Supertrend & CCI Strategy ScalpThis strategy is based on 2 Super Trend Indicators along with CCI .
The longer factor length gives you the current trend and the deviation in the short factor length gives us the opportunity to enter in the trade .
CCI indicator is used to determine the overbought and oversold levels.
Setup :
Long : When atrLength1 > close and atrLength2 < close and CCI < -100 we look for long trades as the longer factor length will be bullish .
Short : When atrLength1 < close and atrLength2 > close and CCI > 100 we look for short trades as the longer factor length will be bearish .
Please tune the settings according to your use .
Trade what you see not what you feel .
Please consult with your financial advisor before you deploy any real money for trading .
Cari dalam skrip untuk "想象图：箱线图+折线组合，横轴为国家，纵轴为响应指数（0-100），箱线显示均值±标准差，叠加红色虚线标注各国确诊高峰时间点"
Blockunity Level Presets (BLP)A simple tool for setting performance targets.
Level Presets (BLP) is a simple tool for setting upside and downside levels relative to the current price of any asset. In this way, you can track which price the asset needs to move towards in order to achieve a defined performance.
How to Use
This indicator is very easy to use, you can set up to 5 upward and downward targets in the parameters.
Elements
The main elements of this tool are upward (default green) and downward (default red) levels.
Settings
Several parameters can be defined in the indicator configuration.
In addition to configuring which performance value to set the level at, you can choose not to display it if you don't need it. For example, here we display only two levels:
You can also choose not to display the labels:
Also concerning labels, you can choose not to display them in currency format, but in numerical format only (for example, if you're viewing a non-USD pair, such as ETHBTC):
Finally, you can modify design elements such as colors, level widths and text size:
How it Works
Here's how upside (_u) and downside (_d) levels are calculated:
source = close
level_1_u = source + (source * (level_1 / 100))
level_1_d = math.max(source - (source * (level_1 / 100)), 0)
Ohlson O-Score IndicatorThe Ohlson O-Score is a financial metric developed by Olof Ohlson to predict the probability of a company experiencing financial distress. It is widely used by investors and analysts as a key tool for financial analysis.
Inputs:
Period: Select the financial period for analysis, either "FY" (Fiscal Year) or "FQ" (Fiscal Quarter).
Country: Specify the country for Gross Net Product data. This helps in tailoring the analysis to specific economic conditions.
Gross Net Product : Define the number of years back for the index to be set at 100. This parameter provides a historical context for the analysis.
Table Display : Customize the display of various tables to suit your preference and analytical needs.
Key Features:
Predictive Power : The Ohlson O-Score is renowned for its predictive power in assessing the financial health of a company. It incorporates multiple financial ratios and indicators to provide a comprehensive view.
Financial Distress Prediction : Use the O-Score to gauge the likelihood of a company facing financial distress in the future. It's a valuable tool for risk assessment.
Country-Specific Analysis : Tailor the analysis to the economic conditions of a specific country, ensuring a more accurate evaluation of financial health.
Historical Context : Set the Gross Net Product index at a specific historical point, allowing for a deeper understanding of how a company's financial health has evolved over time.
How to Use:
Select Period : Choose either Fiscal Year or Fiscal Quarter based on your preference.
Specify Country : Input the country for country-specific Gross Net Product data.
Set Historical Context : Determine the number of years back for the index to be set at 100, providing historical context to your analysis.
Custom Table Display : Personalize the display of various tables to focus on the metrics that matter most to you.
Calculation and component description
Here is the description of O-score components as found in orginal Ohlson publication :
1. SIZE = log(total assets/GNP price-level index). The index assumes a base value of 100 for 1968. Total assets are as reported in dollars. The index year is as of the year prior to the year of the balance sheet date. The procedure assures a real-time implementation of the model. The log transform has an important implication. Suppose two firms, A and B, have a balance sheet date in the same year, then the sign of PA - Pe is independent of the price-level index. (This will not follow unless the log transform is applied.) The latter is, of course, a desirable property.
2. TLTA = Total liabilities divided by total assets.
3. WCTA = Working capital divided by total assets.
4. CLCA = Current liabilities divided by current assets.
5. OENEG = One if total liabilities exceeds total assets, zero otherwise.
6. NITA = Net income divided by total assets.
7. FUTL = Funds provided by operations divided by total liabilities
8. INTWO = One if net income was negative for the last two years, zero otherwise.
9. CHIN = (NI, - NI,-1)/(| NIL + (NI-|), where NI, is net income for the most recent period. The denominator acts as a level indicator. The variable is thus intended to measure change in net income. (The measure appears to be due to McKibben ).
Interpretation
The foundational model for the O-Score evolved from an extensive study encompassing over 2000 companies, a notable leap from its predecessor, the Altman Z-Score, which examined a mere 66 companies. In direct comparison, the O-Score demonstrates significantly heightened accuracy in predicting bankruptcy within a 2-year horizon.
While the original Z-Score boasted an estimated accuracy of over 70%, later iterations reached impressive levels of 90%. Remarkably, the O-Score surpasses even these high benchmarks in accuracy.
It's essential to acknowledge that no mathematical model achieves 100% accuracy. While the O-Score excels in forecasting bankruptcy or solvency, its precision can be influenced by factors both internal and external to the formula.
For the O-Score, any results exceeding 0.5 indicate a heightened likelihood of the firm defaulting within two years. The O-Score stands as a robust tool in financial analysis, offering nuanced insights into a company's financial stability with a remarkable degree of accuracy.
Volume Oscillators Focus IndicatorVolume Oscillators Focus Indicator
Short name VolumeFocus
This indicator seeks to show episodes of high and low volumes analyzing these by calculating three lines and create colorings on the basis of where these lines go relative to each other.
The first line is a percent based on the current volume level, for which a 3 period sma is taken.
It is calculated by using the lowest volume in the lookback as zero, the highest as 100 percent
This line is called “current volume level”
The second line is a percent, based on the median volume of the last five periods. This line is called “new normal volume”
The third line is a percent, based on the median volume of the lookback period. This is called “old normal volume”
For the second and third line the lowest “new normal volume” in the lookback is used as zero while the 100 percent level is the same as in the calculation of the first line.
The reasoning for the colors is as follows:
When both current en new normal level are below old normal, the volume is to be considered ‘low’. When volume is low, the background color is gray and the fill color between the old normal and current lines is navy.
When both current and new normal level are above old normal, the volume is to be considered ‘significantly expanded’. When this happens the fill color between current and old normal is orange.
When volume is not low it is considered normal or high and the background color is green.
The lookback is set to 50, it advise to keep it that way.
Use of the indicator.
Volume results from focus of the market on the instrument. When the price seems correct, some buy it, some sell it but most don’t care. Then the volume is low, the background is gray. The navy fill color indicates ‘how low’.
When the price seems off, many will care and start trading. Then volume is high, background is green. When the trading is really heating up the orange fill color appears, showing that the market has high focus on this instrument, perhaps move in a trend.
Of course we don’t know in which way the market tries to ‘correct’ the price, for that purpose I use this indicator together with REVE Cohorts which provide useful markers to explain what the excess volume means.
Eykpunter
Blockunity Drawdown Visualizer (BDV)Monitor the drawdown (value of the drop between the highest and lowest points) of assets and act accordingly to reduce your risk.
Introducing BDV, the incredibly intuitive metric that visualizes asset drawdowns in the most visually appealing manner. With its color gradient display, BDV allows you to instantly grasp the state of retracement from the asset’s highest price level. But that’s not all – you have the option to display the oscillator’s colorization directly on your chart, enhancing your analysis even further.
The Idea
The goal is to provide the community with the best and most complete tool for visualizing the Drawdown of any asset.
How to Use
Very simple to use, the indicator takes the form of an oscillator, with colors ranging from red to green depending on the Drawdown level. A table summarizes several key data points.
Elements
On the oscillator, you'll find a line with a color gradient showing the asset's Drawdown. The flatter line represents the Max Drawdown (the lowest value reached).
In addition, the table summarizes several data:
The asset's All Time High (ATH).
Current Drawdown.
The Max Drawdown that has been reached.
Settings
First of all, you can activate a "Bar Color" in the settings (You must also uncheck "Borders" and "Wick" in your Chart Settings):
You can display Fibonacci levels on the oscillator. You'll see that levels can be relevant to drawdown. The color of the levels is also configurable.
In the calculation parameters, you can first choose between taking the High of the candles or the Close. By default this is Close, but if you change the parameter to High, the indication next to ATH in the table will change, and you'll see that the values in the table will be affected.
The second calculation parameter (Start Date) lets you modify the effective start date of the ATH, which will affect the drawdown level. Here's an example:
How it Works
First, we calculate the ATH:
var bdv_top = bdv_source
bdv_top := na(bdv_top ) ? bdv_source : math.max(bdv_source, bdv_top )
Then the drawdown is calculated as follows:
bdv = ((bdv_source / bdv_top) * 100) - 100
Then the max drawdown :
bdv_max = bdv
bdv_max := na(bdv_max ) ? bdv : math.min(bdv, bdv_max )
ottlibLibrary "ottlib"
█ OVERVIEW
This library contains functions for the calculation of the OTT (Optimized Trend Tracker) and its variants, originally created by Anıl Özekşi (Anil_Ozeksi). Special thanks to him for the concept and to Kıvanç Özbilgiç (KivancOzbilgic) and dg_factor (dg_factor) for adapting them to Pine Script.
█ WHAT IS "OTT"
The OTT (Optimized Trend Tracker) is a highly customizable and very effective trend-following indicator that relies on moving averages and a trailing stop at its core. Moving averages help reduce noise by smoothing out sudden price movements in the markets, while trailing stops assist in detecting trend reversals with precision. Initially developed as a noise-free trailing stop, the current variants of OTT range from rapid trend reversal detection to long-term trend confirmation, thanks to its extensive customizability.
It's well-known variants are:
OTT (Optimized Trend Tracker).
TOTT (Twin OTT).
OTT Channels.
RISOTTO (RSI OTT).
SOTT (Stochastic OTT).
HOTT & LOTT (Highest & Lowest OTT)
ROTT (Relative OTT)
FT (Original name is Fırsatçı Trend in Turkish which translates to Opportunist Trend)
█ LIBRARY FEATURES
This library has been prepared in accordance with the style, coding, and annotation standards of Pine Script version 5. As a result, explanations and examples will appear when users hover over functions or enter function parameters in the editor.
█ USAGE
Usage of this library is very simple. Just import it to your script with the code below and use its functions.
import ismailcarlik/ottlib/1 as ottlib
█ FUNCTIONS
• f_vidya(source, length, cmoLength)
Short Definition: Chande's Variable Index Dynamic Average (VIDYA).
Details: This function computes Chande's Variable Index Dynamic Average (VIDYA), which serves as the original moving average for OTT. The 'length' parameter determines the number of bars used to calculate the average of the given source. Lower values result in less smoothing of prices, while higher values lead to greater smoothing. While primarily used internally in this library, it has been made available for users who wish to utilize it as a moving average or use in custom OTT implementations.
Parameters:
source (float) : (series float) Series of values to process.
length (simple int) : (simple int) Number of bars to lookback.
cmoLength (simple int) : (simple int) Number of bars to lookback for calculating CMO. Default value is `9`.
Returns: (float) Calculated average of `source` for `length` bars back.
Example:
vidyaValue = ottlib.f_vidya(source = close, length = 20)
plot(vidyaValue, color = color.blue)
• f_mostTrail(source, multiplier)
Short Definition: Calculates trailing stop value.
Details: This function calculates the trailing stop value for a given source and the percentage. The 'multiplier' parameter defines the percentage of the trailing stop. Lower values are beneficial for catching short-term reversals, while higher values aid in identifying long-term trends. Although only used once internally in this library, it has been made available for users who wish to utilize it as a traditional trailing stop or use in custom OTT implementations.
Parameters:
source (float) : (series int/float) Series of values to process.
multiplier (simple float) : (simple float) Percent of trailing stop.
Returns: (float) Calculated value of trailing stop.
Example:
emaValue = ta.ema(source = close, length = 14)
mostValue = ottlib.f_mostTrail(source = emaValue, multiplier = 2.0)
plot(mostValue, color = emaValue >= mostValue ? color.green : color.red)
• f_ottTrail(source, multiplier)
Short Definition: Calculates OTT-specific trailing stop value.
Details: This function calculates the trailing stop value for a given source in the manner used in OTT. Unlike a traditional trailing stop, this function modifies the traditional trailing stop value from two bars prior by adjusting it further with half the specified percentage. The 'multiplier' parameter defines the percentage of the trailing stop. Lower values are beneficial for catching short-term reversals, while higher values aid in identifying long-term trends. Although primarily used internally in this library, it has been made available for users who wish to utilize it as a trailing stop or use in custom OTT implementations.
Parameters:
source (float) : (series int/float) Series of values to process.
multiplier (simple float) : (simple float) Percent of trailing stop.
Returns: (float) Calculated value of OTT-specific trailing stop.
Example:
vidyaValue = ottlib.f_vidya(source = close, length = 20)
ottValue = ottlib.f_ottTrail(source = vidyaValue, multiplier = 1.5)
plot(ottValue, color = vidyaValue >= ottValue ? color.green : color.red)
• ott(source, length, multiplier)
Short Definition: Calculates OTT (Optimized Trend Tracker).
Details: The OTT consists of two lines. The first, known as the "Support Line", is the VIDYA of the given source. The second, called the "OTT Line", is the trailing stop based on the Support Line. The market is considered to be in an uptrend when the Support Line is above the OTT Line, and in a downtrend when it is below.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`.
length (simple int) : (simple int) Number of bars to lookback. Default value is `2`.
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `1.4`.
Returns: ( [ float, float ]) Tuple of `supportLine` and `ottLine`.
Example:
= ottlib.ott(source = close, length = 2, multiplier = 1.4)
longCondition = ta.crossover(supportLine, ottLine)
shortCondition = ta.crossunder(supportLine, ottLine)
• tott(source, length, multiplier, bandsMultiplier)
Short Definition: Calculates TOTT (Twin OTT).
Details: TOTT consists of three lines: the "Support Line," which is the VIDYA of the given source; the "Upper Line," a trailing stop of the Support Line adjusted with an added multiplier; and the "Lower Line," another trailing stop of the Support Line, adjusted with a reduced multiplier. The market is considered in an uptrend if the Support Line is above the Upper Line and in a downtrend if it is below the Lower Line.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`.
length (simple int) : (simple int) Number of bars to lookback. Default value is `40`.
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `0.6`.
bandsMultiplier (simple float) : Multiplier for bands. Default value is `0.0006`.
Returns: ( [ float, float, float ]) Tuple of `supportLine`, `upperLine` and `lowerLine`.
Example:
= ottlib.tott(source = close, length = 40, multiplier = 0.6, bandsMultiplier = 0.0006)
longCondition = ta.crossover(supportLine, upperLine)
shortCondition = ta.crossunder(supportLine, lowerLine)
• ott_channel(source, length, multiplier, ulMultiplier, llMultiplier)
Short Definition: Calculates OTT Channels.
Details: OTT Channels comprise nine lines. The central line, known as the "Mid Line," is the OTT of the given source's VIDYA. The remaining lines are positioned above and below the Mid Line, shifted by specified multipliers.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`
length (simple int) : (simple int) Number of bars to lookback. Default value is `2`
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `1.4`
ulMultiplier (simple float) : (simple float) Multiplier for upper line. Default value is `0.01`
llMultiplier (simple float) : (simple float) Multiplier for lower line. Default value is `0.01`
Returns: ( [ float, float, float, float, float, float, float, float, float ]) Tuple of `ul4`, `ul3`, `ul2`, `ul1`, `midLine`, `ll1`, `ll2`, `ll3`, `ll4`.
Example:
= ottlib.ott_channel(source = close, length = 2, multiplier = 1.4, ulMultiplier = 0.01, llMultiplier = 0.01)
• risotto(source, length, rsiLength, multiplier)
Short Definition: Calculates RISOTTO (RSI OTT).
Details: RISOTTO comprised of two lines: the "Support Line," which is the VIDYA of the given source's RSI value, calculated based on the length parameter, and the "RISOTTO Line," a trailing stop of the Support Line. The market is considered in an uptrend when the Support Line is above the RISOTTO Line, and in a downtrend if it is below.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`.
length (simple int) : (simple int) Number of bars to lookback. Default value is `50`.
rsiLength (simple int) : (simple int) Number of bars used for RSI calculation. Default value is `100`.
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `0.2`.
Returns: ( [ float, float ]) Tuple of `supportLine` and `risottoLine`.
Example:
= ottlib.risotto(source = close, length = 50, rsiLength = 100, multiplier = 0.2)
longCondition = ta.crossover(supportLine, risottoLine)
shortCondition = ta.crossunder(supportLine, risottoLine)
• sott(source, kLength, dLength, multiplier)
Short Definition: Calculates SOTT (Stochastic OTT).
Details: SOTT is comprised of two lines: the "Support Line," which is the VIDYA of the given source's Stochastic value, based on the %K and %D lengths, and the "SOTT Line," serving as the trailing stop of the Support Line. The market is considered in an uptrend when the Support Line is above the SOTT Line, and in a downtrend when it is below.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`.
kLength (simple int) : (simple int) Stochastic %K length. Default value is `500`.
dLength (simple int) : (simple int) Stochastic %D length. Default value is `200`.
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `0.5`.
Returns: ( [ float, float ]) Tuple of `supportLine` and `sottLine`.
Example:
= ottlib.sott(source = close, kLength = 500, dLength = 200, multiplier = 0.5)
longCondition = ta.crossover(supportLine, sottLine)
shortCondition = ta.crossunder(supportLine, sottLine)
• hottlott(length, multiplier)
Short Definition: Calculates HOTT & LOTT (Highest & Lowest OTT).
Details: HOTT & LOTT are composed of two lines: the "HOTT Line", which is the OTT of the highest price's VIDYA, and the "LOTT Line", the OTT of the lowest price's VIDYA. A high price surpassing the HOTT Line can be considered a long signal, while a low price dropping below the LOTT Line may indicate a short signal.
Parameters:
length (simple int) : (simple int) Number of bars to lookback. Default value is `20`.
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `0.6`.
Returns: ( [ float, float ]) Tuple of `hottLine` and `lottLine`.
Example:
= ottlib.hottlott(length = 20, multiplier = 0.6)
longCondition = ta.crossover(high, hottLine)
shortCondition = ta.crossunder(low, lottLine)
• rott(source, length, multiplier)
Short Definition: Calculates ROTT (Relative OTT).
Details: ROTT comprises two lines: the "Support Line", which is the VIDYA of the given source, and the "ROTT Line", the OTT of the Support Line's VIDYA. The market is considered in an uptrend if the Support Line is above the ROTT Line, and in a downtrend if it is below. ROTT is similar to OTT, but the key difference is that the ROTT Line is derived from the VIDYA of two bars of Support Line, not directly from it.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`.
length (simple int) : (simple int) Number of bars to lookback. Default value is `200`.
multiplier (simple float) : (simple float) Percent of trailing stop. Default value is `0.1`.
Returns: ( [ float, float ]) Tuple of `supportLine` and `rottLine`.
Example:
= ottlib.rott(source = close, length = 200, multiplier = 0.1)
isUpTrend = supportLine > rottLine
isDownTrend = supportLine < rottLine
• ft(source, length, majorMultiplier, minorMultiplier)
Short Definition: Calculates Fırsatçı Trend (Opportunist Trend).
Details: FT is comprised of two lines: the "Support Line", which is the VIDYA of the given source, and the "FT Line", a trailing stop of the Support Line calculated using both minor and major trend values. The market is considered in an uptrend when the Support Line is above the FT Line, and in a downtrend when it is below.
Parameters:
source (float) : (series float) Series of values to process. Default value is `close`.
length (simple int) : (simple int) Number of bars to lookback. Default value is `30`.
majorMultiplier (simple float) : (simple float) Percent of major trend. Default value is `3.6`.
minorMultiplier (simple float) : (simple float) Percent of minor trend. Default value is `1.8`.
Returns: ( [ float, float ]) Tuple of `supportLine` and `ftLine`.
Example:
= ottlib.ft(source = close, length = 30, majorMultiplier = 3.6, minorMultiplier = 1.8)
longCondition = ta.crossover(supportLine, ftLine)
shortCondition = ta.crossunder(supportLine, ftLine)
█ CUSTOM OTT CREATION
Users can create custom OTT implementations using f_ottTrail function in this library. The example code which uses EMA of 7 period as moving average and calculates OTT based of it is below.
Source Code:
//@version=5
indicator("Custom OTT", shorttitle = "COTT", overlay = true)
import ismailcarlik/ottlib/1 as ottlib
src = input.source(close, title = "Source")
length = input.int(7, title = "Length", minval = 1)
multiplier = input.float(2.0, title = "Multiplier", minval = 0.1)
support = ta.ema(source = src, length = length)
ott = ottlib.f_ottTrail(source = support, multiplier = multiplier)
pSupport = plot(support, title = "Moving Average Line (Support)", color = color.blue)
pOtt = plot(ott, title = "Custom OTT Line", color = color.orange)
fillColor = support >= ott ? color.new(color.green, 60) : color.new(color.red, 60)
fill(pSupport, pOtt, color = fillColor, title = "Direction")
Result:
█ DISCLAIMER
Trading is risky and most of the day traders lose money eventually. This library and its functions are only for educational purposes and should not be construed as financial advice. Past performances does not guarantee future results.
Leveraged Share Decay Tracker [SS]Releasing this utility tool for leveraged share traders and investors.
It is very difficult to track the amount of decay and efficiency that is associated with leveraged shares and since not all leveraged shares are created equally, I developed this tool to help investors/traders ascertain:
1. The general risk, in $$, per share associated with investing in a particular leveraged ETF
2. The ability of a leveraged share to match what it purports to do (i.e. if it is a 3X Bull share, is it actually returning consistently 3X the underlying or is there a large variance?)
3. The general decay at various timepoints expressed in $$$
How to use:
You need to be opened on the chart of the underlying. In the example above, the chart is on DIA, the leveraged share being tracked is UDOW (3X bull share of the DOW).
Once you are on the chart of the underlying, you then put in the leveraged share of interest. The indicator will perform two major assessments:
1. An analysis of the standard error between the underlying and the leveraged share. This is accomplished through linear regression, but instead of creating a linreg equation, it simply uses the results to ascertain the degree of error associated at various time points (the time points are 10, 20, 30, 40, 50, 100, 252).
2. An analysis of the variance of returns. The indicator requires you to put in the leverage amount. So if the leverage amount is 3% (i.e. SPXL or UPRO is 3 X SPY), be sure that you are putting that factor in the settings. It will then modify the underlying to match the leverage amount, and perform an assessment of variance over 10, 20, 30, 40, 50, 100, 252 days to ensure stability. This will verify whether the leveraged ETF is actually consistently performing how it purports to perform.
Here are some examples, and some tales of caution so you can see, for yourself, how not all leveraged shares are created equal.
SPY and SPXL:
SPY and UPRO:
XBI and LABU (3 x bull share):
XBI and LABD (3 x bear share):
SOX and SOXL:
AAPL and AAPU:
It is VERY pivotal you remember to check and adjust the Leveraged % factor.
For example, AAPU is leveraged 1.5%. You can see above it tracks this well. However, if you accidently leave it at 3%, you will get an erroneous result:
You can also see how some can fail to track the quoted leveraged amount, but still produce relatively lower risk decay.
And, as a final example, let's take a look at the worst leveraged share of life, BOIL:
Trainwreck that one. Stay far away from it!
The chart:
The chart will show you the drift (money value over time) and the variance (% variance between the expected and actual returns) over time. From here, you can ascertain the general length you feel comfortable holding a leveraged share. In general, for most stable shares, <= 50 trading days tends to be the sweet spot, but always check the chart.
There are also options to plot the variances and the drifts so you can see them visually.
And that is the indicator! Kind of boring, but there are absolutely 0 resources out there for doing this job, so hopefully you see the use for it!
Safe trades everyone!
Stock WatchOverview
Watch list are very common in trading, but most of them simply provide the means of tracking a list of symbols and their current price. Then, you click through the list and perform some additional analysis individually from a chart setup. What this indicator is designed to do is provide a watch list that employs a high/low price range analysis in a table view across multiple time ranges for a much faster analysis of the symbols you are watching.
Discussion
The concept of this Stock Watch indicator is best understood when you think in terms of a 52 Week Range indication on many financial web sites. Taken a given symbol, what is the high and the low over a 52 week range and then determine where current price is within that range from a percentage perspective between 0% and 100%.
With this concept in mind, let's see how this Stock Watch indicator is meant to benefit.
There are four different H/L ranges relative to the chart's setting and a Scope property. Let's use a three month (3M) chart as our example and set the indicator's Scope = 4. A 3M chart provides three months of data in a single candle, now when we set the Scope = 4 we are stating that 1X is going to look over four candles for the high/low range.
The Scope property is used to determine how many candles it is to scan to determine the high/low range for the corresponding 1X, 3X, 5X and 10X periods. This is how different time ranges are put into perspective. Using a 3M chart with Scope = 4 would represent the following time windows:
- 1X = 3M * 4 is a 12 Months or 1 Year High/Low Range
- 3X = 3M * 4 * 3 is a 36 Months or 3 Years High/Low Range
- 5X = 3M * 4 * 5 is a 60 Months or 5 Years High/Low Range
- 10X = 3M * 4 * 10 is a 120 Months or 10 Years High/Low Range.
With these calculations, the indicator then determines where current price is within each of these High/Low ranges from a percentage perspective between 0% and 100%.
Once the 0% to 100% value is calculated, it then will shade the value according to a color gradient from red to green (or any other two colors you set the indictor to). This color shading really helps to interpret current price quickly.
The greater power to this range and color shading comes when you are able to see where price is according to price history across the multiple time windows. In this example, there is quick analysis across 1 Year, 3 Year, 5 Year and 10 Year windows.
Now let's further improve this quick analysis over 15 different stocks for which the indicator allows you to watch up to at any one time.
For value traders this is huge, because we're always looking for the bargains and we wait for price to be in the value range. Using this indicator helps to instantly see if price has entered a value range before we decide to do further analysis with other charting and fundamental tools.
The Code
The heart of all this is really very simple as you can see in the following code snippet. We're simply looking for the highest high and lowest low across the different scopes and calculating the percentage of the range where current price is for each symbol being watched.
scope = baseScope
watch1X = math.round(((watchClose - ta.lowest(watchLow, scope)) / (ta.highest(watchHigh, scope) - ta.lowest(watchLow, scope))) * 100, 0)
table.cell(tblWatch, columnId, 2, str.format("{0, number, #}%", watch1X), text_size = size.small, text_color = colorText, bgcolor = getBackColor(watch1X))
//3X Lookback
scope := baseScope * 3
watch3X = math.round(((watchClose - ta.lowest(watchLow, scope)) / (ta.highest(watchHigh, scope) - ta.lowest(watchLow, scope))) * 100, 0)
table.cell(tblWatch, columnId, 3, str.format("{0, number, #}%", watch3X), text_size = size.small, text_color = colorText, bgcolor = getBackColor(watch3X))
Conclusion
The example I've laid out here are for large time windows, because I'm a long term investor. However, keep in mind that this can work on any chart setting, you just need to remember that your chart's time period and scope work together to determine what 1X, 3X, 5X and 10X represent.
Let me try and give you one last scenario on this. Consider your chart is set for a 60 minute chart, meaning each candle represents 60 minutes of time and you set the Stock Watch indicator to a scope = 4. These settings would now represent the following and you would be watching up to 15 different stocks across these windows at one time.
1X = 60 minutes * 4 is 240 minutes or 4 hours of time.
3X = 60 minutes * 4 * 3 = 720 minutes or 12 hours of time.
5X = 60 minutes * 4 * 5 = 1200 minutes or 20 hours of time.
10X = 60 minutes * 4 * 10 = 2400 minutes or 40 hours of time.
I hope you find value in my contribution to the cause of trading, and if you have any comments or critiques, I would love to here from you in the comments.
Christmas Toolkit [LuxAlgo]It's that time of the year... and what would be more appropriate than displaying Christmas-themed elements on your chart?
The Christmas Toolkit displays a tree containing elements affected by various technical indicators. If you're lucky, you just might also find a precious reindeer trotting toward the tree, how fancy!
🔶 USAGE
Each of the 7 X-mas balls is associated with a specific condition.
Each ball has a color indicating:
lime: very bullish
green: bullish
blue: holding the same position or sideline
red: bearish
darkRed: very bearish
From top to bottom:
🔹 RSI (length 14)
rsi < 20 - lime (+2 points)
rsi < 30 - green (+1 point)
rsi > 80 - darkRed (-2 points)
rsi > 70 - red (-1 point)
else - blue
🔹 Stoch (length 14)
stoch < 20 - lime (+2 points)
stoch < 30 - green (+1 point)
stoch > 80 - darkRed (-2 points)
stoch > 70 - red (-1 point)
else - blue
🔹 close vs. ema (length 20)
close > ema 20 - green (+1 point)
else - red (-1 point)
🔹 ema (length 20)
ema 20 rises - green (+1 point)
else - red (-1 point)
🔹 ema (length 50)
ema 50 rises - green (+1 point)
else - red (-1 point)
🔹 ema (length 100)
ema 100 rises - green (+1 point)
else - red (-1 point)
🔹 ema (length 200)
ema 200 rises - green (+1 point)
else - red (-1 point)
The above information can also be found on the right side of the tree.
You'll see the conditions associated with the specific X-mas ball and the meaning of color changes. This can also be visualized by hovering over the labels.
All values are added together, this result is used to color the star at the top of the tree, with a specific color indicating:
lime: very bullish (> 6 points)
green: bullish (6 points)
blue: holding the same position or sideline
red: bearish (-6 points)
darkRed: very bearish (< -6 points)
Switches to green/lime or red/dark red can be seen by the fallen stars at the bottom.
The Last Switch indicates the latest green/lime or red/dark red color (not blue)
🔶 ANIMATION
Randomly moving snowflakes are added to give it a wintry character.
There are also randomly moving stars in the tree.
Garland rotations, style, and color can be adjusted, together with the width and offset of the tree, put your tree anywhere on your chart!
Disabling the "static tree" setting will make the needles 'move'.
Have you happened to see the precious reindeer on the right? This proud reindeer moves towards the most recent candle. Who knows what this reindeer might be bringing to the tree?
🔶 SETTINGS
Width: Width of tree.
Offset: Offset of the tree.
Garland rotations: Amount of rotations, a high number gives other styles.
Color/Style: sets the color & style of garland stars.
Needles: sets the needle color.
Static Tree: Allows the tree needles to 'move' with each tick.
Reindeer Speed: Controls how fast the deer moves toward the most recent bar.
🔶 MESSAGE FROM THE LUXALGO TEAM
It has been an honor to contribute to the TradingView community and we are always so happy to see your supportive messages on our scripts.
We have posted a total of 78 script publications this year, which is no small feat & was only possible thanks to our team of Wizard developers @alexgrover + @dgtrd + @fikira , the development team behind Pine Script, and of course to the support of our legendary community.
Happy Holidays to you all, and we'll see ya next year! ☃️
Custom RSI with RMA SmoothingCustom RSI with RMA Smoothing is smoothing the classic Relative Strength Index to enhance the effectiveness of using the RSI for trend-following through noise reduction.
Principle:
1. RSI is smoothed by the Rolling Moving Average (RMA) and averaged Gains & Losses instead of the classic RSI calculation.
2. A RMA is plotted over the RSI where the crossovers can be entry and exit points.
How is RSI smoothed by the RMA:
1. Outside the common price sources a few new options like hhhlc or hlcc can be chosen where the emphasis is more on the high or the close of the chosen period.
2. Calculation of Price Change: After selecting the price source, the indicator calculates the price change by subtracting the previous period's price from the current price.
3. RMA Smoothing of Price Change: The key step in smoothing the RSI is the application of the Running Moving Average (RMA) to the price change. The length of this RMA is set by the user and determines the extent of smoothing. RMA is a type of moving average that gives more weight to recent data points, making it more responsive to new information while still smoothing out short-term fluctuations.
4. Determining Gains and Losses: The smoothed price change is then used to calculate the gains and losses for each period. Gains are considered when the smoothed price change is positive, and losses when it is negative.
5. Averaging Gains and Losses: These gains and losses are further smoothed by calculating their respective RMAs over the user-defined RSI length. This step is crucial as it dampens the impact of short-term price spikes and drops, giving a more stable and reliable measure of price momentum.
6. RSI Calculation: The standard RSI formula (100 - ) is then applied to these smoothed values. This results in the initial RSI value, which is already more stable than a typical RSI due to the previous smoothing steps.
7. Final RMA Smoothing of RSI: In a final layer of refinement, the RSI itself is smoothed using another RMA, over a length specified by the user. This additional smoothing further reduces the impact of short-term volatility and sharp price movements, providing a more coherent and interpretable RSI line.
TSI Market Timer + Volatility MeterThis is the TSI Market Timer. It is years in the making and it is comprised of four indicators in one. The stock (or source) is run through an indicator called the True Strength Indicator with settings(5,15) , then the TSI is run on both the Index(SPY) by default and what I call a Trigger line which is basically the TSI applied to the DXY (US Dollar Index).
Midline Volatility Indicator:
Lastly, we have a volatility indicator on the midline. The colors of the midline indicate levels of volatility. For the lowest volatility in the last 100 days, the dot turns dark blue. For the lowest volatility in 30 days, the dot turns aqua. For regular volatility, it remains orange. And last, for higher volatility of the last 100 days, it turns red. These are more or less arbitrary but they do come in handy.
Settings for Green/Red Shading:
Next on the indicator are the settings. You can toggle a color change between the stock/source and the index(spy). If the stock/source is greater than the index, it will color the area in between a green and if it is below the index, it will be red.
There is also a toggle for the stock/source and the trigger/DXY. This will also show green when the stock is above the trigger and red if it is below the trigger.
By turning on both of these, you get light green and dark green areas as well as red and darker red areas. The lighter green represent when the stock is above both the index and the trigger and conversely for the red areas.
Settings for vertical line crossings:
When the stock crosses the trigger/dxy line, it shows a green vertical line signal. When the stock crosses below the trigger/dxy, a red vertical line is shown.
You can turn these off by toggling them in the settings.
Stacked Condition:
Lastly, we have a "stacked condition" which shows up as a white triangle at the bottom when the condition of the stock being above the index and the trigger below the zero line.
New Highs:
If you see the stock line turn lime green, this indicates a new high was reached for the last 255 days/periods. This is like a new 52 week high signal.
Note:
This indicator is made mostly for the stock market. It may work ok during the week for crypto but using the trigger/dxy and index lines on the weekends doesn't work too well as they will be flat.
Also note that this indicator is not a recommendation to buy or sell any stock/instrument. It is only a study of market conditions. Any analysis should be followed up with volume analysis or other confirming indicators.
[KVA] Extremes ProfilerExtremes Profiler is a specialized indicator crafted for traders focusing on the relationship between price extremes and moving averages. This tool offers a comprehensive perspective on price dynamics by quantifying and visualizing significant distances of current prices from various moving averages. It effectively highlights the top extremes in market movements, providing key insights into price extremities relative to these averages. The indicator's ability to analyze and display these distances makes it a valuable tool for understanding market trends and potential turning points. Traders can leverage the Extremes Profiler to gain a deeper understanding of how prices behave in relation to commonly watched moving averages, thus aiding in making informed trading decisions
Key Features :
Extensive MA Analysis : Tracks the price distance from multiple moving averages including EMA, SMA, WMA, RMA, and HMA.
Top 50 (100) Distance Metrics : Highlights the 50 (100)greatest (highest or lowest) distances from each selected MA, pinpointing significant market deviations.
Customizable Periods : Offers flexibility with adjustable periods to align with diverse trading strategies.
Comprehensive View : Switch between timeframes for a well-rounded understanding of short-term fluctuations and long-term market trends.
Cross-Asset Comparison : Utilize the indicator to compare different assets, gaining insights into the relative dynamics and volatility of various markets. By analyzing multiple assets, traders can discern broader market trends and better understand asset-specific behaviors.
Customizable Display : Users can adjust the periods and number of results to suit their analytical needs.
savitzkyGolay, KAMA, HPOverview
This trading indicator integrates three distinct analytical tools: the Savitzky-Golay Filter, Kaufman Adaptive Moving Average (KAMA), and Hodrick-Prescott (HP) Filter. It is designed to provide a comprehensive analysis of market trends and potential trading signals.
Components
Hodrick-Prescott (HP) Filter
Purpose: Smooths out the price data to identify the underlying trend.
Parameters: Lambda: Controls the smoothness. Range: 50 to 1600.
Impact of Parameters:
Increasing Lambda: This makes the trend line more responsive to short-term market fluctuations, suitable for short-term analysis. A higher Lambda value decreases the degree of smoothing, making the trend line follow recent market movements more closely.
Decreasing Lambda: A lower Lambda value makes the trend line smoother and less responsive to short-term market fluctuations, ideal for longer-term trend analysis. Decreasing Lambda increases the degree of smoothing, thereby filtering out minor market movements and focusing more on the long-term trend.
Kaufman Adaptive Moving Average (KAMA):
Purpose: An adaptive moving average that adjusts to price volatility.
Parameters: Length, Fast Length, Slow Length: Define the sensitivity and adaptiveness of KAMA.
Impact of Parameters:
Adjusting Length affects the base period for efficiency ratio, altering the overall sensitivity.
Fast Length and Slow Length control the speed of KAMA’s adaptation. A smaller Fast Length makes KAMA more sensitive to price changes, while a larger Slow Length makes it less sensitive.
Savitzky-Golay Filter:
Purpose: Smooths the price data using polynomial regression.
Parameters: Window Size: Determines the size of the moving window (7, 9, 11, 15, 21).
Impact of Parameters:
A larger Window Size results in a smoother curve, which is more effective for identifying long-term trends but can delay reaction to recent market changes.
A smaller Window Size makes the curve more responsive to short-term price movements, suitable for short-term trading strategies.
General Impact of Parameters
Adjusting these parameters can significantly alter the signals generated by the indicator. Users should fine-tune these settings based on their trading style, the characteristics of the traded asset, and market conditions to optimize the indicator's performance.
Signal Logic
Buy Signal: The trend from the HP filter is below both the KAMA and the Savitzky-Golay SMA, and none of these indicators are flat.
Sell Signal: The trend from the HP filter is above both the KAMA and the Savitzky-Golay SMA, and none of these indicators are flat.
Usage
Due to the combination of smoothing algorithms and adaptability, this indicator is highly effective at identifying emerging trends for both initiating long and short positions.
IMPORTANT : Although the code and user settings incorporate measures to limit false signals due to lateral (sideways) movement, they do not completely eliminate such occurrences. Users are strongly advised to avoid signals that emerge during simultaneous lateral movements of all three indicators.
Despite the indicator's success in historical data analysis using its signals alone, it is highly recommended to use this code in combination with other indicators, patterns, and zones. This is particularly important for determining exit points from positions, which can significantly enhance trading results.
Limitations and Recommendations
The indicator has shown excellent performance on the weekly time frame (TF) with the following settings:
Savitzky-Golay (SG): 11
Hodrick-Prescott (HP): 100
Kaufman Adaptive Moving Average (KAMA): 20, 2, 30
For the monthly TF, the recommended settings are:
SG: 15
HP: 100
KAMA: 30, 2, 35
Note: The monthly TF is quite variable. With these settings, there may be fewer signals, but they tend to be more relevant for long-term investors. Based on a sample of 40 different stocks from various countries and sectors, most exhibited an average trade return in the thousands of percent.
It's important to note that while these settings have been successful in past performance, market conditions vary and past performance is not indicative of future results. Users are encouraged to experiment with these settings and adjust them according to their individual needs and market analysis.
As this is my first developed trading indicator, I am very open to and appreciative of any suggestions or comments. Your feedback is invaluable in helping me refine and improve this tool. Please feel free to share your experiences, insights, or any recommendations you may have.
Klinger Oscillator AdvancedThe Klinger Oscillator is not fully implemented in Tradeview. While the description at de.tradingview.com is complete, the implementation is limited to the pure current volume movement. This results in no difference compared to the On Balance Volume indicator.
However, Klinger's goal was to incorporate the trend as volume force in its strength and duration into the calculation. The expression ((V x x T x 100)) for volume force only makes sense as an absolute value, which should probably be expressed as ((V x abs(2 x ((dm/cm) - 1)) x T x 100)). Additionally, there is a need to handle the theoretical possibility of cm == 0.
Since, in general, significantly more trading volume occurs at the closing price than during the day, an additional parameter for weighting the closing price is implemented. In intraday charts, considering the closing price, in my opinion, does not make sense.
The TradeView implementation is displayed on the chart for comparison. Particularly in the analysis of divergence, significant deviations become apparent.
[KVA]K Stochastic IndicatorOriginal Stochastic Oscillator Formula:
%K=(C−Lowest Low)/(Highest High−Lowest Low)×100
Lowest Low refers to the lowest low of the past n periods.
Highest High refers to the highest high of the past n periods.
K Stochastic Indicator Formula:
%K=(Source−Lowest Source)/(Highest Source−Lowest Source)×100
Lowest Source refers to the lowest value of the chosen source over the past length periods.
Highest Source refers to the highest value of the chosen source over the past length periods.
Key Difference :
The original formula calculates %K using the absolute highest high and lowest low of the price over the past n periods.
The K Stochastic formula calculates %K using the highest and lowest values of a chosen source (which could be the close, open, high, or low) over the specified length periods.
So, if _src is set to something other than the high for the Highest Source or something other than the low for the Lowest Source, the K Stochastic will yield different results compared to the original formula which strictly uses the highest high and the lowest low of the price.
Impact on Traders :
Flexibility in Price Source :
By allowing the source (_src) to be customizable, traders can apply the Stochastic calculation to different price points (e.g., open, high, low, close, or even an average of these). This could provide a different perspective on market momentum and potentially offer signals that are more aligned with a trader's specific strategy.
Sensitivity to Price Action :
Changing the source from high/low to potentially less extreme values (like close or open) could result in a less volatile oscillator, smoothing out some of the extreme peaks and troughs and possibly offering a more filtered view of market conditions.
Customization of Periods :
The ability to adjust the length period offers traders the opportunity to fine-tune the sensitivity of the indicator to match their trading horizon. Shorter periods may provide earlier signals, while longer periods could filter out market noise.
Possibility of Applying the Indicator on Other Indicators :
Layered Technical Analysis :
The K Stochastic can be applied to other indicators, not just price. For example, it could be applied to a moving average to analyze its momentum or to indicators like RSI or MACD, offering a meta-analysis that studies the oscillator's behavior of other technical tools.
Creation of Composite Indicator s:
By applying the K Stochastic logic to other indicators, traders could create composite indicators that blend the characteristics of multiple indicators, potentially leading to unique signals that could offer an edge in certain market conditions.
Enhanced Signal Interpretation :
When applied to other indicators, the K Stochastic can help in identifying overbought or oversold conditions within those indicators, offering a different dimension to the interpretation of their output.
Overall Implications :
The KStochastic Indicator's modifications could lead to a more tailored application, giving traders the ability to adapt the tool to their specific trading style and analysis preferences.
By being applicable to other indicators, it broadens the scope of stochastic analysis beyond price action, potentially offering innovative ways to interpret data and make trading decisions.
The changes might also influence the trading signals, either by smoothing the oscillator's output to reduce noise or by altering the sensitivity to generate more or fewer signal
Including the additional %F line, which is unique to the K Stochastic Indicator, further expands the potential impacts and applications for traders:
Impact on Traders with the %F Line:
Triple Smoothing :
The %F line introduces a third level of smoothing, which could help in identifying longer-term trends and filtering out short-term fluctuations. This could be particularly useful for traders looking to avoid whipsaws and focus on more sustained movements.
Potential for Enhanced Confirmation :
The %F line might be used as a confirmation signal. For instance, if all three lines (%K, %D, and %F) are in agreement, a trader might consider this as a stronger signal to buy or sell, as opposed to when only the traditional two lines (%K and %D) are used.
Risk Management:
The additional line could be utilized for more sophisticated risk management strategies, where a trader might decide to scale in or out of positions based on the convergence or divergence of these lines.
Possibility of Applying the Indicator on Other Indicators with the %F Line:
Depth of Analysis :
When applied to other indicators, the %F line can provide an even deeper layer of analysis, perhaps identifying macro trends within the indicator it is applied to, which could go unnoticed with just the traditional two-line approach.
Refined Signal Strength Assessment :
The strength of signals from other indicators could be assessed by the position and direction of the %F line, providing an additional filter to evaluate the robustness of buy or sell signals.
Overall Implications with the %F Line :
The inclusion of the %F line in the K Stochastic Indicator enhances its utility as a tool for trend analysis and signal confirmation. It allows traders to potentially identify and act on more reliable trading opportunities.
This feature can enrich the trader's toolkit by providing a nuanced view of momentum and trend strength, which can be particularly valuable in volatile or choppy markets.
For those applying the K Stochastic to other indicators, the %F line could be integral in creating a multi-tiered analysis strategy, potentially leading to more sophisticated interpretations and decisions.
The presence of the %F line adds a dimension of depth to the analysis possible with the K Stochastic Indicator, making it a versatile tool that could be tailored to a variety of trading styles and objectives. However, as with any indicator, the additional complexity requires careful study and back-testing to ensure its signals are understood and actionable within the context of a comprehensive trading plan.
Liquidations Meter [LuxAlgo]The Liquidation Meter aims to gauge the momentum of the bar, identify the strength of the bulls and bears, and more importantly identify probable exhaustion/reversals by measuring probable liquidations.
🔶 USAGE
This tool includes many features related to the concept of liquidation. The two core ones are the liquidation meter and liquidation price calculator, highlighted below.
🔹 Liquidation Meter
The liquidation meter presents liquidations on the price chart by measuring the highest leverage value of longs and shorts that have been potentially liquidated on the last chart bar, hence allowing traders to:
gauge the momentum of the bar.
identify the strength of the bulls and bears.
identify probable reversal/exhaustion points.
Liquidation of low-leveraged positions can be indicative of exhaustion.
🔹 Liquidation Price Calculator
A liquidation price calculator might come in handy when you need to calculate at what price level your leveraged position in Crypto, Forex, Stocks, or any other asset class gets liquidated to add a protective stop to mitigate risk. Monitoring an open position gets easier if the trader can calculate the total risk in order for them to choose the right amount of margin and leverage.
Liquidation price is the distance from the trader's entry price to the price where trader's leveraged position gets liquidated due to a loss. As the leverage is increased, the distance from trader's entry price to the liquidation price shrinks.
While you have one or several trades open you can quickly check their liquidation levels and determine which one of the trades is closest to their liquidation price.
If you are a day trader that uses leverage and you want to know which trade has the best outlook you can calculate the liquidation price to see which one of the trades looks best.
🔹 Dashboard
The bar statistics option enables measuring and presenting trading activity, volatility, and probable liquidations for the last chart bar.
🔶 DETAILS
It's important to note that liquidation price calculator tool uses a formula to calculate the liquidation price based on the entry price + leverage ratio.
Other factors such as leveraged fees, position size, and other interest payments have been excluded since they are variables that don’t directly affect the level of liquidation of a leveraged position.
The calculator also assumes that traders are using an isolated margin for one single position and does not take into consideration the additional margin they might have in their account.
🔹Liquidation price formula
the liquidation distance in percentage = 100 / leverage ratio
the liquidation distance in price = current asset price x the liquidation distance in percentage
the liquidation price (longs) = current asset price – the liquidation distance in price
the liquidation price (shorts) = current asset price + the liquidation distance in price
or simply
the liquidation price (longs) = entry price * (1 – 1 / leverage ratio)
the liquidation price (shorts) = entry price * (1 + 1 / leverage ratio)
Example:
Let’s say that you are trading a leverage ratio of 1:20. The first step is to calculate the distance to your liquidation point in percentage.
the liquidation distance in percentage = 100 / 20 = 5%
Now you know that your liquidation price is 5% away from your entry price. Let's calculate 5% below and above the entry price of the asset you are currently trading. As an example, we assume that you are trading bitcoin which is currently priced at $35000.
the liquidation distance in price = $35000 x 0.05 = $1750
Finally, calculate liquidation prices.
the liquidation price (longs) = $35000 – $1750 = $33250
the liquidation price (short) = $35000 + $1750 = $36750
In this example, short liquidation price is $36750 and long liquidation price is $33250.
🔹How leverage ratio affects the liquidation price
The entry price is the starting point of the calculation and it is from here that the liquidation price is calculated, where the leverage ratio has a direct impact on the liquidation price since the more you borrow the less “wiggle-room” your trade has.
An increase in leverage will subsequently reduce the distance to full liquidation. On the contrary, choosing a lower leverage ratio will give the position more room to move on.
🔶 SETTINGS
🔹Liquidations Meter
Base Price: The option where to set the reference/base price.
🔹Liquidation Price Calculator
Liquidation Price Calculator: Toggles the visibility of the calculator. Details and assumptions made during the calculations are stated in the tooltip of the option.
Entry Price: The option where to set the entry price, a value of 0 will use the current closing price. Details are given in the tooltip of the option.
Leverage: The option where to set the leverage value.
Show Calculated Liquidation Prices on the Chart: Toggles the visibility of the liquidation prices on the price chart.
🔹Dashboard
Show Bar Statistics: Toggles the visibility of the last bar statistics.
🔹Others
Liquidations Meter Text Size: Liquidations Meter text size.
Liquidations Meter Offset: Liquidations Meter offset.
Dashboard/Calculator Placement: Dashboard/calculator position on the chart.
Dashboard/Calculator Text Size: Dashboard text size.
🔶 RELATED SCRIPTS
Here are some of the scripts that are related to the liquidation and liquidity concept, for more and other conceptual scripts you are kindly invited to visit LuxAlgo-Scripts .
Liquidation-Levels
Liquidations-Real-Time
Buyside-Sellside-Liquidity
Cryptocurrency Cointegration Matrix (SpiritualHealer117)This indicator plots a cointegration matrix for the pairings of 100 cryptocurrencies. The matrix is populated with ADF t-stats (from an ADF-test with 1 lag). An ADF-test (Augmented Dickey-Fuller test) tests the null hypothesis that an AR process has a unit root. If rejected, the alternative hypothesis is usually that the AR process is either stationary or trend-stationary. This model extends upon Lejmer's Cointegration Matrix for forex by enabling the indicator to use cryptocurrency pairs and allows for significantly more pairs to be analyzed using the group selection feature. This indicator arose from collaboration with TradingView user CryptoJuju.
This indicator runs an ADF-test on the residuals (spread) of each pairing (i.e. a cointegration test). It tests if there is a unit root in the spread between the two assets of a pairing. If there is a unit root in the spread, it means the spread varies randomly over time, and any mean reversion in the spread is very hard to predict. By contrast, if a unit root does not exist, the spread (distance between the assets) should remain more or less constant over time, or rise/fall in close to the same rate over time. The more negative the number from an ADF-test, the stronger the rejection of the idea that the spread has a unit root. In statistics, there are different levels which correspond with the confidence level of the test. For this indicator, -3.238 equals a confidence level of 90%, -3.589 equals a confidence level of 95% and -4.375 equals a confidence level of 99% that there is not a unit root. So the colors are based on the confidence level of the test statistic (the t-stat, i.e. the number of the pairing in the matrix). So if the number is greater than -3.238 it is green, if it's between -3.238 and -3.589 it's yellow, if it's between -3.589 and -4.375 it's orange, and if its lower than -4.375 it's red.
There are multiple ways to interpret the results. A strong rejection of the presence of a unit root (i.e. a value of -4.375 or below) is not a guarantee that there is no unit root, or that any of the two alternative hypotheses (that the spread is stationary or trend-stationary) are correct. It only means that in 99% of the cases, if the spread is an AR process, the test is right, and there is no unit root in the spread. Therefore, the results of this test is no guarantee that the result proves one of the alternative solutions. Green therefore means that a unit root cannot be ruled out (which can be interpreted as "the two cryptocurrencies probably don't move together over time"), and red means that a unit root is likely not present (which can be interpreted as "the two cryptocurrencies may move together over time").
One possible way to use this indicator is to make sure you don't trade two pairs that move together at the same time. So basically the idea is that if you already have a trade open in one of the currency pairs of the pairing, only enter a trade in the other currency pair of that pairing if the color is green, or you may be doubling your risk. Alternatively, you could implement this indicator into a pairs trading system, such as a simple strategy where you buy the spread between two cryptocurrencies with a red result when the spread's value drops one standard deviation away from its moving average, and conversely sell when it moves up one standard deviation above the moving average. However, this strategy is not guaranteed to work, since historical data does not guarantee the future.
Specific to this indicator, there are 100 different cryptocurrency tickers which are included in the matrix, and the cointegration matrices between all the tickers can be checked by switching asset group 1 and asset group 2 to different asset groups. The ADF test is computed using a specified length, and if there is insufficient data for the length, the test produces a grayed out box.
NOTE: The indicator can take a while to load since it computes the value of 400 ADF tests each time it is run.
Statistics TableStrategy Statistics
This library will add a table with statistics from your strategy. With this library, you won't have to switch to your strategy tester tab to view your results and positions.
Usage:
You can choose whether to set the table by input fields by adding the below code to your strategy or replace the parameters with the ones you would like to use manually.
// Statistics table options.
statistics_table_enabled = input.string(title='Show a table with statistics', defval='YES', options= , group='STATISTICS')
statistics_table_position = input.string(title='Position', defval='RIGHT', options= , group='STATISTICS')
statistics_table_margin = input.int(title='Table Margin', defval=10, minval=0, maxval=100, step=1, group='STATISTICS')
statistics_table_transparency = input.int(title='Cell Transparency', defval=20, minval=1, maxval=100, step=1, group='STATISTICS')
statistics_table_text_color = input.color(title='Text Color', defval=color.new(color.white, 0), group='STATISTICS')
statistics_table_title_cell_color = input.color(title='Title Cell Color', defval=color.new(color.gray, 80), group='STATISTICS')
statistics_table_cell_color = input.color(title='Cell Color', defval=color.new(color.purple, 0), group='STATISTICS')
// Statistics table init.
statistics.table(strategy.initial_capital, close, statistics_table_enabled, statistics_table_position, statistics_table_margin, statistics_table_transparency, statistics_table_text_color, statistics_table_title_cell_color, statistics_table_cell_color)
Sample:
If you are interested in the strategy used for this statistics table, you can browse the strategies on my profile.
Oscillator Volume Profile [Trendoscope®]The Oscillator Volume Profile indicator is designed to construct a volume profile based on predefined oscillator levels. It integrates volume data with oscillator readings to offer a unique perspective on market dynamics.
🎲 Selectable Oscillators:
Users can select from an array of oscillator options for the basis of the volume profile, including:
Relative Strength Index (RSI)
Chande Momentum Oscillator (CMO)
Center of Gravity (COG)
Money Flow Index (MFI)
Rate of Change (ROC)
Commodity Channel Index (CCI)
Stochastic Oscillator (Stoch)
True Strength Index (TSI)
Williams %R (WPR)
The length parameters - Length, Fast Length, Slow Length allows users to define the period over which the chosen oscillator is calculated, tailoring the sensitivity of the indicator to their trading strategy.
🎲 Dynamic Overbought/Oversold Ranges:
This indicator enhances traditional concepts by introducing dynamic overbought and oversold levels. These adaptable thresholds are calculated using various methods, including:
🎯 Highest/Lowest Range Method : This method establishes the range based on the highest and lowest values of the oscillator within the last N bars.
🎯 Moving Average Range Method : The range is derived from a moving average of the oscillator, providing a smoothed threshold that reflects more recent market conditions.
In addition to these methods, the indicator incorporates a unique 'Sticky Border' feature:
🎯 Sticky Border: With this option enabled, the dynamic ranges maintain their levels until the oscillator breaks out of the range. Once a breakout occurs, the levels are recalculated and updated. This mechanism ensures that the borders remain consistent and relevant, only adjusting to significant market movements that warrant a recalculation.
Users can select their preferred method for determining dynamic ranges, allowing for a customized approach that aligns with their analysis and trading strategy. The sticky border feature further refines this functionality, offering continuity until a decisive market move occurs.
🎲 Volume Profile Calculation Parameters:
🎯 Trend Filter: The indicator provides a versatile trend filter with four selectable options:
Uptrend: The volume profile is calculated when the oscillator indicates an uptrend.
Downtrend: The volume profile is calculated when the oscillator indicates a downtrend.
Any: The volume profile is calculated regardless of the trend.
External: Users can input values from an external indicator. The volume profile is then calculated only when the external indicator's value is non-zero, integrating external analysis into the volume profile construction.
🎯 Precision: Users have the option to define the precision for calculating the volume profile, which is crucial due to the varying scales of different oscillators (e.g., some oscillators range from 0 to 100, while others from -1 to 1). Selecting an appropriate precision ensures that the volume profile is accurately aligned with the minimal price range significant to the chosen oscillator. This setting requires user intervention for optimal configuration, as automatic calculation is not feasible due to the diverse nature of oscillator ranges.
🎯 Number of Bars: Users can select a specific number of bars for volume profile calculation, or opt to include all available historical bars for a comprehensive profile.
🎲 Selecting the right precision:
Users must select the right precision based on their choice of indicator. For example, RSI values range from 0-100. Hence, the default precision of 1 work fine on RSI as the volume profiles are plotted from 0 to 100 at the interval of 0.1
But, the default precision of 1 will not be ok on TSI because TSI values range from -1 to 1. Hence, using 1 as precision will result in very less volume profile lines as shown below.
Due to this, it is necessary to increase the precision for oscillators such as TSI where the range between highest and lowest value is far less. Once we set the precision to 2, we can see more appropriate volume profile division.
🎲 Note of thanks:
This publication uses polyline feature for drawing volume profiles. The advantage of using polyline is that we can overcome max 500 lines issue that we face by using the regular line objects. More details of polyline can be found in the tradingview blog post
Further, using polyline for display of volume profiles is inspired by the publications of fikira and KioseffTrading
MA + MACD alert TrendsThis is a strategy/combination of warning indicators using 6MA+MACD.
The strategy details are as follows: This is a simple warning strategy created so that we don't have to monitor the candlestick chart too often.
Note: This isn't an entry strategy; it's a signaling strategy for upcoming trends. For maximum efficiency, we should incorporate more formulas into the command. In the case below, I use Fibonacci to enter the command.
This strategy setting works for a 15-minute time frame, but it can still work for different time frames.
It has been working well with Gold and USOIL for the last two years, as well as with currency pairs like EURUSD and many others.
Components:
EMA100 + EMA200 + MA400 + MA800
MACD (timeframe greater than 1 timeframe)
Fibonacci retreat.
Uptrend alert:
Candles on both EMAs (100-200) + 2 SMAs (400-800)
In the previous 80 candles:
EMA100 cross up to EMA200
At the same time, the MACD cross up 0.
The uptrend warning will trigger when EMA6 cuts down to MA10. That's when the price creates the top and we'll wait for the market to go back to the Fibonacci threshold of 0.618 and start buying (or wait for markets to break up the trendline to buy).
Downtrend alert:
Candles are below both EMAs ( 100-200 ) + 2 SMAs ( 400-800 )
In the previous 80 candles:
EMA100 cross down to EMA200
At the same time, the MACD cross down zero.
The downtrend warning will trigger when EMA6 cuts to MA10. That's when the price creates a bottom and we'll wait for the market to go back to the Fibonacci threshold of 0.618 and start selling (or wait for the market to break down the trendline to sell).
Recommended RR: 1:1
If you have any questions please let me know!
Fiboborsa+BistTitle: "Fiboborsa+Bist Indicator for TradingView"
Description: The "Fiboborsa+Bist" indicator is a powerful tool designed for TradingView users. This indicator offers a comprehensive set of technical indicators to assist you in your technical analysis and trading decisions.
Features:
Simple Moving Averages (SMA): You can enable or disable SMA with different periods (20, 50, 100, 200) to observe different timeframes and trends.
SMA Strategy: Use SMA crossovers to determine trends. Watch for the 20-period SMA crossing above the 50-period SMA for a bullish signal. For a bearish signal, observe the 50-period SMA crossing below the 100-period SMA.
Exponential Moving Averages (EMA): Similar to SMA, you can enable or disable EMA with different periods (5, 8, 14, 21, 34, 55, 89, 144, 233) for more precise trend analysis.
EMA Strategy: Use EMA crossovers and crossunders for short-term trend changes. A buy signal may occur when the 5-period EMA crosses above the 14-period EMA, while a crossunder suggests a selling opportunity.
Weighted Moving Averages (WMA): Customize WMA settings with various periods (5, 13, 21, 34, 89, 144, 233, 377, 610, 987) to suit your trading style.
WMA Strategy: Use WMA crossovers to verify trends. When the 13-period WMA crosses above the 34-period WMA, it may indicate an uptrend.
Buy and Sell Signals: The indicator provides buy and sell signals based on EMA crossovers and crossunders. Strong signals are also highlighted.
EMA Buy and Sell Strategy: Make informed trading decisions using buy and sell signals generated by EMA crossovers and crossunders.
Ichimoku Cloud: You can enable the Ichimoku Cloud for a clear visual representation of support and resistance levels.
Ichimoku Strategy: Use the Ichimoku Cloud to determine trend direction. Entering long positions is common when the price is above the cloud and considering short positions when it's below the cloud. Verify the trend with the Chikou Span.
Bollinger Bands: Easily visualize price volatility by enabling the Bollinger Bands feature.
Bollinger Bands Strategy: Bollinger Bands help you visualize price volatility. Look for potential reversal points when the price touches or crosses the upper or lower bands.
Use the "Fiboborsa+Bist" indicator to enhance your trading strategies and make informed decisions in the dynamic world of financial markets.
Additional Information:
Bollinger Bands: Bollinger Bands are a technical analysis tool used to monitor price volatility and determine overbought or oversold conditions. This indicator consists of three components:
Middle Moving Average (SMA): Typically, a 20-day SMA is used.
Upper Band: Calculated by adding two times the standard deviation to the SMA.
Lower Band: Calculated by subtracting two times the standard deviation from the SMA.
As the price moves between these two bands, it becomes possible to identify potential buying or selling points by comparing its height or low with these bands.
Ichimoku Cloud: The Ichimoku Cloud is a comprehensive indicator used for trend identification, defining support and resistance levels, and measuring trend strength. The Ichimoku Cloud comprises five key components:
Tenkan Sen (Conversion Line): Used to identify short-term trends.
Kijun Sen (Base Line): Used to identify medium-term trends.
Senkou Span A (Leading Span A): Calculated as (Tenkan Sen + Kijun Sen) / 2 and shows future support and resistance levels.
Senkou Span B (Leading Span B): Calculated as (highest high + lowest low) / 2 and indicates future support and resistance levels.
Chikou Span (Lagging Line): Enables tracking the price backward.
The Ichimoku Cloud interprets a price above the cloud as an uptrend and below the cloud as a downtrend. The Chikou Span assists in verifying the current trend.
ADDITIONAL STRATEGY WITH RSI AND MACD INDICATORS
**Strategy: Two-Stage Trading Strategy Using RSI, MACD, and Fiboborsa+Bist Indicators**
**Stage 1: Determining the Trend and Selecting the Trading Direction**
1. **Trend Identification with Fiboborsa+Bist Indicator:**
- Analyze the simple moving averages (SMA), exponential moving averages (EMA), and weighted moving averages (WMA) used with the Fiboborsa+Bist indicator. These indicators will provide information about the direction of the market trend.
2. **Identifying Overbought and Oversold Conditions with RSI:**
- Use the RSI indicator to identify overbought (70 and above) and oversold (30 and below) conditions. This helps in measuring the strength of the trend. If RSI enters the overbought zone, a downward correction is likely. If RSI enters the oversold zone, an upward correction is probable.
3. **Evaluating Momentum with MACD:**
- Examine price momentum using the MACD indicator. When the MACD line crosses above the signal line, it may indicate an increasing upward momentum. Conversely, a downward cross can suggest an increasing downward momentum.
**Stage 2: Generating Buy and Sell Signals**
4. **Combining RSI, MACD, and Fiboborsa+Bist Indicators:**
- To generate a buy signal, wait for RSI to move out of the oversold region into an uptrend and for the MACD line to cross above the signal line.
- To generate a sell signal, wait for RSI to move out of the overbought region into a downtrend and for the MACD line to cross below the signal line.
5. **Confirmation with Fiboborsa+Bist Indicator:**
- When you receive a buy or sell signal, use the Fiboborsa+Bist indicator to confirm the market trend. Confirming the trend can strengthen your trade signals.
6. **Setting Stop-Loss and Take-Profit Levels:**
- Remember to manage risk when opening buy or sell positions. Set stop-loss and take-profit levels to limit your risk.
7. **Monitor and Adjust Your Trades:**
- Continuously monitor your trade positions and adjust your strategy as per market conditions.
This two-stage trading strategy offers the ability to determine trends and generate trade signals using different indicators. However, every trading strategy involves risks, so risk management and practical application are essential. Also, it's recommended to test this strategy in a demo account before using it in a real trading account.
Ichimoku Oscillator With Divergences [ChartPrime]The Ichimoku Oscillator is a trading indicator designed to streamline the interpretation of Ichimoku clouds. It aims to refine and condense the complexities of the Chikou (the lag line), presenting its implications in real-time through an oscillator format, beneficial for those familiar with Ichimoku components but to have a new interpretation of their indicators.
The basics of an Ichimoku:
Conversion Line (Tenkan-Sen): It represents a midpoint of the highest and lowest prices over a specific period, usually 9 periods, reflecting short-term price movements.
Base Line (Kijun-Sen): It acts similarly to the Conversion Line but over a longer period, typically 26 periods, representing medium-term price movements.
Leading Span A & B (Kumo): Span A is the average of the Conversion Line and Base Line, and Span B is the midpoint of the highest and lowest prices over a usually longer period, typically 52 periods. Their interaction denotes trend direction, and the cloud color changes depending on whether Span A is above or below Span B, indicating bullish or bearish market conditions, respectively.
Lagging Span (Chikou Span): It is the current closing price plotted 26 periods behind, assisting in confirming the trend direction and potential momentum.
Advantage of an Oscillator:
Utilizing the oscillator format allows traders to interpret market dynamics more efficiently by visualizing the momentum and trend strength in a bounded range, enabling quick assessments of overbought or oversold conditions. Creating this oscillator provides multiple advantageous; particularly in sideway markets, helping to identify potential reversal points and offering insights on market entries and exits. When building this oscillator we've put a focus on unique interpretations such as overbought and sold areas and divergences; otherwise not found in traditional Ichimoku techniques. It is important to note these divergences are naturally not 100% real time.
When the oscillator turns green; the market is in an uptrend, red for downtrend and yellow for a transitioning market. The center line and the inner most cloud represent a balanced market state.
Key Features & Input Parameters:
Signal Source: Allows the selection of the price data source for signal generation, such as closing prices, and it’s the foundational parameter upon which the oscillator functions.
Normalization Settings: Users can select the normalization mode (“All”, “Window”, or “Disabled”), influencing how the oscillator scales its values. When enabled, it will scale from 100 to -100, allowing the user to understand better the relative positioning of price data.
Smoothing: This indicator offers advanced smoothing features, with options for additional smoothing, allowing traders to adjust the signal's sensitivity to price movements.
Kumo & Chikou Visibility: Traders can customize the visibility settings of Kumo and Chikou, tailoring the display of each component to their preference, enabling a cleaner and more intuitive view of market conditions.
Color Coding: Each component and condition, like bullish or bearish states, can be color-coded, providing visual cues to enhance the interpretability of market trends and states.
Color on Conversion: The oscillator provides an option to color the signal based on the crossover of the conversion and base lines.
Divergence: The oscillator can detect and highlight regular and hidden bullish and bearish divergences between the signal and price, aiding traders in identifying potential trend reversals or continuations.
Alerts:
The list of inbuilt alerts are provided below:
Inside Cloud: The signal line is inside the cloud.
Up Out of Cloud: The signal line crossed above the cloud.
Down Out of Cloud: The signal line crossed below the cloud.
Future Kumo Cross Bullish: The future Kumo lines have crossed in a bullish manner.
Future Kumo Cross Bearish: The future Kumo lines have crossed in a bearish manner.
Current Kumo Cross Bullish: The current Kumo lines have crossed in a bullish manner.
Current Kumo Cross Bearish: The current Kumo lines have crossed in a bearish manner.
Conversion Base Bullish: The conversion line crossed above the base line.
Conversion Base Bearish: The conversion line crossed below the base line.
Signal Bullish on Conversion Base: The signal line crossed above the maximum of conversion and base lines.
Signal Bearish on Conversion Base: The signal line crossed below the minimum of conversion and base lines.
Chikou Bullish: The Chikou line crossed above zero.
Chikou Bearish: The Chikou line crossed below zero.
Signal Over Max: The signal line crossed above the max level.
Signal Over High: The signal line crossed above the high level.
Signal Under Min: The signal line crossed below the min level.
Signal Under Low: The signal line crossed below the low level.
Chikou Over Max: The Chikou line crossed above the max level.
Chikou Over High: The Chikou line crossed above the high level.
Chikou Under Min: The Chikou line crossed below the min level.
Chikou Under Low: The Chikou line crossed below the low level.
Signal Crossover MA: The signal line crossed over the moving average.
Signal Crossunder MA: The signal line crossed under the moving average.
Regular Bullish Divergence: Regular bullish divergence detected.
Hidden Bullish Divergence: Hidden bullish divergence detected.
Regular Bearish Divergence: Regular bearish divergence detected.
Hidden Bearish Divergence: Hidden bearish divergence detected.
Bounce off of Kumo Up: Bullish Bounce off of Kumo.
Bounce off of Kumo Down: Bearish Bounce off of Kumo.
By providing a cohesive visualization of the Ichimoku elements and market momentum within a bounded range, this oscillator is a unique tool and insight into markets.
[blackcat] L3 CCI-RSI ComboCCI-RSI Combo indicator is a combination indicator that includes CCI and RSI. It uses some parameters to calculate the values of CCI and RSI, and generates corresponding charts based on these values. On the chart, when CCI exceeds 100 or falls below -100, yellow or magenta filling areas are displayed. Additionally, gradient colors are used on the RSI chart to represent different value ranges. Based on the values of CCI and RSI, buying or selling signals can be identified and "B" or "S" labels are displayed at the corresponding positions. It utilizes some technical indicators and logic to generate buying and selling signals, and displays the corresponding labels on the chart.
Here are the main parts of the code:
1. Definition of some variables:
- `N`, `M`, `N1`: Parameters used to calculate CCI and RSI.
- `xcn(cond, len)` and `xex(cond, len)`: Two functions used to calculate the number of times a condition is met.
2. Calculation of CCI (Commodity Channel Index):
- Calculate the CCI value based on the formula `(TYP - ta.sma(TYP, M)) / (0.015 * ta.stdev(TYP, M))`.
- Use the `plot()` function to plot CCI on the chart and set the color based on its value.
3. Calculation of RSI (Relative Strength Index):
- First calculate RSI1 by taking the average of positive differences between closing prices and the average of all absolute differences, and then multiplying by 100.
- Then use the ALMA function to transform RSI1 into a smoother curve.
- Use the `plot()` function to plot RSI on the chart and select gradient colors for shading based on its value.
4. Setting up the gradient color array:
- Create a color array using `array.new_color()` and add a series of color values to it.
5. Generating buying and selling signals based on conditions:
- Use logical operators and technical indicator functions to determine the conditions for buying and selling.
- Use the `label.new()` function to draw the corresponding labels on the chart to represent buying or selling signals.
