric," ResearchGate, Sep. 02, 2015.
(5) Y. Kerimbekov and H. Şakir Bilge, "Lorentzian Distance Classifier for Multiple Features," Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods, 2017, DOI: 10.5220/0006197004930501.
(6) V. Surya Prasath et al., "Effects of Distance Measure Choice on KNN Classifier Performance - A Review." .
█ ACKNOWLEDGEMENTS
@veryfid - For many invaluable insights, discussions, and advice that helped to shape this project.
@capissimo - For open sourcing his interesting ideas regarding various KNN implementations in PineScript, several of which helped inspire my original undertaking of this project.
@RikkiTavi - For many invaluable physics-related conversations and for his helping me develop a mechanism for visualizing various distance algorithms in 3D using JavaScript
@jlaurel - For invaluable literature recommendations that helped me to understand the underlying subject matter of this project.
@annutara - For help in beta-testing this indicator and for sharing many helpful ideas and insights early on in its development.
@jasontaylor7 - For helping to beta-test this indicator and for many helpful conversations that helped to shape my backtesting workflow
@meddymarkusvanhala - For helping to beta-test this indicator
@dlbnext - For incredibly detailed backtesting testing of this indicator and for sharing numerous ideas on how the user experience could be improved.
RSI Dashboard Monitor [Skiploss]RSI Dashboard Monitor is indicator for scanner Relative Strength Index ( RSI ) for 4 symbols and 10 Timeframe, detached from the main chart.
You can choose to display all 4 symbols or just 1 symbol, also can choose resolution (TF). Still, these 10 timeframes should be enough to use. Default is set to 14 but you can modify it from settings.
How it work??
RSI >= 75 : Fill color maximum overbought
RSI >= 65 : Fill color medium overbought
RSI >= 55 : Fill color minimum overbought
RSI >= 45 : Fill color minimum overbought
RSI >= 35 : Fill color medium overbought
RSI >= 25 : Fill color maximum overbought
** Values level and color style can modify it from settings
RedK K-MACD : a MACD with some more musclesMoving Averages are probably the most commonly used analysis tools, and MACD is possibly the first charting indicator a trader gets to learn about.
MACD Basic concept
----------------------------
Without repeating all the tons of documentation about what MACD does, let's quickly re-visit the MACD concept from a 10-mile altitude (note we're keen on simplifying here rather than being technically accurate - so please forgive the use of any "common lingos")
- MACD goal is to represent the distance between 2 Moving Averages (MAs) - one fast and one slow, relatively - as an unrestricted zero-based oscillator.
- The value of the main MACD line is the distance, or the displacement between the 2 MA's
- usually a signal line is used (which is another MA of that distance value) to enable better visualization of the change (and rate of change, since this is all depicted on a time axis) of that displacement - this represents price momentum (price movement in the recent period versus movements for a relatively longer period).
- the difference between the main MACD line and its signal is then represented as a histogram above and below the zero line. in this case, that histogram is really redundant, since it shows a value that is already represented visually by the main line and its signal line.
How K-MACD is different
---------------------------------
K-MACD takes that simple concept of the classic MACD and expands around it - the idea is to use the same simple approach to representing price momentum while bringing in more insight to price moves in the short, medium and long terms, ability to represent more than 2 MA's and to enable better identification of tradeable patterns (like Volatility Contraction and others) - while still keeping things simple and visually clean.
K-MACD is an indicator that allows us to view how price moves against 3 moving averages: a fast / slow pair, and a "market" Filter or Baseline (very long) that will be used as a flag for Bear/Bull market mode. Many traders and trading literature use the 200 day (40 week) SMA as that key filter
so in total, there are 4 MA lines in K-MACD (excluding the "orange" signal line):
* Price Proxy: Which is a very fast moving average that will represent the price itself - let's use a WMA(3) or something close to that here - there will be a signal line to enable better visualization of this similar to a classic MACD - that's the orange line
* Fast & Slow MA's : Use whatever represents the "medium term" momentum for your trading - Some traders use 20 and 50, others use 10 and 20 .. if on your price chart, you keep using a pair of MA's for this, use the same settings in K-MACD - these will be represented by the 3-color Momentum Bars that fluctuate above and below the baseline
* Filter/Baseline MA: Should be your long (Bullish/Bearish Mode) MA. so 100 or 200 or any other value you consider your market to be bearish below and bullish above. on K-MACD this is actually the blue zero line - everything else is "relative" to it
Review the sample chart which explains various elements and the "price chart" setup that K-MACD represents. With K-MACD you can clean up your chart from those various Moving Averages - or use a different set than the ones you already have K-MACD represent - or other indicators (like ATR channels..etc)
Other "muscles" in the K-MACD
---------------------------------------------
- Relative vs Classic Calculation Mode
A key issue with the classic MACD is that the displacement between the 2 moving averages is represented as "absolute or direct" values - as the price of the underlying increases with time, you can't really use these values to make useful comparison between the past and now (see below example) - also you can't use them to compare 2 different instruments.
- The "Relative" calculation option in K-MACD addresses that issue by relating all "distances" to the Baseline MA as percentage (above or below) - you can see this clear when you look at the above chart the far left versus the far right and compare K-MACD with the classic MACD - the Classic option is still available
- More MA "type" options for all MA lines: choose between SMA, EMA, WMA, and RSS_WMA (which i use a lot in my trading and is my default for the Price Proxy)
- More Alerts: a total or 9 alerts (in 3 groups) are available with K-MACD (Momentum above or below baseline, Price Proxy crossing signal line, and Price Proxy crossing baseline)
- New 52 week High / Low markers: These will show as Green/red circles on the zero line in K-MACD. this will only work for 1D timeframe and above, i'm just using a simple approach and would like to keep it that way.
- i know i added some more features not covered above :) -- if you have questions about any of the settings, feel free to ask below
Closing thoughts
-------------------------
K-MACD is a combination of couple of indicators i published in the past (xMACD and Mo_Bars) - so you can go back and read about them if needed - I then added improvements to accommodate ideas from swing trading literature and common practices that i plan to focus on in future. So K-MACD is really part of my own trading setup.
I assume here that most traders are familiar with what a MACD is - so kept this post short - if you thing we should expand more about the concepts covered here let me know in the comments - i can make some separate posts with examples and more details.
I hope many fellow traders find this work useful - and feel free let me know in comments below if you do.
TechnicalRating█ OVERVIEW
This library is a Pine Script™ programmer’s tool for incorporating TradingView's well-known technical ratings within their scripts. The ratings produced by this library are the same as those from the speedometers in the technical analysis summary and the "Rating" indicator in the Screener , which use the aggregate biases of 26 technical indicators to calculate their results.
█ CONCEPTS
Ensemble analysis
Ensemble analysis uses multiple weaker models to produce a potentially stronger one. A common form of ensemble analysis in technical analysis is the usage of aggregate indicators together in hopes of gaining further market insight and reinforcing trading decisions.
Technical ratings
Technical ratings provide a simplified way to analyze financial markets by combining signals from an ensemble of indicators into a singular value, allowing traders to assess market sentiment more quickly and conveniently than analyzing each constituent separately. By consolidating the signals from multiple indicators into a single rating, traders can more intuitively and easily interpret the "technical health" of the market.
Calculating the rating value
Using a variety of built-in TA functions and functions from our ta library, this script calculates technical ratings for moving averages, oscillators, and their overall result within the `calcRatingAll()` function.
The function uses the script's `calcRatingMA()` function to calculate the moving average technical rating from an ensemble of 15 moving averages and filters:
• Six Simple Moving Averages and six Exponential Moving Averages with periods of 10, 20, 30, 50, 100, and 200
• A Hull Moving Average with a period of 9
• A Volume-Weighted Moving Average with a period of 20
• An Ichimoku Cloud with a conversion line length of 9, base length of 26, and leading span B length of 52
The function uses the script's `calcRating()` function to calculate the oscillator technical rating from an ensemble of 11 oscillators:
• RSI with a period of 14
• Stochastic with a %K period of 14, a smoothing period of 3, and a %D period of 3
• CCI with a period of 20
• ADX with a DI length of 14 and an ADX smoothing period of 14
• Awesome Oscillator
• Momentum with a period of 10
• MACD with fast, slow, and signal periods of 12, 26, and 9
• Stochastic RSI with an RSI period of 14, a %K period of 14, a smoothing period of 3, and a %D period of 3
• Williams %R with a period of 14
• Bull Bear Power with a period of 50
• Ultimate Oscillator with fast, middle, and slow lengths of 7, 14, and 28
Each indicator is assigned a value of +1, 0, or -1, representing a bullish, neutral, or bearish rating. The moving average rating is the mean of all ratings that use the `calcRatingMA()` function, and the oscillator rating is the mean of all ratings that use the `calcRating()` function. The overall rating is the mean of the moving average and oscillator ratings, which ranges between +1 and -1. This overall rating, along with the separate MA and oscillator ratings, can be used to gain insight into the technical strength of the market. For a more detailed breakdown of the signals and conditions used to calculate the indicators' ratings, consult our Help Center explanation.
Determining rating status
The `ratingStatus()` function produces a string representing the status of a series of ratings. The `strongBound` and `weakBound` parameters, with respective default values of 0.5 and 0.1, define the bounds for "strong" and "weak" ratings.
The rating status is determined as follows:
Rating Value Rating Status
< -strongBound Strong Sell
< -weakBound Sell
-weakBound to weakBound Neutral
> weakBound Buy
> strongBound Strong Buy
By customizing the `strongBound` and `weakBound` values, traders can tailor the `ratingStatus()` function to fit their trading style or strategy, leading to a more personalized approach to evaluating ratings.
Look first. Then leap.
█ FUNCTIONS
This library contains the following functions:
calcRatingAll()
Calculates 3 ratings (ratings total, MA ratings, indicator ratings) using the aggregate biases of 26 different technical indicators.
Returns: A 3-element tuple: ( [(float) ratingTotal, (float) ratingOther, (float) ratingMA ].
countRising(plot)
Calculates the number of times the values in the given series increase in value up to a maximum count of 5.
Parameters:
plot : (series float) The series of values to check for rising values.
Returns: (int) The number of times the values in the series increased in value.
ratingStatus(ratingValue, strongBound, weakBound)
Determines the rating status of a given series based on its values and defined bounds.
Parameters:
ratingValue : (series float) The series of values to determine the rating status for.
strongBound : (series float) The upper bound for a "strong" rating.
weakBound : (series float) The upper bound for a "weak" rating.
Returns: (string) The rating status of the given series ("Strong Buy", "Buy", "Neutral", "Sell", or "Strong Sell").
LowHighFinderThis chart display how value change of (low,high,close,open) is considered as a factor for buying or selling. Each element take same weight when consider the final price. The price change over a certain threshold would be the decision point (buy/sell)
Factors considered in this chart
1.Quotes: High,low,close,open,volume. If one of them higher than previous day, then it increase, otherwise decreases.
2. Multipler: If you think one quote is more important than other (High more important than close, you can set multipler higher)
3. EMA smoother: It is using to balance the price effect. Like if price increased dramatically, EMA would notify whether could be a good time to sell. (Because high deviation between MA and price suggest price increase too fast)
4. Length of line: set length of line for you need
5. Percentage change: how much percentage change is considered a significant change? 5%? or 10%? In which case should it count toward the final indicator? Adjust percentage change needed, smaller for minutes chart (less than 10) higher for hours chart (10-20), even higher for day chart
Buy/Sell method:
1. When green dot appears, wait after price start to get close to moving average to find the low point and buy.
2. Reverse for red dot.
