Barndorff-Nielsen and Shephard Jump Statistic [Loxx]The following comments and descriptions are from from "Problems in the Application of Jump Detection Tests to Stock Price Data" by Michael William Schwert; Professor George Tauchen, Faculty Advisor.
This indicator applies several jump detection tests to intraday stock price data sampled at various frequencies. It finds that the choice of sampling frequency has an effect on both the amount of jumps detected by these tests, as well as the timing of those jumps. Furthermore, although these tests are designed to identify the same phenomenon, they find different amounts and timing of jumps when performed on the same data. These results suggest that these jump detection tests are probably identifying different types of jump behavior in stock price data, so they are not really substitutes for one another.
In recent years there has been a great deal of interest in studying jumps in asset price movements. Reasons why it is important to know when and how frequently jumps occur include risk management and the pricing and hedging of derivative contracts. Investors would benefit greatly from knowing the properties of jumps, since large instantaneous drops in asset prices result in large instantaneous losses. The effect of jumps on derivative pricing is equally significant, especially considering the important role derivatives play in modern financial markets. When asset price movements are continuous, investors can perfectly hedge derivative contracts such as options, but when jumps occur, they cause a change in the derivative price that is non-linear to the change in the price of the underlying asset. Thus, jumps introduce an unhedgeable risk to the holders of derivative contracts.
The ability to identify realized jumps in the financial markets could provide helpful information such as how frequently jumps occur, how large the jumps are, and whether they tend to occur in clusters. With this goal in mind, several authors have developed tests to determine whether or not an asset price movement is a statistically significant jump. These tests take advantage of the high-frequency intraday price data available today through electronic sources. Barndorff-Nielsen and Shephard (2004, 2006) use the difference between an estimate of variance and a jump-robust measure of variance to detect jumps over the course of a day. Approaching the problem differently, Jiang and Oomen (2007) exploit high order sample moments of returns to identify days that include jumps. Aїt-Sahalia and Jacod (2008) also exploit high order sample moments of returns to detect jumps by comparing price data sampled at two different frequencies. Lee and Mykland (2007) test for jumps at individual price observations by scaling returns by a local volatility measure. While these tests employ different strategies for detecting jumps, they are all designed to identify the same phenomenon.
For this indicator we are focused on the Barndorff-Nielsen and Shephard jump statistic.
Barndorff-Nielsen and Shephard (2004, 2006) developed a test that uses high-frequency price data to determine whether there is a jump over the course of a day. Their test compares two measures of variance: Realized Variance, which converges to the integrated variance plus a jump component as the time between observations approaches zero; and Bipower Variation, which converges to the integrated variance as the time between observations approaches zero, and is robust to jumps in the price path, an important fact for this application. The integrated variance of a price process is the integral of the square of the σ(t) term in (2.2.2), taken over the course of a day. Since prices cannot be observed continuously, one cannot calculate integrated variance exactly, and must estimate it instead.
For our purposes here, this is calculated as:
r = log(p /p )
This the geometric return from time ti-1 to time ti.
Then, Realized Variance and Bipower Variation are described by the following functions (see code for details)
realizedVariance(float src, int per)
and
bipowerVariance(float src, int per)
Huang and Tauchen (2005) also consider Relative Jump, a measure that approximates the percentage of total variance attributable to jumps:
RJ = (RV - BV) / RV
This statistic approximates the ratio of the sum of squared jumps to the total variance and is useful because it scales out long-term trends in volatility so one can compare the relative contribution of jumps to the variance of two price series with different volatilities.
To develop a statistical test to determine whether there is a significant difference between RV and BV, one needs an estimate of integrated quarticity. Andersen, Bollerslev, and Diebold (2004) recommend using a jump-robust realized Tri-Power Quarticity, I've included commentary in code to better explain how this indicator is collocated. See code for details.
How to use this indicator
When the bars turn gray, it's an indication that a jump has occurred in the market. It serves a warning that price jumped. I've included a percent point function (or inverse cumulative distribution function) to cutoff Z-score values depicted by histogram values. The top line at 3 is the empirical maximum Z-score value a serves merely as a point of reference. The Red line is the cutoff line calculated using PPF. When the histogram is green, no jumps have been detected. This indicator also includes alerts, signals, and bar coloring. I've also expanded the possible source types using my own Expanded Source Types library so you can test different log return methods as inputs. It is recommended to use window sizes of 7, 16, 78, 110, 156, and 270 returns for sampling intervals of 1 week, 1 day, 1 hour, 30 minutes, 15 minutes, and 5 minutes, respectively.
If you'ed like to better understand PPF, see here: Distributions in python
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Cerca negli script per "电力行业+股票+11年涨幅"
Leavitt Convolution Slope [CC]The Leavitt Convolution Slope indicator was created by Jay Leavitt (Stocks and Commodities Oct 2019, page 11), who is most well known for creating the Volume-Weighted Average Price indicator. This indicator is very similar to the Leavitt Convolution indicator but the big difference is that it is getting the slope instead of predicting the next Convolution value. I changed quite a few things from the original source code so let me know if you like these changes. I added a normalization function using code from a good friend @loxx that I recommend to leave on but feel free to experiment with it. Last but not least, the unsure levels are essentially acting as a buy or sell threshold. I personally recommend to buy or sell for zero crossovers but another option would be to buy or sell for crossovers using the unsure levels. I have color coded the lines to turn light green for a normal buy signal or dark green for a strong buy signal and light red for a normal sell signal, and dark red for a strong sell signal.
This is another indicator in a series that I'm publishing to fulfill a special request from @ashok1961 so let me know if you ever have any special requests for me.
Leavitt Convolution [CC]The Leavitt Convolution indicator was created by Jay Leavitt (Stocks and Commodities Oct 2019, page 11), who is most well known for creating the Volume-Weighted Average Price indicator. This indicator is very similar to my Leavitt Projection script and I forgot to mention that both of these indicators are actually predictive moving averages. The Leavitt Convolution indicator doubles down on this idea by creating a prediction of the Leavitt Projection which is another prediction for the next bar. Obviously this means that it isn't always correct in its predictions but it does a very good job at predicting big trend changes before they happen. The recommended strategy for how to trade with these indicators is to plot a fast version and a slow version and go long when the fast version crosses over the slow version or to go short when the fast version crosses under the slow version. I have color coded the lines to turn light green for a normal buy signal or dark green for a strong buy signal and light red for a normal sell signal, and dark red for a strong sell signal.
This is another indicator in a series that I'm publishing to fulfill a special request from @ashok1961 so let me know if you ever have any special requests for me.
Intraday Range CalculatorThis indicator shows an easy way to determine if the stock, index or ETF ended within a configurable intraday range.
This solution is ideal for those who study and like Iron Condors or Iron Butterflies strategies.
Results:
If the square is red, it means that the selected deviation limits have been exceeded within the chosen times.
If the square is green, the price stayed within the pre-set limits.
A yellow circle marks the moment when the price leaves the range, either by the upper band or by the lower band.
In the last bar a label with the test results will be displayed.
Settings:
In the configuration there are three fields:
1. Deviation : is the range in percentage that the price can move up or down from the start time to the end time.
2. Begin Time: is the time (in 24h or military format) where the process begins.
3. End Time: is the time (in 24h or military format) where the process ends.
Example:
* for the time 11:00 am, you must enter "1100"
* for the time 2:45pm, you must enter "1445"
Important:
The selected timeframe must be less than 1 hour and Extended Trading Hours in the lower left corner), otherwise the indicator may not show results.
Later I will make an improvement to solve these inconveniences.
Leavitt Projection [CC]The Leavitt Projection indicator was created by Jay Leavitt (Stocks and Commodities Oct 2019, page 11), who is most well known for creating the Volume-Weighted Average Price indicator. This indicator is very simple but is also the building block of many other indicators, so I'm starting with the publication of this one. Since this is the first in a series I will be publishing, keep in mind that the concepts introduced in this script will be the same across the entire series. The recommended strategy for how to trade with these indicators is to plot a fast version and a slow version and go long when the fast version crosses over the slow version or to go short when the fast version crosses under the slow version. I have color coded the lines to turn light green for a normal buy signal or dark green for a strong buy signal and light red for a normal sell signal, and dark red for a strong sell signal.
I know many of you have wondered where I have been, and my personal life has become super hectic. I was recently hired full-time by TradingView, and my wife is pregnant with twins, and she is due in a few months. I will do my absolute best to get back to posting scripts regularly, but I will post a bunch today in the meantime to fulfill a special request from one of my loyal followers (@ashok1961).
NSE Paper Stocks IndexThis is the script to create a custom index of NSE paper stocks. in this example 11 stocks are selected
Volatility Percentage IndicatorThis simple indicator plot 11 lines in the chart at prices that correspond to -5%, -4%, -3%, -2%, -1%, 0%, 1%, 2%, 3%, 4%, 5%, referred to realtime price.
So the lines will move with the price.
The indicator is intended to give an at-a-glance information on price volatility by comparing the amplitude of the last candles with the percentages above.
Price Action in action
What?
Price Action in Action is an indicator to help Price Action learners and practitioners to get everything related for Price Action in one place.
Price Action is:
Price + Volume = Action
In this indicator, we have the following features available:
Support/Resistance
Using the RSI with different periods in a multiple of 7 (7, 14, 21, 28), we first determine the overbought (above 70, customizable) and oversold (below 30, customizable) regions. Then we pick up the highest point and lowest point in the RSI values in the overbought and oversold regions, respectively. These are the point, historically supply/demand emerged for surety to push down/up the RSI indicator and the corresponding price. So, these are the most accurate way, we believe, to draw support/resistance (or demand/supply) in the chart. By default, the Support is green color and Resistance is red color. To give a visual representation, we differentiate the different shades of green and red. For example, for Level-1 (i.e. 7 by default) we use the darkest shade (0 transparency) and Level-4 (i.e. 28 by default) we use lighter shade (60 transparency). Note please: you can customize the color of support and resistance lines (say if you want resistance as green and support as red). The respective shades (transparency) will be automatically adjusted accordingly. But those shade (transparency) levels are not customizable, they are fixed (please bear with it for version-1 at least).
Strength of Support/Resistance
In the chart above/below the Resistance / Support lines you can see the tiny labels with some numbers like 1, 2.
We found out how many times a particular support/resistance is appearing across multiple RSI periods. E.g. if price P1 appears 2 times among 4 different RSI periods, the number will be 2 for that calculation, and so on.
There can be multiple presence of these numbers in a support/resistance line (i.e. multiple tiny labels). Something like: 1, 1, 2 (into different candles). This means the same support/resistance is tested so many times in different occasion (means there is a RSI max/min coincides in this level over multiple occasions) at different candles.
This will help you to intuitionally gauge the “strength” of a support/resistance line.
The more the marrier, unworthy to mention.
Candle Stick Patterns
Well: we don’t need to tell anything about the Candlestick. All of you know it better than us. And it’s a time proven, zero-lag mechanism to judge the Price-Action is unfolding in the market. We do not know if there is anything better possible than this time tested patterns to judge the prevailing sentiments of market.
Price-Action does not complete without finding out the Candlestick Patterns correctly.
And in this indicator your will get all of these: Single Candle such as Doji (default off), Marubozu, Spinner, hammers, inverted-hammer etc. ; 2 candles like Tweezer, Inside Candle, Engulfing; 3 candles like morning star/evening star.
In the multi candle patterns (2/3 candles), we are grouping the candles with a dotted rectangle such that it is clear which 2/3 candles are part of the pattern. E.g. Morning Star: 3 candles are grouped in a dotted rectangle and the Morning Star label will come to the latest candle (3rd most – as the pattern is detected reliably only on the completion of the 3rd final candle).
Of course, any program can not eliminate your trained eyes and brain to capture the patterns. But we have provided sufficient knobs to adjust various parameters to tweak the candle-pattern detection. Such as Strict Inside Candle(Harami) Boolean knob where the whole current candle including wicks will be inside the body part of the previous big candle. For non-strict mode, the current candle just inside the previous candle, possibly by wicks.
To make it better usable, for every such knobs (which are not obvious) we have added user-friendly tooltip (just mouse hover the question mark (?) besides the control/switch). There are plenty of it.
Volume
Here we have a rudimentary (yet effective) way to judge the volumes.
We find out the Volume Weighted Moving Average (VMWA) of the 20-period (default, but customizable) and the latest volume. If the latest volume is more than the 20 period vwma, we just add a grey diamond on the top of the candle to denote it’s attracting volumes. Of course, we provide a Weight coefficient (default is set to 1). So if the current bar’s volume on bar’s completion is more than the 20 period volume vmwa times the weigh-cofficient, we mark it with a tiny grey diamond.
Points to be noted:
In all places we mark the indication only on the completion of the bar (technically speaking we have checks, as far as possible, with barstate.isconfirmed). However, if you wish, you can turn it off for Candlestick (as some experts may want to check candlestick on the real time, even before the closing of bars).
In case if you see the chart looks cluttered (because of many information, specially in smaller timeframes like 5 min), there are controls given in the settings to toggle each and every features.
By default, we turn off Doji candles (all 3 types of Doji’s – normal, Gravestone & Dragonfly) as they are mainly indecision. However, you can toggle it to turn it on.
It does not give you any Buy/Sell call. The interpretation it does not have.
Why?
What’s unique in it?
As we already mentioned our intention is to include Price (in forms of Support / Resistance), Volume and Action (sentiments in terms of Candlestick patterns) into a single place. And so far, to the best of our knowledge, we could not come across a single indicator provides all of these.
There were works available to determine the RSI based support / resistance zones. Those are great piece works at that time (lets say 3 years back when PineScript was in earlier versions). To the best of our knowledge those does not cover up finding out the lowest / highest point of RSI and the corresponding price to get the simplistic and distinct support/resistance lines.
We have the intuitive support/resistance strength included which we could not found out in current set of available indicators.
To the best of our knowledge, there seems no indicator can detect 3-candle patterns which are extremely popular to detect trend reversals (such as Morning Star or Evening Star). Moreover for the multi-candle patterns we are grouping the candles part of the pattens (2-candles or 3-candles) using a dotted rectangle such that it’s visually clearly (and a well educative material for Price-Action learners also).
Mentions:
There are many works which inspire us along the way. Honestly: we sometimes forgot which all indicators we experimented with. We are sincerely apologetic in case we forgot to mention. A few note-worthy:
There is an indicator from user “repo32” named as “Candlestick Patterns Identified (updated 3/11/15)”. (We could not be able to contact “repo32”). We are inspired from his work that it’s feasible to detect Candlestick patterns.
There is an awesome work done by “RSI Based Automatic Demand and Supply” by user “shtcoinr”. The idea of consulting multiple RSI levels to find out the demand/supply zone we inspired from him. (We did contact “shtcoinr” and got his kind permission to use the concept.)
We are greatly thankful to these abovementioned wizards for their pioneering a-prior work in this front.
And of course, this TradingView platform to provide this abstraction, facilitates and felicitates collaborative contributions.
Ultimately, what’s for you?
That’s the main question. What’s for you?
Price-action comprises of following 3 tasks (at least):
Draw support/resistance lines in the chart.
Once price reaches at the support/resistance line, you fervently look out the candles’ formation to mentally map to the candle patterns. Your aim is divine: You want to judge if the price-action will continue or take a rejection/reversal.
Then you double-confirm with the volume (in a non-overlaid chart below).
Finally take a trade.
For a price-action newbie or seasoned, expert practitioner, you must be doing all the above tasks regularly and manually, in a mechanical, mundane way. There come the humanly subjectivity & the inevitable emotions . This indicator, being a piece of program/code in PineScript latest version v5 , eliminates (or at least, reduces to a great extend) that subjectivity & emotions out of the way of decision making . Thus resulting better yield.
Of course, you can argue that you draw slanted trend lines also. We recommend an already existing indicator by user LuxAlgo named as “Trendlines with Breaks ”, if you wish so.
Disclaimer:
This piece of software does not come up with any warrantee or any rights of not changing it over the future course of time.
We are not responsible for any trading/investment decision you are taking out of the outcome of this indicator.
Happy trading.
[HA] Heikin-Ashi Shadow Candles// For overlaying Heikin Ashi candles over basic charts, or for use in it's own panel as an oscillator.
// Enjoy the visual cues of HA candles, without giving up price action awareness.
// Good for learning and comparison.
// Aug 11 2022
Release Notes: * Bugfix: Candle color was based on classic direction not HA direction (did not update cover photo).
// Aug 12 2022
Release Notes: * Implemented true oscillator mode.
Provided as separate plot (styles tab) or mode switch option (Inputs tab). TV gets spazzy with "styles tab" "default hidden" plots, and will reset them if any variables are modified that affect them (i.e. wick color override). Mode switch should be sufficient for both users.
// Aug 21 2022
Republished because of typo in indicator name prevented search.
Rsi/W%R/Stoch/Mfi: HTF overlay mini-plotsOverlay mini-plots for various indicators. Shows current timeframe; and option to plot 2x higher timeframes (i.e. 15min and 60min on the 5min chart above).
The idea is to de-clutter chart when you just want real-time snippets for an indicator.
Useful for gauging overbought/oversold, across timeframes, at a glance.
~~Indicators~~
~RSI: Relative strength index
~W%R: Williams percent range
~Stochastic
~MFI: Money flow index
~~Inputs~~
~indicator length (NB default is set to 12, NOT the standard 14)
~choose 2x HTFs, show/hide HTF plots
~choose number of bars to show (current timeframe only; HTF plots show only 6 bars)
~horizontal position: offset (bars); shift plots right or left. Can be negative
~vertical position: top/middle/bottom
~other formatting options (color, line thickness, show/hide labels, 70/30 lines, 80/20 lines)
~~tips~~
~should be relatively easy to add further indicators, so long as they are 0-100 based; by editing lines 9 and 11
~change the vertical compression of the plots by playing around with the numbers (+100, -400, etc) in lines 24 and 25
Educational: FillThis script showcases the latest feature of colour fill between lines with gradient
There are 17 ema's, all with adjustable lengths.
In the settings there are 3 options: '1' , '2' , and '1 & 2' :
Option '1'
Here the highest - lowest lines are filled with a gradient colour,
dependable where the 3rd highest/lowest ema is situated in regard of these 2 lines:
Option '2'
Here the colour fill is applied between every ema and the one next to it.
The gradient colour is dependable where the ema is situated in regard of the highest - lowest line:
Option '1 & 2'
A combination of both options:
The setting 'switch colours at ema x' regulates the switch between bullish and bearish colours.
When close is above the chosen ema -> bullish colours, when below -> bearish colours.
Examples of other settings of 'switch colours at ema x' :
Colour switch when close above/below:
ema 14
ema 11
ema 8
ema 5
ema 2
The colours can be set below, both for option '1' and '2'
Cheers!
Modified Covariance Autoregressive Estimator of Price [Loxx]What is the Modified Covariance AR Estimator?
The Modified Covariance AR Estimator uses the modified covariance method to fit an autoregressive (AR) model to the input data. This method minimizes the forward and backward prediction errors in the least squares sense. The input is a frame of consecutive time samples, which is assumed to be the output of an AR system driven by white noise. The block computes the normalized estimate of the AR system parameters, A(z), independently for each successive input.
Characteristics of Modified Covariance AR Estimator
Minimizes the forward prediction error in the least squares sense
Minimizes the forward and backward prediction errors in the least squares sense
High resolution for short data records
Able to extract frequencies from data consisting of p or more pure sinusoids
Does not suffer spectral line-splitting
May produce unstable models
Peak locations slightly dependent on initial phase
Minor frequency bias for estimates of sinusoids in noise
Order must be less than or equal to 2/3 the input frame size
Purpose
This indicator calculates a prediction of price. This will NOT work on all tickers. To see whether this works on a ticker for the settings you have chosen, you must check the label message on the lower right of the chart. The label will show either a pass or fail. If it passes, then it's green, if it fails, it's red. The reason for this is because the Modified Covariance method produce unstable models
H(z)= G / A(z) = G / (1+. a(2)z −1 +…+a(p+1)z)
You specify the order, "ip", of the all-pole model in the Estimation order parameter. To guarantee a valid output, you must set the Estimation order parameter to be less than or equal to two thirds the input vector length.
The output port labeled "a" outputs the normalized estimate of the AR model coefficients in descending powers of z.
The implementation of the Modified Covariance AR Estimator in this indicator is the fast algorithm for the solution of the modified covariance least squares normal equations.
Inputs
x - Array of complex data samples X(1) through X(N)
ip - Order of linear prediction model (integer)
Notable local variables
v - Real linear prediction variance at order IP
Outputs
a - Array of complex linear prediction coefficients
stop - value at time of exit, with error message
false - for normal exit (no numerical ill-conditioning)
true - if v is not a positive value
true - if delta and gamma do not lie in the range 0 to 1
true - if v is not a positive value
true - if delta and gamma do not lie in the range 0 to 1
errormessage - an error message based on "stop" parameter; this message will be displayed in the lower righthand corner of the chart. If you see a green "passed" then the analysis is valid, otherwise the test failed.
Indicator inputs
LastBar = bars backward from current bar to test estimate reliability
PastBars = how many bars are we going to analyze
LPOrder = Order of Linear Prediction, and for Modified Covariance AR method, this must be less than or equal to 2/3 the input frame size, so this number has a max value of 0.67
FutBars = how many bars you'd like to show in the future. This algorithm will either accept or reject your value input here and then project forward
Further reading
Spectrum Analysis-A Modern Perspective 1380 PROCEEDINGS OF THE IEEE, VOL. 69, NO. 11, NOVEMBER 1981
Related indicators
Levinson-Durbin Autocorrelation Extrapolation of Price
Weighted Burg AR Spectral Estimate Extrapolation of Price
Helme-Nikias Weighted Burg AR-SE Extra. of Price
Itakura-Saito Autoregressive Extrapolation of Price
Modified Covariance Autoregressive Estimator of Price
Normalized, Variety, Fast Fourier Transform Explorer [Loxx]Normalized, Variety, Fast Fourier Transform Explorer demonstrates Real, Cosine, and Sine Fast Fourier Transform algorithms. This indicator can be used as a rule of thumb but shouldn't be used in trading.
What is the Discrete Fourier Transform?
In mathematics, the discrete Fourier transform (DFT) converts a finite sequence of equally-spaced samples of a function into a same-length sequence of equally-spaced samples of the discrete-time Fourier transform (DTFT), which is a complex-valued function of frequency. The interval at which the DTFT is sampled is the reciprocal of the duration of the input sequence. An inverse DFT is a Fourier series, using the DTFT samples as coefficients of complex sinusoids at the corresponding DTFT frequencies. It has the same sample-values as the original input sequence. The DFT is therefore said to be a frequency domain representation of the original input sequence. If the original sequence spans all the non-zero values of a function, its DTFT is continuous (and periodic), and the DFT provides discrete samples of one cycle. If the original sequence is one cycle of a periodic function, the DFT provides all the non-zero values of one DTFT cycle.
What is the Complex Fast Fourier Transform?
The complex Fast Fourier Transform algorithm transforms N real or complex numbers into another N complex numbers. The complex FFT transforms a real or complex signal x in the time domain into a complex two-sided spectrum X in the frequency domain. You must remember that zero frequency corresponds to n = 0, positive frequencies 0 < f < f_c correspond to values 1 ≤ n ≤ N/2 −1, while negative frequencies −fc < f < 0 correspond to N/2 +1 ≤ n ≤ N −1. The value n = N/2 corresponds to both f = f_c and f = −f_c. f_c is the critical or Nyquist frequency with f_c = 1/(2*T) or half the sampling frequency. The first harmonic X corresponds to the frequency 1/(N*T).
The complex FFT requires the list of values (resolution, or N) to be a power 2. If the input size if not a power of 2, then the input data will be padded with zeros to fit the size of the closest power of 2 upward.
What is Real-Fast Fourier Transform?
Has conditions similar to the complex Fast Fourier Transform value, except that the input data must be purely real. If the time series data has the basic type complex64, only the real parts of the complex numbers are used for the calculation. The imaginary parts are silently discarded.
What is the Real-Fast Fourier Transform?
In many applications, the input data for the DFT are purely real, in which case the outputs satisfy the symmetry
X(N-k)=X(k)
and efficient FFT algorithms have been designed for this situation (see e.g. Sorensen, 1987). One approach consists of taking an ordinary algorithm (e.g. Cooley–Tukey) and removing the redundant parts of the computation, saving roughly a factor of two in time and memory. Alternatively, it is possible to express an even-length real-input DFT as a complex DFT of half the length (whose real and imaginary parts are the even/odd elements of the original real data), followed by O(N) post-processing operations.
It was once believed that real-input DFTs could be more efficiently computed by means of the discrete Hartley transform (DHT), but it was subsequently argued that a specialized real-input DFT algorithm (FFT) can typically be found that requires fewer operations than the corresponding DHT algorithm (FHT) for the same number of inputs. Bruun's algorithm (above) is another method that was initially proposed to take advantage of real inputs, but it has not proved popular.
There are further FFT specializations for the cases of real data that have even/odd symmetry, in which case one can gain another factor of roughly two in time and memory and the DFT becomes the discrete cosine/sine transform(s) (DCT/DST). Instead of directly modifying an FFT algorithm for these cases, DCTs/DSTs can also be computed via FFTs of real data combined with O(N) pre- and post-processing.
What is the Discrete Cosine Transform?
A discrete cosine transform ( DCT ) expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. The DCT , first proposed by Nasir Ahmed in 1972, is a widely used transformation technique in signal processing and data compression. It is used in most digital media, including digital images (such as JPEG and HEIF, where small high-frequency components can be discarded), digital video (such as MPEG and H.26x), digital audio (such as Dolby Digital, MP3 and AAC ), digital television (such as SDTV, HDTV and VOD ), digital radio (such as AAC+ and DAB+), and speech coding (such as AAC-LD, Siren and Opus). DCTs are also important to numerous other applications in science and engineering, such as digital signal processing, telecommunication devices, reducing network bandwidth usage, and spectral methods for the numerical solution of partial differential equations.
The use of cosine rather than sine functions is critical for compression, since it turns out (as described below) that fewer cosine functions are needed to approximate a typical signal, whereas for differential equations the cosines express a particular choice of boundary conditions. In particular, a DCT is a Fourier-related transform similar to the discrete Fourier transform (DFT), but using only real numbers. The DCTs are generally related to Fourier Series coefficients of a periodically and symmetrically extended sequence whereas DFTs are related to Fourier Series coefficients of only periodically extended sequences. DCTs are equivalent to DFTs of roughly twice the length, operating on real data with even symmetry (since the Fourier transform of a real and even function is real and even), whereas in some variants the input and/or output data are shifted by half a sample. There are eight standard DCT variants, of which four are common.
The most common variant of discrete cosine transform is the type-II DCT , which is often called simply "the DCT". This was the original DCT as first proposed by Ahmed. Its inverse, the type-III DCT , is correspondingly often called simply "the inverse DCT" or "the IDCT". Two related transforms are the discrete sine transform ( DST ), which is equivalent to a DFT of real and odd functions, and the modified discrete cosine transform (MDCT), which is based on a DCT of overlapping data. Multidimensional DCTs ( MD DCTs) are developed to extend the concept of DCT to MD signals. There are several algorithms to compute MD DCT . A variety of fast algorithms have been developed to reduce the computational complexity of implementing DCT . One of these is the integer DCT (IntDCT), an integer approximation of the standard DCT ,: ix, xiii, 1, 141–304 used in several ISO /IEC and ITU-T international standards.
What is the Discrete Sine Transform?
In mathematics, the discrete sine transform (DST) is a Fourier-related transform similar to the discrete Fourier transform (DFT), but using a purely real matrix. It is equivalent to the imaginary parts of a DFT of roughly twice the length, operating on real data with odd symmetry (since the Fourier transform of a real and odd function is imaginary and odd), where in some variants the input and/or output data are shifted by half a sample.
A family of transforms composed of sine and sine hyperbolic functions exists. These transforms are made based on the natural vibration of thin square plates with different boundary conditions.
The DST is related to the discrete cosine transform (DCT), which is equivalent to a DFT of real and even functions. See the DCT article for a general discussion of how the boundary conditions relate the various DCT and DST types. Generally, the DST is derived from the DCT by replacing the Neumann condition at x=0 with a Dirichlet condition. Both the DCT and the DST were described by Nasir Ahmed T. Natarajan and K.R. Rao in 1974. The type-I DST (DST-I) was later described by Anil K. Jain in 1976, and the type-II DST (DST-II) was then described by H.B. Kekra and J.K. Solanka in 1978.
Notable settings
windowper = period for calculation, restricted to powers of 2: "16", "32", "64", "128", "256", "512", "1024", "2048", this reason for this is FFT is an algorithm that computes DFT (Discrete Fourier Transform) in a fast way, generally in 𝑂(𝑁⋅log2(𝑁)) instead of 𝑂(𝑁2). To achieve this the input matrix has to be a power of 2 but many FFT algorithm can handle any size of input since the matrix can be zero-padded. For our purposes here, we stick to powers of 2 to keep this fast and neat. read more about this here: Cooley–Tukey FFT algorithm
SS = smoothing count, this smoothing happens after the first FCT regular pass. this zeros out frequencies from the previously calculated values above SS count. the lower this number, the smoother the output, it works opposite from other smoothing periods
Fmin1 = zeroes out frequencies not passing this test for min value
Fmax1 = zeroes out frequencies not passing this test for max value
barsback = moves the window backward
Inverse = whether or not you wish to invert the FFT after first pass calculation
Related indicators
Real-Fast Fourier Transform of Price Oscillator
STD-Stepped Fast Cosine Transform Moving Average
Real-Fast Fourier Transform of Price w/ Linear Regression
Variety RSI of Fast Discrete Cosine Transform
Additional reading
A Fast Computational Algorithm for the Discrete Cosine Transform by Chen et al.
Practical Fast 1-D DCT Algorithms With 11 Multiplications by Loeffler et al.
Cooley–Tukey FFT algorithm
Ahmed, Nasir (January 1991). "How I Came Up With the Discrete Cosine Transform". Digital Signal Processing. 1 (1): 4–5. doi:10.1016/1051-2004(91)90086-Z.
DCT-History - How I Came Up With The Discrete Cosine Transform
Comparative Analysis for Discrete Sine Transform as a suitable method for noise estimation
STD-Stepped Fast Cosine Transform Moving Average [Loxx]STD-Stepped Fast Cosine Transform Moving Average is an experimental moving average that uses Fast Cosine Transform to calculate a moving average. This indicator has standard deviation stepping in order to smooth the trend by weeding out low volatility movements.
What is the Discrete Cosine Transform?
A discrete cosine transform (DCT) expresses a finite sequence of data points in terms of a sum of cosine functions oscillating at different frequencies. The DCT, first proposed by Nasir Ahmed in 1972, is a widely used transformation technique in signal processing and data compression. It is used in most digital media, including digital images (such as JPEG and HEIF, where small high-frequency components can be discarded), digital video (such as MPEG and H.26x), digital audio (such as Dolby Digital, MP3 and AAC), digital television (such as SDTV, HDTV and VOD), digital radio (such as AAC+ and DAB+), and speech coding (such as AAC-LD, Siren and Opus). DCTs are also important to numerous other applications in science and engineering, such as digital signal processing, telecommunication devices, reducing network bandwidth usage, and spectral methods for the numerical solution of partial differential equations.
The use of cosine rather than sine functions is critical for compression, since it turns out (as described below) that fewer cosine functions are needed to approximate a typical signal, whereas for differential equations the cosines express a particular choice of boundary conditions. In particular, a DCT is a Fourier-related transform similar to the discrete Fourier transform (DFT), but using only real numbers. The DCTs are generally related to Fourier Series coefficients of a periodically and symmetrically extended sequence whereas DFTs are related to Fourier Series coefficients of only periodically extended sequences. DCTs are equivalent to DFTs of roughly twice the length, operating on real data with even symmetry (since the Fourier transform of a real and even function is real and even), whereas in some variants the input and/or output data are shifted by half a sample. There are eight standard DCT variants, of which four are common.
The most common variant of discrete cosine transform is the type-II DCT, which is often called simply "the DCT". This was the original DCT as first proposed by Ahmed. Its inverse, the type-III DCT, is correspondingly often called simply "the inverse DCT" or "the IDCT". Two related transforms are the discrete sine transform (DST), which is equivalent to a DFT of real and odd functions, and the modified discrete cosine transform (MDCT), which is based on a DCT of overlapping data. Multidimensional DCTs (MD DCTs) are developed to extend the concept of DCT to MD signals. There are several algorithms to compute MD DCT. A variety of fast algorithms have been developed to reduce the computational complexity of implementing DCT. One of these is the integer DCT (IntDCT), an integer approximation of the standard DCT, : ix, xiii, 1, 141–304 used in several ISO/IEC and ITU-T international standards.
Notable settings
windowper = period for calculation, restricted to powers of 2: "16", "32", "64", "128", "256", "512", "1024", "2048", this reason for this is FFT is an algorithm that computes DFT (Discrete Fourier Transform) in a fast way, generally in 𝑂(𝑁⋅log2(𝑁)) instead of 𝑂(𝑁2). To achieve this the input matrix has to be a power of 2 but many FFT algorithm can handle any size of input since the matrix can be zero-padded. For our purposes here, we stick to powers of 2 to keep this fast and neat. read more about this here: Cooley–Tukey FFT algorithm
smthper = smoothing count, this smoothing happens after the first FCT regular pass. this zeros out frequencies from the previously calculated values above SS count. the lower this number, the smoother the output, it works opposite from other smoothing periods
Included
Alerts
Signals
Loxx's Expanded Source Types
Additional reading
A Fast Computational Algorithm for the Discrete Cosine Transform by Chen et al.
Practical Fast 1-D DCT Algorithms With 11 Multiplications by Loeffler et al.
Cooley–Tukey FFT algorithm
The Investment ClockThe Investment Clock was most likely introduced to the general public in a research paper distributed by Merrill Lynch. It’s a simple yet useful framework for understanding the various stages of the US economic cycle and which asset classes perform best in each stage.
The Investment Clock splits the business cycle into four phases, where each phase is comprised of the orientation of growth and inflation relative to their sustainable levels:
Reflation phase (6:01 to 8:59): Growth is sluggish and inflation is low. This phase occurs during the heart of a bear market. The economy is plagued by excess capacity and falling demand. This keeps commodity prices low and pulls down inflation. The yield curve steepens as the central bank lowers short-term rates in an attempt to stimulate growth and inflation. Bonds are the best asset class in this phase.
Recovery phase (9:01 to 11:59): The central bank’s easing takes effect and begins driving growth to above the trend rate. Though growth picks up, inflation remains low because there’s still excess capacity. Rising growth and low inflation are the Goldilocks phase of every cycle. Stocks are the best asset class in this phase.
Overheat phase(12:01 to 2:59): Productivity growth slows and the GDP gap closes causing the economy to bump up against supply constraints. This causes inflation to rise. Rising inflation spurs the central banks to hike rates. As a result, the yield curve begins flattening. With high growth and high inflation, stocks still perform but not as well as in recovery. Volatility returns as bond yields rise and stocks compete with higher yields for capital flows. In this phase, commodities are the best asset class.
Stagflation phase (3:01 to 5:59): GDP growth slows but inflation remains high (sidenote: most bear markets are preceded by a 100%+ increase in the price of oil which drives inflation up and causes central banks to tighten). Productivity dives and a wage-price spiral develops as companies raise prices to protect compressing margins. This goes on until there’s a steep rise in unemployment which breaks the cycle. Central banks keep rates high until they reign in inflation. This causes the yield curve to invert. During this phase, cash is the best asset.
Additional notes from Merrill Lynch:
Cyclicality: When growth is accelerating (12 o'clock), Stocks and Commodities do well. Cyclical sectors like Tech or Steel outperform. When growth is slowing (6 o'clock), Bonds, Cash, and defensives outperform.
Duration: When inflation is falling (9 o'clock), discount rates drop and financial assets do well. Investors pay up for long duration Growth stocks. When inflation is rising (3 o'clock), real assets like Commodities and Cash do best. Pricing power is plentiful and short-duration Value stocks outperform.
Interest Rate-Sensitives: Banks and Consumer Discretionary stocks are interest-rate sensitive “early cycle” performers, doing best in Reflation and Recovery when central banks are easing and growth is starting to recover.
Asset Plays: Some sectors are linked to the performance of an underlying asset. Insurance stocks and Investment Banks are often bond or equity price sensitive, doing well in the Reflation or Recovery phases. Mining stocks are metal price-sensitive, doing well during an Overheat.
About the indicator:
This indicator suggests iShares ETFs for sector rotation analysis. There are likely other ETFs to consider which have lower fees and are outperforming their sector peers.
You may get errors if your chart is set to a different timeframe & ticker other than 1d for symbol/tickers GDPC1 or CPILFESL.
Investment Clock settings are based on a "sustainable level" of growth and inflation, which are each slightly subjective depending on the economist and probably have changed since the last time this indicator was updated. Hence, the sustainable levels are customizable in the settings. When I was formally educated I was trained to use average CPI of 3.1% for financial planning purposes, the default for the indicator is 2.5%, and the Medium article backtested and optimized a 2% sustainable inflation rate. Again, user-defined sustainable growth and rates are slightly subjective and will affect results.
I have not been trained or even had much experience with MetaTrader code, which is how this indicator was originally coded. See the original Medium article that inspired this indicator if you want to audit & compare code.
Hover over info panel for detailed information.
Features: Advanced info panel that performs Investment Clock analysis and offers additional hover info such as sector rotation suggestions. Customizable sustainable levels, growth input, and inflation input. Phase background coloring.
⚠ DISCLAIMER: Not financial advice. Not a trading system. DYOR. I am not affiliated with Medium, Macro Ops, iShares, or Merrill Lynch.
About the Author: I am a patent-holding inventor, a futures trader, a hobby PineScripter, and a former FINRA Registered Representative.
Historical US Bond Yield CurvePreface: I'm just the bartender serving today's freshly blended concoction; I'd like to send a massive THANK YOU to all the coders and PineWizards for the locally-sourced ingredients. I am simply a code editor, not a code author. Many thanks to these original authors!
Source 1 (Aug 8, 2019):
Source 2 (Aug 11, 2019):
About the Indicator: The term yield curve refers to the yields of U.S. treasury bills, notes, and bonds in order from shortest to longest maturity date. The yield curve describes the shapes of the term structures of interest rates and their respective terms to maturity in years. The slope of the yield curve tells us how the bond market expects short-term interest rates to move in the future based on bond traders' expectations about economic activity and inflation. The best use of the yield curve is to get a sense of the economy's direction rather than to try to make an exact prediction. This indicator plots the U.S. yield curve as maturity (x-axis/time) vs yield (y-axis/price) in addition to historical yield curves and advanced data tickers . The visual array of historical yield curves helps investors visualize shifts in the yield curve that are useful when identifying & forecasting economic conditions. The bond market can help predict the direction of the economy which can be useful in crafting your investment strategy. An inverted 10y/2y yield curve for durations longer than 5 consecutive trading days signals an almost certain recession on the horizon. An inversion happens when short-term bonds pay better than longer-term bonds. There is Federal Reserve Board data that suggests the 10y3m may be a better predictor of recessions.
Features: Advanced dual data ticker that performs curve & important spread analysis, plus additional hover info. Advanced yield curve data labels with additional hover info. Customizable historical curves and color theme.
‼ IMPORTANT: Hover over labels/tables for advanced information. Chart asset and timeframe may affect the yield curve results; I have found consistently accurate results using BINANCE:BTCUSDT on 1d timeframe. Historical curve lookbacks will have an effect on whether the curve analysis says the curve is bull/bear steepening/flattening, so please use appropriate lookbacks.
⚠ DISCLAIMER: Not financial advice. Not a trading system. DYOR. I am not affiliated with the original authors, TradingView, Binance, or the Federal Reserve Board.
About the Editor: I am a former FINRA Registered Representative, inventor/patent holder, futures trader, and hobby PineScripter.
Easy Scalping by JayKasunBINANCE:BTCUSDTPERP
This indicator can show stochastic RSI K and D line crosses and some candlestick patterns on chart.
You can use this indicator to scalping, check usage for more info. Always backtest before trading with your real money.
This indicator will also help mobile TradingView users to get an idea when getting stochastic RSI signals, they can use this indicator to check if stochastic RSI K and D crossed or not. ( Because they have limited area to view chart ) .
4 Exponential moving averages are there in the indicator with easy enable disable option. 9 , 21 , 55 , 100 is suggested as default values.
Meanings of signs in chart
Blue triangle bellow candle means it's a stochastic RSI K and D line cross in oversold level
Red triangle above candle means it's a stochastic RSI K and D line cross in overbought level
Green plus sign shows when EMA 50 crossover EMA 100
Red plus sign shows when EMA 50 cross bellow EMA 100
Features
You can enable candlestick pattern displaying when stochastic RSI K and D cross happen. Check indicator settings.
You can enable displaying ATR Trailing Stops in indicator settings.
Indicator will only show blue triangle after Green plus sign and Red triangles after Red plus sign
After you enable candlestick pattern option, stochastic RSI crosses with candlestick patterns will show in deferent colors. Blue triangle will turn into green and Red triangle into pink.
Usage
Use lower time frames like 5m or 15m
After green plus sign, if price retouched 21 EMA or 55 EMA and blue triangle appeared , you can enter a long position.
After red plus sign, if price retouched 21 EMA or 55 EMA and red triangle appeared , you can enter a short position.
Always wait for candle close . signs of chart can be changed when candle closing. ( Does repaint until candle close )
Use ATR trailing to get a stop loss price.
Use 1:1 or 1:0.5 Risk Reward ratio. Because it's scalping and lower time frame.
Use more indicators like RSI to get more confirmations ( like divergences ) before entering a trade. Its more reliable.
Candlestick Patterns Short names
H - Hammer
IH -Inverted Hammer
BE - Bullish Engulfing ( green triangle )
BE - Bearish Engulfing ( pink triangle )
BH - Bullish Harami ( green triangle )
BH - Bearish Harami ( pink triangle )
I have included ATR + Trailing Stops by SimpleCryptoLife and Candlestick Patterns Identified (updated 3/11/15) by repo32
this is a combination of multiple indicators
credit goes to original creators of above indicators
RSITrendStrategyI don't know if there is any strategy based on RSI cross over. The strategy is designed based on RSI crossover, considering RSI(5) and RSI(11), with RSI(6) to identify highs & lows.
I used this strategy to trade in Nifty 50 & Nifty bank indices. Whenever there is long mentioned on chart, I go for buying call option with premium near to 300, and placing stoploss of 50 on candle closing basis, vice versa.
Target is open until short is mentioned on the chart. Sometimes, i used standard pivot points as well to mark my targets and also to trail my trades.
[blackcat] L2 James Garofallou RSI In 4 DimLevel 2
Background
Traders’ Tips of September 2020, the focus is James Garofallou’s article in the September issue, “Tracking Relative Strength In Four Dimensions”.
Function
In “Tracking Relative Strength In Four Dimensions” in this issue, author James Garofallou introduces us to a new method of measuring the relative strength of a security. This new technique creates a much broader reference than would be obtained by using a single security or index and combines several dimensions, as the author calls them, into a single rank value. This study compares a security to another in four dimensions, as explained in the article. James Garofallou presents a metric for a security’s strength relative to 11 major market sectors and over several time periods. All this is squeezed into a single value. The first step is the RS2. It normalizes the security to a market index, then calculates four moving averages and encodes their relations in a returned number. I just modified it by using most BTC-correlated instruments to reflect how BTC response to their performance.
Remarks
This is a Level 2 free and open source indicator.
Feedbacks are appreciated.
Realtime FootprintThe purpose of this script is to gain a better understanding of the order flow by the footprint. To that end, i have added unusual features in addition to the standard features.
I use "Real Time 5D Profile by LucF" main engine to create basic footprint(profile type) and added some popular features and my favorites.
This script can only be used in realtime, because tradingview doesn't provide historical Bid/Ask date.
Bid/Ask date used this script are up/down ticks.
This script can only be used by time based chart (1m, 5m , 60m and daily etc)
This script use many labels and these are limited max 500, so you can't display many bars.
If you want to display foot print bars longer, turn off the unused sub-display function.
Default setting is footprint is 25 labels, IB count is 1, COT high and Ratio high is 1, COT low and Ratio low is 1 and Delta Box Ratio Volume is 1 , total 29.
plus UA , IB stripes , ladder fading mark use several labels.
///////// General Setting ///////////
Resets on Volume / Range bar
: If you want to use simple time based Resets on, please set Total Volume is 0.
Your timeframe is always the first condition. So if you set Total Volume is 1000, both conditions(Volume >= 1000 and your timeframe start next bar) must be met. (that is, new footprint bar doesn't start at when total volume = exactly 1000).
Ticks per row and Maximum row of Bar
: 1 is minimum size(tick). "Maximum row of Bar" decide the number of rows used in one footprint. 1 row is created from 1 label, so you need to reduce this number to display many footprints (Max label is 500).
Volume Filter and For Calculation and Display
: "Volume Filter" decide minimum size of using volume for this script.
"For Calculation and Display" is used to convert volume to an integer.
This script only use integer to make profile look better (I contained Bid number and Ask number in one row( one label) to saving labels. This require to make no difference in width by the number of digits and this script corresponds integers from 0 to 3 digits).
ex) Symbol average volume size is from 0.0001 to 0.001. You decide only use Volume >= 0.0005 by "Volume Filter".
Next, you convert volume to integer, by setting "For Calculation and Display" is 1000 (0.0005 * 1000 = 5).
If 0.00052 → 5.2 → 5, 0.00058 → 5.8 → 6 (Decimal numbers are rounded off)
This integer is used to all calculation in this script.
//////// Main Display ///////
Footprint, Total, Row Delta, Diagonal Delta and Profile
: "Footprint" display Ask and Bid per row. "Total" display Ask + Bid per row.
"Row Delta" display Ask - Bid per row. "Diagonal Delta" display Ask(row N) - Bid(row N -1) per row.
Profile display Total Volume(Ask + Bid) per row by using Block. Profile Block coloring are decided by Row Delta value(default: positive Row Delta (Ask > Bid) is greenish colors and negative Row Delta (Ask < Bid) is reddish colors.)
Volume per Profile Block, Row Imbalance Ratio and Delta Bull/Bear/Neutral Colors
: "Volume per Profile Block" decide one block contain how many total volume.
ex) When you set 20, Total volume 70 display 3 block.
The maximum number of blocks that can be used per low is 20.
So if you set 20, Total volume 400 is 20 blocks. total volume 800 is 20 blocks too.
"Row Imbalance Ratio" decide block coloring. The row imbalance is that the difference between Ask and Bid (row delta) is large.
default is x3, x2 and x1. The larger the difference, the brighter the color.
ex) Ask 30 Bid 10 is light green. Ask 20 Bid 10 is green. Ask 11 Bid 10 is dark green.
Ask 0 Bid 1 is light red. Ask 1 Bid 2 is red. ask 30 Bid 59 is dark green.
Ask 10 Bid 10 is neutral color(gray)
profile coloring is reflected same row's other elements(Ask, Bid, Total and Delta) too.
It's because one label can only use one text color.
/////// Sub Display ///////
Delta, total and Commitment of Traders
: "Delta" is total Ask - total Bid in one footprint bar. Total is total Ask + total Bid in one footprint bar.
"Commitment of traders" is variation of "Delta". COT High is reset to 0 when current highest is touched. COT Low is opposite.
Basic concept of Delta is to compare price with Delta. Ordinary, when price move up, delta is positive. Price move down is negative delta.
This is because market orders move price and market orders are counted by Delta (although this description is not exactly correct).
But, sometimes prices do not move even though many market orders are putting pressure on price , or conversely, price move strongly without many market orders.
This is key point. Big player absorb market orders by iceberg order(Subdivide large orders and pretend to be small limit orders.
Small limit orders look weak in the order book, but they are added each time you fill, so they are more powerful than they look.), so price don't move.
On the other hand, when the price is moving easily, smart players may be aiming to attract and counterattack to a better price for them.
It's more of a sport than science, and there's always no right response. Pay attention to the relationship between price, volume and delta.
ex) If COT Low is large negative value, it means many sell market orders is coming, but iceberg order is absorbing their attack at limit order.
you should not do buy entry, only this clue. but this is one of the hints.
"Delta, Box Ratio and Total texts is contained same label and its color are "Delta" coloring. Positive Delta is Delta Bull color(green),Negative Delta is Delta Bear Color
and Delta = 0 is Neutral Color(gray). When Delta direction and price direction are opposite is Delta Divergence Color(yellow).
I didn't add the cumulative volume delta because I prefer to display the CVD line on the price chart rather than the number.
Box Ratio , Box Ratio Divisor and Heavy Box Ratio Ratio
: This is not ordinary footprint features, but I like this concept so I added.
Box Ratio by Richard W. Arms is simple but useful tool. calculation is "total volume (one bar) divided by Bar range (highest - lowest)."
When Bull and bear are fighting fiercely this number become large, and then important price move happen.
I made average BR from something like 5 SMA and if current BR exceeds average BR x (Heavy Box Ratio Ratio), BR box mark will be filled.
Box Ratio Divisor is used to good looking display(BR multiplied by Box Ratio Divisor is rounded off and displayed as an integer)
Diagonal Imbalance Count , D IB Mark and D IB Stripes
: Diagonal Imbalance is defined by "Diagonal Imbalance Ratio".
ex) You set 2. When Ask(row N) 30 Bid(row N -1)10, it's 30 > 10*2, so positive Diagonal Imbalance.
When Ask(row N) 4 Bid(row N -1)9, it's 4*2 < 9, so negative Diagonal Imbalance.
This calculation does not use equals to avoid Ask(row N) 0 Bid(row N -1)0 became Diagonal Imbalance.
Ask(row N) 0 Bid(row N -1)0, it's 0 = 0*2, not Diagonal Imbalance. Ask(row N) 10 Bid(row N -1)5, it's 10 = 5*2, not Diagonal Imbalance.
"D IB Mark" emphasize Ask or Bid number which is dominant side(Winner of Diagonal Imbalance calculation), by under line.
"Diagonal Imbalance Count" compare Ask side D IB Mark to Bid side D IB Mark in one footprint.
Coloring depend on which is more aggressive side (it has many IB Mark) and When Aggressive direction and price direction are opposite is Delta Divergence Color(yellow).
"D IB Stripes" is a function that further emphasizes with an arrow Mark, when a DIB mark is added on the same side for three consecutive row. Three consecutive arrow is added at third row.
Unfinished Auction, Ratio Bounds and Ladder fading Mark
: "Unfinished Auction" emphasize highest or lowest row which has both Ask and Bid, by Delta Divergence Color(yellow) XXXXXX mark.
Unfinished Auction sometimes has magnet effect, price may touch and breakout at UA side in the future.
This concept is famous as profit taking target than entry decision.
But, I'm interested in the case that Big player make fake breakout at UA side and trapped retail traders, and then do reversal with retail traders stop-loss hunt.
Anyway, it's not stand alone signal.
"Ratio Bounds" gauge decrease of pressure at extreme price. Ratio Bounds High is number which second highest ask is divided by highest ask.
Ratio Bounds Low is number which second lowest bid is divided by lowest bid. The larger the number, the less momentum the price has.
ex)first footprint bar has Ratio Bounds Low 2, second footprint bar has RBL 4, third footprint bar has RBL 20.
This indicates that the bear's power is gradually diminishing.
"Ladder fading mark" emphasizes the decrease of the value in 3 consecutive row at extreme price. I added two type Marks.
Ask/Bid type(triangle Mark) is Ask/Bid values are decreasing of three consecutive row at extreme price.
Row Imbalance type(Diamond Mark) are row Imbalance values are decreasing of three consecutive row at extreme price.
ex)Third lowest Bid 40, second lowest Bid 10 and lowest Bid 5 have triangle up Mark. That is bear's power is gradually diminishing.
(This Mark only check Bid value at lowest price and Ask value at highest price).
Third highest row delta + 60, second highest row delta + 5, highest delta - 20 have diamond Mark. That is Bull's power is gradually diminishing.
Sub display use Delta colors at bottom of Sub display section.
////// Candle & POC /////////
candle and POC
: Ordinary, "POC" Point of Control is row of largest total volume, but this script'POC is volume weighted average.
This is because the regular POC was visually displayed by the profile ,and I was influenced LucF's ideas.
POC coloring is decided in relation to the previous POC. When current POC is higher than previous POC, color is UP Bar Color(green).
In the opposite case, Down Bar color is used.
POC Divergence Color is used when Current POC is up but current bar close is lower than open (Down price Bar),or in the opposite case.
POC coloring has option also highlight background by Delta Divergence Color(yellow). but bg color is displayed at your time frame current price bar not current footprint bar.
The basic explanation is over.
I add some image to promote understanding basic ideas.
Frog in Pan IndicatorWhat is it?
This indicator is the percent of negative days minus the percent of positive days in a year multiplied by the sign of the overall return of the lookback (365 days for crypto and 252 days for stocks).
FIP = sign(return of lookback) *
What is it used for?
This indicator is used as a quality screener for momentum stocks. It is based behind the ideas in Wesley Gray & Jack Vogel's book: Quantitative Momentum: A Practitioner's Guide to Building a Momentum-Based Stock Selection System that iterates that quality momentum stocks consist of steady uptrends (where more days are positive rather than negative) as opposed to characteristics of "lottery-like" stocks that are "jumpy" and more volatile. More research behind this indicator can be found here
How to use
In the indicator settings, the default lookback parameter is set to 365 days for analysis on cryptocurrencies and was used on a daily timeframe. If you want to use this indicator on individual stocks, it is best to change this lookback to 252 days. The more negative the value is, the higher quality of momentum it is.
Strings█ OVERVIEW
This library provides string manipulation functions to complement the Pine Script™ `str.*()` built-in functions.
█ CONCEPTS
At the time our String Manipulation Framework was published, there was little in the way of built-in functions to manipulate strings. Since then, we have witnessed several meaningful developments on this front by the nimble Pine team. The newly released functions (including the ones in this blog post ) have deprecated most of our functions. This library captures the small handful of functions we think are still pertinent. It is worth noting that, thanks to the new string built-ins in Pine Script™, these functions greatly outperform their earlier counterparts, both performance-wise and because they can return values of simple form, which are a necessity in some circumstances, such as when used as arguments to some parameters of request.security() .
█ NOTES
`leftOf()` and `rightOf()`
Using the functions in this library is straightforward. The `leftOf()` and `rightOf()` functions extract the part of a string that is to the left or to the right of another string or character. This can be useful to separate the exchange and symbol components of user-entered tickers, for example. The separation is done with the underused str.match() , which can use regular expressions (or regex) to scan a string and separate characters based on a search pattern. The possibilities with regex are virtually endless; they can be used in “find and replace” applications, or to validate phone numbers, emails, passwords, credit card numbers, dates, etc. Note that Pine supports the same regex features as Java .
String operations in Pine Script™
The Pine Script™ runtime is optimized for number crunching. You can thus optimize script performance by limiting operations on strings whenever possible. This includes declaring strings with the var keyword, and containing re-assignments to local if blocks using barstate.islast , for example.
Look first. Then leap.
█ FUNCTIONS
leftOf(str, separator, occurrence)
Extracts the part of the `str` string that is left of the nth `occurrence` of the `separator` string.
Parameters:
str : (series string) Source string.
separator : (series string) Separator string.
occurrence : (series int) Occurrence of the separator string. Optional. The default value is zero (the 1st occurrence).
Returns: (string) The extracted string.
rightOf(str, separator, occurrence)
Extracts the part of the `str` string that is right of the nth `occurrence` of the `separator` string.
Parameters:
str : (series string) Source string.
separator : (series string) Separator string.
occurrence : (series int) Occurrence of the separator string. Optional. The default value is zero (the 1st occurrence).
Returns: (string) The extracted string.
Ratings AlgoThe ratings algo is my discount version of the many paid-for algorithms put out by numerous different companies. A technical "rating" (by default between -10 and 10) is produced for each candle, telling the user when to buy, sell, or hold. I took 11 of my personal favorite indicators to develop a rating system. They are:
50/200 SMA crossover
10/20 SMA crossover
10/20 LSMA crossover
10/20 EMA crossover
"Arnold" a rate-of-change analysis of a smoothed LSMA
PVT and OBV momentum
MACD
RSI
DMI
Fisher Transform
The ratings system is very basic (a more complex, detailed version will be coming in the future!) where each indicator returns -1, 0, or 1, and the MAs and Oscillators are stratified with a user-defined weighting. The total calculation is based on the function:
maweight * (average of MA ratings) + oscillator weight * (average of osc ratings)
If the total value > user-defined threshold, the bar is teal, and if > 2.5 * threshold, is green, and vice versa for orange/red respectively. Purple is given if the total value is close to zero.
"Strong" signals are printed if the bar changes to either green or red and exits are printed if the bars change from green/red to any other color.
A table is also produced showing what each indicator is indicating, either "Buy" "Sell" or "Hold.
Reversal Bands are printed, intended to be used as areas where a trade might be exited if the market is sideways. If a Strong Buy signal is produced, it may be a good idea to enter the trade, and hold until the price enters the reversal bands, then hold until a candle closes outside the band for the first time.
This indicator truly shines in trending markets (like most indicators), but with very fast-acting exit signals and reversal zones, will facilitate minimal losses and possibly even profits in sideways markets.
