Mason’s Line IndicatorThe Macon Strategy is an idea conceived by Didier Darcet , co-founder of Gavekal Intelligence Software. Inspired by the Water Level, an instrument used by masons to check the horizontality or verticality of a wall. This method aims to measure the psychology of financial markets and determine if the market is balanced or tilting towards an unfavorable side, focusing on the behavioral risk of markets rather than economic or political factors.
The strategy examines the satisfaction and frustration of investors based on the distance between the low and high points of the market over a period of one year. Investor satisfaction is influenced by the current price of the index and the path taken to reach that price. The distance to the low point provides satisfaction, while the distance to the high point generates frustration. The balance between the two dictates investors’ desire to hold or sell their positions.
To refine the strategy, it is important to consider the opinion of a group of investors rather than just one individual. The members of a hypothetical investor club invest successively throughout the past year. The overall satisfaction of the market on a given day is a democratic expression of all participants.
If the overall satisfaction is below 50%, investors are frustrated and sell their positions. If it is above, they are satisfied and hold their positions. The position of the group of investors relative to the high and low points represents the position of the air bubble in the water level. Market performance is measured day by day based on participant satisfaction or dissatisfaction.
In conclusion, memory, emotions, and decision-making ability are closely linked, and their interaction influences investment decisions. The Macon Strategy highlights the importance of the behavioral dimension in understanding financial market dynamics. By studying investor behavior through this strategy, it is possible to better anticipate market trends and make more informed investment decisions.
Presentation of the Mason’s Line Indicator:
The main strategy of this indicator is to measure the average satisfaction of investors based on the position of an imaginary air bubble in a tube delimited by the market’s highs and lows over a given period. After calculating the satisfaction level, it is then normalized between 0 and 1, and a moving average can be used to visualize trends.
Key features:
Calculation of highs and lows over a user-defined period.
Determination of the position of the air bubble in the tube based on the closing price.
Calculation of the average satisfaction of investors over a selected period.
Normalization of the average satisfaction between 0 and 1.
Visualization of normalized or non-normalized average satisfaction levels, as well as their corresponding moving averages.
User parameters:
Period for min and max (days) : Sets the period over which highs and lows will be calculated (1 to 365 days).
Period for average satisfaction (days) : Determines the period over which the average satisfaction of investors will be calculated (1 to 365 days).
Period for SMA : Sets the period of the simple moving average used to smooth the data (1 to 1000 days).
Bubble_value : Adjustment of the air bubble value, ranging from 0 to 1, in increments of 0.025.
Normalized average satisfaction : Option to choose whether to display the normalized or non-normalized average satisfaction.
Please note that the Mason’s Line Indicator is not a guarantee of future market performance and should be used in conjunction with proper risk management. Always ensure that you have a thorough understanding of the indicator’s methodology and its limitations before making any investment decisions. Additionally, past performance is not indicative of future results.
Statistics
Kimchi Premium watchThis indicator provides easy-to-see Kimchi premium information.
It provides three pieces of information.
1. Current premium
2. The highest value of the premium over the last 240 candlesticks in the current timeframe.
3. The highest value of the premium over the last 240 candlesticks in the current timeframe.
I think this script is a very simple indicator.
It is usually recommended to get value in a large time frame.
The basic operation formula is as follows.
premium(percent) = ( BTC KRW - ( BTC USDT x USD KRW ) / ( BTC USDT x USDT USD x USD KRW )) x 100
Thank you.
Сoncentrated Market Maker Strategy by oxowlConcentrated Market Maker Strategy by oxowl. This script plots an upper and lower bound for liquidity provision, and checks for rebalancing conditions. It also includes alert conditions for when the price crosses the upper or lower bounds.
Here's an overview of the script:
It defines the input parameters: liquidity range percentage, rebalance frequency in minutes, and minimum trade size in assets.
It calculates the upper and lower bounds for liquidity provision based on the liquidity range percentage.
It initializes variables for the last rebalance time and price.
It defines a rebalance condition based on the frequency and current price within the specified range.
If the rebalance condition is met, it updates the last rebalance time and price.
It plots the upper and lower bounds on the chart as lines and adds price labels for both bounds.
It defines alert conditions for when the price crosses the upper or lower bounds.
Finally, it creates alert conditions with appropriate messages for when the price crosses the upper or lower bounds.
Concentrated liquidity is a concept often used in decentralized finance (DeFi) market-making strategies. It allows liquidity providers (LPs) to focus their liquidity within a specific price range, rather than across the entire price curve. Using an indicator with concentrated liquidity can offer several advantages:
Increased capital efficiency: Concentrated liquidity allows LPs to allocate their capital within a narrower price range. This means that the same amount of capital can generate more significant price impact and potentially higher returns compared to providing liquidity across a broader range.
Customized risk exposure: LPs can choose the price range they feel most comfortable with, allowing them to better manage their risk exposure. By selecting a range based on their market outlook, they can optimize their positions to maximize potential returns.
Adaptive strategies: Indicators that support concentrated liquidity can help traders adapt their strategies based on market conditions. For example, they can choose to provide liquidity around a stable price range during low-volatility periods or adjust their range when market conditions change.
To continue integrating this script into your trading strategy, follow these steps:
Import the script into your TradingView account. Navigate to the Pine editor, paste the code, and save it as a new script.
Apply the indicator to a trading pair chart. You can customize the input parameters (liquidity range percentage, rebalance frequency, and minimum trade size) based on your preferences and risk tolerance.
Set alerts for when the price crosses the upper or lower bounds. This will notify you when it's time to take action, such as adding or removing liquidity, or rebalancing your position.
Monitor the performance of your strategy over time. Adjust the input parameters as needed to optimize your returns and manage risk effectively.
(Optional) Integrate the script with a trading bot or automation platform. If you're using an API-based trading solution, you can incorporate the logic and conditions from the script into your bot's algorithm to automate the process of providing concentrated liquidity and rebalancing your positions.
Remember that no strategy is foolproof, and past performance is not indicative of future results. Always exercise caution when trading and carefully consider your risk tolerance.
Linear Regress on Price And VolumeLinear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship between the dependent variable and the independent variable(s) and attempts to fit a straight line that best describes the relationship.
In the context of predicting the price of a stock based on the volume, we can use linear regression to build a model that relates the price of the stock (dependent variable) to the volume (independent variable). The idea is to use lookback period to predict future prices based on the volume.
To build this indicator, we start by collecting data on the price of the stock and the volume over a selected of time or by default 21 days. We then plot the data on a scatter plot with the volume on the x-axis and the price on the y-axis. If there is a clear pattern in the data, we can fit a straight line to the data using a method called least squares regression. The line represents the best linear approximation of the relationship between the price and the volume.
Once we have the line, we can use it to make predictions. For example, if we observe a certain volume, we can use the line to estimate the corresponding price.
It's worth noting that linear regression assumes a linear relationship between the variables. In reality, the relationship between the price and the volume may be more complex, and other factors may also influence the price of the stock. Therefore, while linear regression can be a useful tool, it should be used in conjunction with other methods and should be interpreted with caution.
Bitwise, Encode, DecodeLibrary "Bitwise, Encode, Decode"
Bitwise, Encode, Decode, and more Library
docs()
Hover-Over Documentation for inside Text Editor
bAnd(a, b)
Returns the bitwise AND of two integers
Parameters:
a : `int` - The first integer
b : `int` - The second integer
Returns: `int` - The bitwise AND of the two integers
bOr(a, b)
Performs a bitwise OR operation on two integers.
Parameters:
a : `int` - The first integer.
b : `int` - The second integer.
Returns: `int` - The result of the bitwise OR operation.
bXor(a, b)
Performs a bitwise Xor operation on two integers.
Parameters:
a : `int` - The first integer.
b : `int` - The second integer.
Returns: `int` - The result of the bitwise Xor operation.
bNot(n)
Performs a bitwise NOT operation on an integer.
Parameters:
n : `int` - The integer to perform the bitwise NOT operation on.
Returns: `int` - The result of the bitwise NOT operation.
bShiftLeft(n, step)
Performs a bitwise left shift operation on an integer.
Parameters:
n : `int` - The integer to perform the bitwise left shift operation on.
step : `int` - The number of positions to shift the bits to the left.
Returns: `int` - The result of the bitwise left shift operation.
bShiftRight(n, step)
Performs a bitwise right shift operation on an integer.
Parameters:
n : `int` - The integer to perform the bitwise right shift operation on.
step : `int` - The number of bits to shift by.
Returns: `int` - The result of the bitwise right shift operation.
bRotateLeft(n, step)
Performs a bitwise right shift operation on an integer.
Parameters:
n : `int` - The int to perform the bitwise Left rotation on the bits.
step : `int` - The number of bits to shift by.
Returns: `int`- The result of the bitwise right shift operation.
bRotateRight(n, step)
Performs a bitwise right shift operation on an integer.
Parameters:
n : `int` - The int to perform the bitwise Right rotation on the bits.
step : `int` - The number of bits to shift by.
Returns: `int` - The result of the bitwise right shift operation.
bSetCheck(n, pos)
Checks if the bit at the given position is set to 1.
Parameters:
n : `int` - The integer to check.
pos : `int` - The position of the bit to check.
Returns: `bool` - True if the bit is set to 1, False otherwise.
bClear(n, pos)
Clears a particular bit of an integer (changes from 1 to 0) passes if bit at pos is 0.
Parameters:
n : `int` - The integer to clear a bit from.
pos : `int` - The zero-based index of the bit to clear.
Returns: `int` - The result of clearing the specified bit.
bFlip0s(n)
Flips all 0 bits in the number to 1.
Parameters:
n : `int` - The integer to flip the bits of.
Returns: `int` - The result of flipping all 0 bits in the number.
bFlip1s(n)
Flips all 1 bits in the number to 0.
Parameters:
n : `int` - The integer to flip the bits of.
Returns: `int` - The result of flipping all 1 bits in the number.
bFlipAll(n)
Flips all bits in the number.
Parameters:
n : `int` - The integer to flip the bits of.
Returns: `int` - The result of flipping all bits in the number.
bSet(n, pos, newBit)
Changes the value of the bit at the given position.
Parameters:
n : `int` - The integer to modify.
pos : `int` - The position of the bit to change.
newBit : `int` - na = flips bit at pos reguardless 1 or 0 | The new value of the bit (0 or 1).
Returns: `int` - The modified integer.
changeDigit(n, pos, newDigit)
Changes the value of the digit at the given position.
Parameters:
n : `int` - The integer to modify.
pos : `int` - The position of the digit to change.
newDigit : `int` - The new value of the digit (0-9).
Returns: `int` - The modified integer.
bSwap(n, i, j)
Switch the position of 2 bits of an int
Parameters:
n : `int` - int to manipulate
i : `int` - bit pos to switch with j
j : `int` - bit pos to switch with i
Returns: `int` - new int with bits switched
bPalindrome(n)
Checks to see if the binary form is a Palindrome (reads the same left to right and vice versa)
Parameters:
n : `int` - int to check
Returns: `bool` - result of check
bEven(n)
Checks if n is Even
Parameters:
n : `int` - The integer to check.
Returns: `bool` - result.
bOdd(n)
checks if n is Even if not even Odd
Parameters:
n : `int` - The integer to check.
Returns: `bool` - result.
bPowerOfTwo(n)
Checks if n is a Power of 2.
Parameters:
n : `int` - number to check.
Returns: `bool` - result.
bCount(n, to_count)
Counts the number of bits that are equal to 1 in an integer.
Parameters:
n : `int` - The integer to count the bits in.
to_count `string` - the bits to count
Returns: `int` - The number of bits that are equal to 1 in n.
GCD(a, b)
Finds the greatest common divisor (GCD) of two numbers.
Parameters:
a : `int` - The first number.
b : `int` - The second number.
Returns: `int` - The GCD of a and b.
LCM(a, b)
Finds the least common multiple (LCM) of two integers.
Parameters:
a : `int` - The first integer.
b : `int` - The second integer.
Returns: `int` - The LCM of a and b.
aLCM(nums)
Finds the LCM of an array of integers.
Parameters:
nums : `int ` - The list of integers.
Returns: `int` - The LCM of the integers in nums.
adjustedLCM(nums, LCM)
adjust an array of integers to Least Common Multiple (LCM)
Parameters:
nums : `int ` - The first integer
LCM : `int` - The second integer
Returns: `int ` - array of ints with LCM
charAt(str, pos)
gets a Char at a given position.
Parameters:
str : `string` - string to pull char from.
pos : `int` - pos to get char from string (left to right index).
Returns: `string` - char from pos of string or "" if pos is not within index range
decimalToBinary(num)
Converts a decimal number to binary
Parameters:
num : `int` - The decimal number to convert to binary
Returns: `string` - The binary representation of the decimal number
decimalToBinary(num, to_binary_int)
Converts a decimal number to binary
Parameters:
num : `int` - The decimal number to convert to binary
to_binary_int : `bool` - bool to convert to int or to string (true for int, false for string)
Returns: `string` - The binary representation of the decimal number
binaryToDecimal(binary)
Converts a binary number to decimal
Parameters:
binary : `string` - The binary number to convert to decimal
Returns: `int` - The decimal representation of the binary number
decimal_len(n)
way of finding decimal length using arithmetic
Parameters:
n `float` - floating decimal point to get length of.
Returns: `int` - number of decimal places
int_len(n)
way of finding number length using arithmetic
Parameters:
n : `int`- value to find length of number
Returns: `int` - lenth of nunber i.e. 23 == 2
float_decimal_to_whole(n)
Converts a float decimal number to an integer `0.365 to 365`.
Parameters:
n : `string` - The decimal number represented as a string.
Returns: `int` - The integer obtained by removing the decimal point and leading zeroes from s.
fractional_part(x)
Returns the fractional part of a float.
Parameters:
x : `float` - The float to get the fractional part of.
Returns: `float` - The fractional part of the float.
form_decimal(a, b, zero_fix)
helper to form 2 ints into 1 float seperated by the decimal
Parameters:
a : `int` - a int
b : `int` - b int
zero_fix : `bool` - fix for trailing zeros being truncated when converting to float
Returns: ` ` - float = float decimal of ints | string = string version of b for future use to ref length
bEncode(n1, n2)
Encodes two numbers into one using bit OR. (fastest)
Parameters:
n1 : `int` - The first number to Encodes.
n2 : `int` - The second number to Encodes.
Returns: `int` - The result of combining the two numbers using bit OR.
bDecode(n)
Decodes an integer created by the bCombine function.(fastest)
Parameters:
n : `int` - The integer to decode.
Returns: ` ` - A tuple containing the two decoded components of the integer.
Encode(a, b)
Encodes by seperating ints into left and right of decimal float
Parameters:
a : `int` - a int
b : `int` - b int
Returns: `float` - new float of encoded ints one on left of decimal point one on right
Decode(encoded)
Decodes float of 2 ints seperated by decimal point
Parameters:
encoded : `float` - the encoded float value
Returns: ` ` - tuple of the 2 ints from encoded float
encode_heavy(a, b)
Encodes by combining numbers and tracking size in the
decimal of a floating number (slowest)
Parameters:
a : `int` - a int
b : `int` - b int
Returns: `float` - new decimal of encoded ints
decode_heavy(encoded)
Decodes encoded float that tracks size of ints in float decimal
Parameters:
encoded : `float` - encoded float
Returns: ` ` - tuple of decoded ints
decimal of float (slowest)
Parameters:
encoded : `float` - the encoded float value
Returns: ` ` - tuple of the 2 ints from encoded float
Bitwise, Encode, Decode Docs
In the documentation you may notice the word decimal
not used as normal this is because when referring to
binary a decimal number is a number that
can be represented with base 10 numbers 0-9
(the wiki below explains better)
A rule of thumb for the two integers being
encoded it to keep both numbers
less than 65535 this is because anything lower uses 16 bits or less
this will maintain 100% accuracy when decoding
although it is possible to do numbers up to 2147483645 with
this library doesnt seem useful enough
to explain or demonstrate.
The functions provided work within this 32-bit range,
where the highest number is all 1s and
the lowest number is all 0s. These functions were created
to overcome the lack of built-in bitwise functions in Pinescript.
By combining two integers into a single number,
the code can access both values i.e when
indexing only one array index
for a matrices row/column, thus improving execution time.
This technique can be applied to various coding
scenarios to enhance performance.
Bitwise functions are a way to use integers in binary form
that can be used to speed up several different processes
most languages have operators to perform these function such as
`<<, >>, &, ^, |, ~`
en.wikipedia.org
Spot vs Derivative PremiumDifference between spot and derivative prices. With this indicator you can get an idea on how strong the market is.
Turtle Trading Risk Adjusted Position Size CalculatorTurtle Trading Risk Adjusted Position Size Calculator
Hello Traders !
Turtle Trading Risk Adjustment Calculator (inspired by the Turtle Traders Position sizing methods) aims to objectively help day traders allocate the appropriate position size per trade by scaling different instruments by their risk, as measured by their volatility via the ATR (default - Average of 14 period True Range). By doing so This volatility-based position sizing method normalizes risk across different asset classes.
Understanding The formula
Formula U normalizes positions sizes among any non FX asset, by representing a standard unit of risk as a fraction of volatility adjusted by a risk coefficient (note higher risk coeff values (high uncertainty) will lead to lower trade capital allocation i.e lower position size - Varying the risk coefficient is relevant in expressing uncertainty) and scaled to ones trading account size relative to 1 contract of the asset to be traded, This is referred to as the Dollar volatility, formula D.
Dollar volatility is a bit confusing but in essence it is simply a factor of the asset price such that quantity sums to ones Trading account balance or how many times larger ones trading account is than the assets current market price, or more formally The amount of value a $1 change in the contract would impact your trading account given you are current trading all you account equity.
Formula TVPP is my own adaptation of the Turtle Trading Position Sizing formulas and the standard value per pip formula adjusted for volatility, this iteration has the same logic as stated above although the formulas vary.
Hope this is Useful, Wishing you Luck in your Trading Journey - u got this !!
Futures/Spot Ratiowhat is Futures /Spot Ratio?
Although futures and spot markets are separate markets, they are correlated. arbitrage bots allow this gap to be closed. But arbitrage bots also have their limits. so there are always slight differences between futures and spot markets. By analyzing these differences, the movements of the players in the market can be interpreted and important information about the price can be obtained. Futures /Spot Ratio is a tool that facilitates this analysis.
what it does?
it compresses the ratio between two selected spot and futures trading pairs between 0 and 100. its purpose is to facilitate use and interpretation. it also passes a regression (Colorful Regression) through the middle of the data for the same purpose.
about Colorful Regression:
how it does it?
it uses this formula:
how to use it?
use it to understand whether the market is priced with spot trades or leveraged positions. A value of 50 is the breakeven point where the ratio of the spot and leveraged markets are equal. Values above 50 indicate excess of long positions in the market, values below 50 indicate excess of short positions. I have explained how to interpret these ratios with examples below.
RS Stage AnalysisThis script trying to detect different lifecycle of stock / Stages.
There is mainly 4 stages of stocks.
1) stage 1 - Accumulation = color = aqua
2) stage 2 - Advancing = color = green
3) stage 3 - Distribution = color = yellow
4) stage 4 - Declining = color = red
At some point the condition i wrote wont detect any stage.
XLY/XLP RatioThe XLY/XLP ratio is a financial indicator that measures the ratio between the two ETFs (Exchange Traded Funds) Consumer Discretionary Select Sector SPDR Fund (XLY) and Consumer Staples Select Sector SPDR Fund (XLP). This ratio is often used by traders and investors as a measure of the relative success of companies in the consumer goods and consumer services sectors.
A higher XLY/XLP ratio indicates that consumer confidence is higher and people are more willing to spend their money on non-essential items, such as entertainment or luxury goods (discretionary spending). A lower XLY/XLP ratio, on the other hand, indicates that consumer confidence is lower and people are more willing to spend their money on essential items like food and household items (staple spending).
The interpretation of the XLY/XLP ratio depends on the current market situation and the analysis of the economic and political factors that may influence consumption. If the XLY/XLP ratio rises, it could be an indication of a growing economy and increasing consumer sentiment. However, if it falls, it could be an indication of a weakening economy or declining consumer confidence.
It is important to note that the XLY/XLP indicator should not be used as the sole indicator to make trading decisions. It is advisable to also consider other indicators, such as technical and fundamental analysis, before making a decision.
Gaps [Kioseff Trading]Hello!
This script "Gaps" is a continuation and improvement on a subset indicator included in the "Quartile Volume; Volume Aggregation; US Range Bars; Gaps)" script!
As advised by @thebearfib, the "Gaps" indicator is now standalone!
Features
Stat: Avg. Bars to Fill Up Gap
Stat: Avg. Bars to Fill Down Gap
Stat: Cumulative Up Gap % Increase
Stat: Cumulative Down Gap % Increase
Stat: Avg Up Gap % Increase
Stat: Avg Down Gap % Decrease
Nearest Unfilled Up Gaps and Down Gaps Displayed in Table
% Price Move Requirement, Including Dollar Amount, for Nearest Unfilled Gaps to Fill
Gaps Marked on Chart, Including Partially Filled Gaps and The % Amount a Partially Filled Gap Has Been Violated
Gaps Chart
The image above shows the data tables included in the indicator!
Settings
The image above shows various settings for the indicator!
The image above shows how partially filled gaps are marked using the default settings.
Exceeded price areas are shaded darker; however, by selecting the "No Partially Filled Gaps" option, the indicator will treat partially filled gaps differently.
The image above shows alternative behavior! Instead of the gap changing color it narrows in size.
The image above shows the indicator's behavior when selecting to show gap data in labels.
Therefore, when a gap is small and the box text is imperceptible, you can select to show the data in a label.
Additionally, you can select to display a "Gaps Chart".
The image above shows this feature enabled. The gaps chart shows the sequence of price gaps for the asset as candlesticks.
Thank you for checking this out; if you'd like other features included please let me know!
Highest/Lowest value since X time ago, various indicatorsThis script will count the bars back since the last time the current bar indicator value was either this low or this high.
It will provide the time in either, seconds, minutes, hours, days, weeks, months, or years.
please note:
There are currently no alerts setup for this script.
the length options only apply to the sources that have the "(MA)" in their name.
There is a horizontal line display issue which corrects once you adjust the amount of sources you want to use.
Once you select the amount of sources you would like to use, align the indicator so the horizontal lines match up with the table lines.
If find any bugs in the script, let me know.
Price Data LabelThis indicator gives you the ability to see historical data for each bar on the chart by simply hovering over the high of the bar, similar to the functionality of MarketSmith.
Data for each bar includes:
Open
High
Low
Close + Change
Percentage Change
Closing Range
Volume
Volume Percent based on 50 day average
Distance to 4 selectable moving averages
Example of stats on a historical bar:
* Note this only works on the last 500 historical bars. If you use bar replay it will work with 500 historical bars from the last bar.
* If you have multiple indicators on your chart, in order to see the data you will need to use visual order to bring to front. This can be done by clicking the three dots next to the indicator name and selecting visual order.
Correlation Coefficient TableThis is a sample PineSript code implementation using Correlation Coefficient. It uses the ta.correlation library of Pinescript and calculates the correlation based on user input length. The results are then plotted on a table. The corr value displays the actual correlation coefficient value while the Corr Status displays the interpretation of the correlation coefficient values.
The script takes the following input
Source Symbol - This is the base symbol which will be used in calculating correlation coefficient. In my case, since i am looking more often on crypto. I defaulted it to BTCUSDT
Symbol 1 - Symbol 5 - These are the coins that will be compared to our base symbol for correlation.
Source - You can select on which price source you want to be calculated. By default this is set to candle close price.
Length - The number of price bar to look back and retrieve correlation coefficient. Set to 20 bars by default.
Table Settings - Since the correlation coefficient are displayed on a table. An option to customize the table settings are presented.
The Correlation Status column was based on this Interpretation:
For more information, read this article www.tradingview.com
Global (World) Monetary Supply M2 (measured in USD)This is the Global Monetary Supply M2 of the richest and most populous countries that have info from at least 2008
It is measured in USD (converting the M2 of each of the countries respective currencies and virtually converting them into USD)
This is less than the global liquidity as it does not include the countries' assets in other currencies (on their balance sheets), it only focuses on the monetary supply of each of the countries own currencies.
DataChartLibrary "DataChart"
Library to plot scatterplot or heatmaps for your own set of data samples
draw(this)
draw contents of the chart object
Parameters:
this : Chart object
Returns: current chart object
init(this)
Initialize Chart object.
Parameters:
this : Chart object to be initialized
Returns: current chart object
addSample(this, sample, trigger)
Add sample data to chart using Sample object
Parameters:
this : Chart object
sample : Sample object containing sample x and y values to be plotted
trigger : Samples are added to chart only if trigger is set to true. Default value is true
Returns: current chart object
addSample(this, x, y, trigger)
Add sample data to chart using x and y values
Parameters:
this : Chart object
x : x value of sample data
y : y value of sample data
trigger : Samples are added to chart only if trigger is set to true. Default value is true
Returns: current chart object
addPriceSample(this, priceSampleData, config)
Add price sample data - special type of sample designed to measure price displacements of events
Parameters:
this : Chart object
priceSampleData : PriceSampleData object containing event driven displacement data of x and y
config : PriceSampleConfig object containing configurations for deriving x and y from priceSampleData
Returns: current chart object
Sample
Sample data for chart
Fields:
xValue : x value of the sample data
yValue : y value of the sample data
ChartProperties
Properties of plotting chart
Fields:
title : Title of the chart
suffix : Suffix for values. It can be used to reference 10X or 4% etc. Used only if format is not format.percent
matrixSize : size of the matrix used for plotting
chartType : Can be either scatterplot or heatmap. Default is scatterplot
outliersStart : Indicates the percentile of data to filter out from the starting point to get rid of outliers
outliersEnd : Indicates the percentile of data to filter out from the ending point to get rid of outliers.
backgroundColor
plotColor : color of plots on the chart. Default is color.yellow. Only used for scatterplot type
heatmapColor : color of heatmaps on the chart. Default is color.red. Only used for heatmap type
borderColor : border color of the chart table. Default is color.yellow.
plotSize : size of scatter plots. Default is size.large
format : data representation format in tooltips. Use mintick.percent if measuring any data in terms of percent. Else, use format.mintick
showCounters : display counters which shows totals on each quadrants. These are single cell tables at the corners displaying number of occurences on each quadrant.
showTitle : display title at the top center. Uses the title string set in the properties
counterBackground : background color of counter table cells. Default is color.teal
counterTextColor : text color of counter table cells. Default is color.white
counterTextSize : size of counter table cells. Default is size.large
titleBackground : background color of chart title. Default is color.maroon
titleTextColor : text color of the chart title. Default is color.white
titleTextSize : text size of the title cell. Default is size.large
addOutliersToBorder : If set, instead of removing the outliers, it will be added to the border cells.
useCommonScale : Use common scale for both x and y. If not selected, different scales are calculated based on range of x and y values from samples. Default is set to false.
plotchar : scatter plot character. Default is set to ascii bullet.
ChartDrawing
Chart drawing objects collection
Fields:
properties : ChartProperties object which determines the type and characteristics of chart being plotted
titleTable : table containing title of the chart.
mainTable : table containing plots or heatmaps.
quadrantTables : Array of tables containing counters of all 4 quandrants
Chart
Chart type which contains all the information of chart being plotted
Fields:
properties : ChartProperties object which determines the type and characteristics of chart being plotted
samples : Array of Sample objects collected over period of time for plotting on chart.
displacements : Array containing displacement values. Both x and y values
displacementX : Array containing only X displacement values.
displacementY : Array containing only Y displacement values.
drawing : ChartDrawing object which contains all the drawing elements
PriceSampleConfig
Configs used for adding specific type of samples called PriceSamples
Fields:
duration : impact duration for which price displacement samples are calculated.
useAtrReference : Default is true. If set to true, price is measured in terms of Atr. Else is measured in terms of percentage of price.
atrLength : atrLength to be used for measuring the price based on ATR. Used only if useAtrReference is set to true.
PriceSampleData
Special type of sample called price sample. Can be used instead of basic Sample type
Fields:
trigger : consider sample only if trigger is set to true. Default is true.
source : Price source. Default is close
highSource : High price source. Default is high
lowSource : Low price source. Default is low
tr : True range value. Default is ta.tr
Global Monetary Supply M2 Vs the Global GDP This indicator compares the Global (world) Monetary Supply (measured in USD) compared to the Global GDP.
This can be useful to measure the "money printing speed" of the world compare to the "world gdp growth", the higher the slope (angle of growth) the more money printing.
It includes the exact same countries of the Global M2 indicator (done by me), to make fair the comparison, which has the richest and most populous countries so to have a clear overview.
There tough a few very populated countries excluded, the details can be found on the Global M2 indicator script and reason for exclusion.
Enjoy!
Feature ScalingLibrary "Feature_Scaling"
FS: This library helps you scale your data to certain ranges or standarize, normalize, unit scale or min-max scale your data in your prefered way. Mostly used for normalization purposes.
minmaxscale(source, min, max, length)
minmaxscale: Min-max normalization scales your data to set minimum and maximum range
Parameters:
source
min
max
length
Returns: res: Data scaled to the set minimum and maximum range
meanscale(source, length)
meanscale: Mean normalization of your data
Parameters:
source
length
Returns: res: Mean normalization result of the source
standarize(source, length, biased)
standarize: Standarization of your data
Parameters:
source
length
biased
Returns: res: Standarized data
unitlength(source, length)
unitlength: Scales your data into overall unit length
Parameters:
source
length
Returns: res: Your data scaled to the unit length
TableBuilderLibrary "TableBuilder"
A helper library to make it simpler to create tables in pinescript
This is a simple table building library that I created because I personally feel that the built-in table building method is too verbose. It features chaining methods and variable arguments.
There are many features that are lacking because the implementation is early, and there may be antipatterns because I am not familiar with the runtime behavior like pinescript. If you have any comments on code improvements or features you want, please comment :D
Hurst Exponent Trend filterHello Traders !!
Hurst Exponent Trend filter utalises the Hurst Exponent and VAWMA (one of my other unique indicators - check my script publishings to use) to categorise the market and decide whether its Trending, H > 0.5, In random Geometric Brownian Motion (GBM) H = 0.5 or Mean reverting (Contrarian), H < 0.5, When Trending a Trend following indicator -The VAWMA- is color highlighted, By doing so, theoreticaly price noise is eleimnated leaving statsitcaly true zones of price action Trend.
What is The Hurst Exponent ?
Developed by The Hydrologist Edwin Harlod Hurst, The Hurst Exponent measures auto correlation in time series sets, Its first applicartions were in the natural world, e.g. in measureing the volume of water in a river.
Although since then it has had applications in Finance, this may be largly due to autocorrelation functions being usefull tools in univaritae time series anaylyis.
The Hurst Exponent (H) aims to segment the market into three differnet states, Trending (H > 0.5), Random Geometric Brownian Motion (H = 0.5) and Mean Reverting / Contrarian (H < 0.5). In my interpritation this can be used as a trend filter that iliminates market noise, which may be achived by only focusing on trending zones.
How to Interprit the Indicator :
Focusing on the Above image, When H > 0.5 A trend is presnet, to decide the directional bias, both VAWMA`s position is checked, given the fast VAWMA > slow VAWMA and the current close > the fast VAWMA a bulish bias is present, signafied by a vibrant green fill between the fast VAWMA and price action. note the exact opposite logic for a bearish bias and H > 0.5 (signafied by a vibrant red fill). .
I will continue to update this Trading Indicator.
PS : Thats given I can hopfully remmember
Happy Trading !!
SILLibrary "SIL"
mean_src(x, y)
calculates moving average : x is the source of price (OHLC) & y = the lookback period
Parameters:
x
y
stan_dev(x, y, z)
calculates standard deviation, x = source of price (OHLC), y = the average lookback, z = average given prior two float and intger inputs, call the f_avg_src() function in f_stan_dev()
Parameters:
x
y
z
vawma(x, y)
calculates volume weighted moving average, x = source of price (OHLC), y = loookback period
Parameters:
x
y
gethurst(x, y, z)
calculates the Hurst Exponent and Hurst Exponent average, x = source of price (OHLC), y = lookback period for Hurst Exponent Calculation, z = lookback period for average Hurst Exponent
Parameters:
x
y
z
