רגרסיה ליניארית
Linear Regression Slope HistogramThis is the slope of linear regression plotted as a histogram. It has a threshold for the slope in case there is a minimum slope amount that the user wants for it to show green or red. The color shows a dim and bright effect depending on the direction of the slope to better show its rise and fall. You can set the length of the slope in the input setting screen.
Some interesting things about linear regression slope that I found out: It kind of looks like the histogram of ADX or the directional movement lines depending on the length used for the slope. According to Tushar Chande, linear regression can be used as an alternative of finding trend direction, although it is hard to say what length to use for it, I haven't been able to figure out a good way to apply it yet. According to Bulkowski, linear regression of the volume can be used with certain chart patterns that he listed to increase their chance of success by taking trades where the linear regression line points towards, so if you use volume as a source on this indicator you can take trades in whatever said direction. Got any other ideas on how to use linear regression? Feel free to let me knows, would gladly appreciate it.
Variable Linear Regression With Pearsons R OscillatorThis is the Oscillator that is meant to go with the Variable Linear Regression With Pearsons R Script. Set this to match the settings you have on the other script.
To use this, you want to do trades something like this:
The bottom -0.80 (green line) means that the trend is very strong in the upward direction. This almost always means it's about to finish its trend soon and reverse the other direction. If you see the other linear regression lines also coincide with this result it would best to get ready to reverse your position and do a short once some other indicators show you it is turning the other direction in addition to this.
The reverse for longing would be the case after a large downtrend and the 0.80 line (red line) is touched by many of the linear regressions, usually it means the downtrend is ending and there is a good buy opportunity soon.
This is called a variable linear regression systems because you can set as many variable linear regressions as you want. I have capped it at 10 but you can modify the code to raise this if really necessary. Every color is customizable for each different linear regression trend.
Variable Linear Regression With Pearsons RThis script allows you to set a variable number of linear regressions with alternating colors you can select. It will have a partner oscillator script shortly so you can see how the different trends change and interact with each other over time.
The script is currently very slow if you set the period for the linear regression too high. I recommend not going past 100. Works very well on the high time frames for this reason.
Anyone that knows of a faster way to run these calculations please let me know!
Donchian BlasterRelease Note:
Keeping in mind of getting full potential of Donchian Channels, As part of this script, Linear Regression is used as primary source to identify trend and execute the trades.
Hull Moving Average given as alternative option in place of Linear regression.
Linear Regression:
Linear regression used to identify trend, trade setup, and stop. Based on this, Color fillings on Donchian channels is updated. That will give clear idea of strength or weakness in any trading instruments.
HMA:
HMA used as alternative to identify trend, trade setup, and stop. Based on this, Color fillings on Donchian channels is updated. That will give clear idea of strength or weakness in any trading instruments.
Donchian Channels:
As it is one of the oldest trend riding system, combined here with differentiation in color will help to go with the trend.
Inputs:
Input periods can be changed by users/traders as per their understanding and observations.
//Disclaimer: Idea of publishing this script is to identify the strength of the instrument using multiple confirmation.
//Disclaimer: Using this indicator, changing inputs, and trading decisions are up to the users/traders.
//Courtesy: Thanks to Richard Donchian, Alan Hull, and author of LSMA as this indicator/script inspired by Donchian Channels, Hull Moving Average, and LSMA
Linear Regression (Log Scale)This code is a slightly modified version of Tradingviews' built-in "linear regression" script which can be correctly plotted on logarithmic charts.
Logarithmic regressionLogarithmic regression (or known as Tseng's tunnels), is used to model data where growth or decay accelerates rapidly at first and then slows over time.
This model is for the long term series data (such as 10 years time span).
The user can consider entering the market when the price below 25% or 5% confidence and consider take profit when the price goes above 75% or 95% confidence line.
Technical issues
*The user have to pan over the chart from the beginning to the end of the study range (such as 10 years of bars) so the pine script could generate those regression lines on the chart.
*If on the chart the number of bar is less than the lookback period, it won't generate any lines as well
Adjacent HMA StopRelease Note:
This indicator script setup is published to identify the strength and ride the trends.
HMA:
HMA 100 period is used as default. However this can be changed as per wish. Major turning points or decisions can be made using this HMA line. Purple color is used to identify this. This can be used as the final extreme stop for any trend before it turns.
Adjacent Line:
Blue colored Adjacent line can be used to determine the trend strength or ride the trend till it slows down. When trending move happens it can be ride till the candles comes within this Adjacent line. And then sideways move or the moves between HMA & Adjacent line can be traded
Stop:
Dotted lines in Red and Green colors are used as very tight stop. This can be also used as first level of profit booking when very strong move happens.
Trend View:
There is an optional 'Trend View' which can be used to determined the trend.
Disclaimer:
//This script/indicator published with the idea of finding the strength of any instruments. Trade decisions and execution are up to the traders/users as per their understanding
//Courtesy: Thanks to Alan Hull and Richard Donchian as some of the concepts are inspired from them
Polynomial Regression Bands + Channel [DW]This is an experimental study designed to calculate polynomial regression for any order polynomial that TV is able to support.
This study aims to educate users on polynomial curve fitting, and the derivation process of Least Squares Moving Averages (LSMAs).
I also designed this study with the intent of showcasing some of the capabilities and potential applications of TV's fantastic new array functions.
Polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modeled as a polynomial of nth degree (order).
For clarification, linear regression can also be described as a first order polynomial regression. The process of deriving linear, quadratic, cubic, and higher order polynomial relationships is all the same.
In addition, although deriving a polynomial regression equation results in a nonlinear output, the process of solving for polynomials by least squares is actually a special case of multiple linear regression.
So, just like in multiple linear regression, polynomial regression can be solved in essentially the same way through a system of linear equations.
In this study, you are first given the option to smooth the input data using the 2 pole Super Smoother Filter from John Ehlers.
I chose this specific filter because I find it provides superior smoothing with low lag and fairly clean cutoff. You can, of course, implement your own filter functions to see how they compare if you feel like experimenting.
Filtering noise prior to regression calculation can be useful for providing a more stable estimation since least squares regression can be rather sensitive to noise.
This is especially true on lower sampling lengths and higher degree polynomials since the regression output becomes more "overfit" to the sample data.
Next, data arrays are populated for the x-axis and y-axis values. These are the main datasets utilized in the rest of the calculations.
To keep the calculations more numerically stable for higher periods and orders, the x array is filled with integers 1 through the sampling period rather than using current bar numbers.
This process can be thought of as shifting the origin of the x-axis as new data emerges.
This keeps the axis values significantly lower than the 10k+ bar values, thus maintaining more numerical stability at higher orders and sample lengths.
The data arrays are then used to create a pseudo 2D matrix of x power sums, and a vector of x power*y sums.
These matrices are a representation the system of equations that need to be solved in order to find the regression coefficients.
Below, you'll see some examples of the pattern of equations used to solve for our coefficients represented in augmented matrix form.
For example, the augmented matrix for the system equations required to solve a second order (quadratic) polynomial regression by least squares is formed like this:
(∑x^0 ∑x^1 ∑x^2 | ∑(x^0)y)
(∑x^1 ∑x^2 ∑x^3 | ∑(x^1)y)
(∑x^2 ∑x^3 ∑x^4 | ∑(x^2)y)
The augmented matrix for the third order (cubic) system is formed like this:
(∑x^0 ∑x^1 ∑x^2 ∑x^3 | ∑(x^0)y)
(∑x^1 ∑x^2 ∑x^3 ∑x^4 | ∑(x^1)y)
(∑x^2 ∑x^3 ∑x^4 ∑x^5 | ∑(x^2)y)
(∑x^3 ∑x^4 ∑x^5 ∑x^6 | ∑(x^3)y)
This pattern continues for any n ordered polynomial regression, in which the coefficient matrix is a n + 1 wide square matrix with the last term being ∑x^2n, and the last term of the result vector being ∑(x^n)y.
Thanks to this pattern, it's rather convenient to solve the for our regression coefficients of any nth degree polynomial by a number of different methods.
In this script, I utilize a process known as LU Decomposition to solve for the regression coefficients.
Lower-upper (LU) Decomposition is a neat form of matrix manipulation that expresses a 2D matrix as the product of lower and upper triangular matrices.
This decomposition method is incredibly handy for solving systems of equations, calculating determinants, and inverting matrices.
For a linear system Ax=b, where A is our coefficient matrix, x is our vector of unknowns, and b is our vector of results, LU Decomposition turns our system into LUx=b.
We can then factor this into two separate matrix equations and solve the system using these two simple steps:
1. Solve Ly=b for y, where y is a new vector of unknowns that satisfies the equation, using forward substitution.
2. Solve Ux=y for x using backward substitution. This gives us the values of our original unknowns - in this case, the coefficients for our regression equation.
After solving for the regression coefficients, the values are then plugged into our regression equation:
Y = a0 + a1*x + a1*x^2 + ... + an*x^n, where a() is the ()th coefficient in ascending order and n is the polynomial degree.
From here, an array of curve values for the period based on the current equation is populated, and standard deviation is added to and subtracted from the equation to calculate the channel high and low levels.
The calculated curve values can also be shifted to the left or right using the "Regression Offset" input
Changing the offset parameter will move the curve left for negative values, and right for positive values.
This offset parameter shifts the curve points within our window while using the same equation, allowing you to use offset datapoints on the regression curve to calculate the LSMA and bands.
The curve and channel's appearance is optionally approximated using Pine's v4 line tools to draw segments.
Since there is a limitation on how many lines can be displayed per script, each curve consists of 10 segments with lengths determined by a user defined step size. In total, there are 30 lines displayed at once when active.
By default, the step size is 10, meaning each segment is 10 bars long. This is because the default sampling period is 100, so this step size will show the approximate curve for the entire period.
When adjusting your sampling period, be sure to adjust your step size accordingly when curve drawing is active if you want to see the full approximate curve for the period.
Note that when you have a larger step size, you will see more seemingly "sharp" turning points on the polynomial curve, especially on higher degree polynomials.
The polynomial functions that are calculated are continuous and differentiable across all points. The perceived sharpness is simply due to our limitation on available lines to draw them.
The approximate channel drawings also come equipped with style inputs, so you can control the type, color, and width of the regression, channel high, and channel low curves.
I also included an input to determine if the curves are updated continuously, or only upon the closing of a bar for reduced runtime demands. More about why this is important in the notes below.
For additional reference, I also included the option to display the current regression equation.
This allows you to easily track the polynomial function you're using, and to confirm that the polynomial is properly supported within Pine.
There are some cases that aren't supported properly due to Pine's limitations. More about this in the notes on the bottom.
In addition, I included a line of text beneath the equation to indicate how many bars left or right the calculated curve data is currently shifted.
The display label comes equipped with style editing inputs, so you can control the size, background color, and text color of the equation display.
The Polynomial LSMA, high band, and low band in this script are generated by tracking the current endpoints of the regression, channel high, and channel low curves respectively.
The output of these bands is similar in nature to Bollinger Bands, but with an obviously different derivation process.
By displaying the LSMA and bands in tandem with the polynomial channel, it's easy to visualize how LSMAs are derived, and how the process that goes into them is drastically different from a typical moving average.
The main difference between LSMA and other MAs is that LSMA is showing the value of the regression curve on the current bar, which is the result of a modelled relationship between x and the expected value of y.
With other MA / filter types, they are typically just averaging or frequency filtering the samples. This is an important distinction in interpretation. However, both can be applied similarly when trading.
An important distinction with the LSMA in this script is that since we can model higher degree polynomial relationships, the LSMA here is not limited to only linear as it is in TV's built in LSMA.
Bar colors are also included in this script. The color scheme is based on disparity between source and the LSMA.
This script is a great study for educating yourself on the process that goes into polynomial regression, as well as one of the many processes computers utilize to solve systems of equations.
Also, the Polynomial LSMA and bands are great components to try implementing into your own analysis setup.
I hope you all enjoy it!
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NOTES:
- Even though the algorithm used in this script can be implemented to find any order polynomial relationship, TV has a limit on the significant figures for its floating point outputs.
This means that as you increase your sampling period and / or polynomial order, some higher order coefficients will be output as 0 due to floating point round-off.
There is currently no viable workaround for this issue since there isn't a way to calculate more significant figures than the limit.
However, in my humble opinion, fitting a polynomial higher than cubic to most time series data is "overkill" due to bias-variance tradeoff.
Although, this tradeoff is also dependent on the sampling period. Keep that in mind. A good rule of thumb is to aim for a nice "middle ground" between bias and variance.
If TV ever chooses to expand its significant figure limits, then it will be possible to accurately calculate even higher order polynomials and periods if you feel the desire to do so.
To test if your polynomial is properly supported within Pine's constraints, check the equation label.
If you see a coefficient value of 0 in front of any of the x values, reduce your period and / or polynomial order.
- Although this algorithm has less computational complexity than most other linear system solving methods, this script itself can still be rather demanding on runtime resources - especially when drawing the curves.
In the event you find your current configuration is throwing back an error saying that the calculation takes too long, there are a few things you can try:
-> Refresh your chart or hide and unhide the indicator.
The runtime environment on TV is very dynamic and the allocation of available memory varies with collective server usage.
By refreshing, you can often get it to process since you're basically just waiting for your allotment to increase. This method works well in a lot of cases.
-> Change the curve update frequency to "Close Only".
If you've tried refreshing multiple times and still have the error, your configuration may simply be too demanding of resources.
v4 drawing objects, most notably lines, can be highly taxing on the servers. That's why Pine has a limit on how many can be displayed in the first place.
By limiting the curve updates to only bar closes, this will significantly reduce the runtime needs of the lines since they will only be calculated once per bar.
Note that doing this will only limit the visual output of the curve segments. It has no impact on regression calculation, equation display, or LSMA and band displays.
-> Uncheck the display boxes for the drawing objects.
If you still have troubles after trying the above options, then simply stop displaying the curve - unless it's important to you.
As I mentioned, v4 drawing objects can be rather resource intensive. So a simple fix that often works when other things fail is to just stop them from being displayed.
-> Reduce sampling period, polynomial order, or curve drawing step size.
If you're having runtime errors and don't want to sacrifice the curve drawings, then you'll need to reduce the calculation complexity.
If you're using a large sampling period, or high order polynomial, the operational complexity becomes significantly higher than lower periods and orders.
When you have larger step sizes, more historical referencing is used for x-axis locations, which does have an impact as well.
By reducing these parameters, the runtime issue will often be solved.
Another important detail to note with this is that you may have configurations that work just fine in real time, but struggle to load properly in replay mode.
This is because the replay framework also requires its own allotment of runtime, so that must be taken into consideration as well.
- Please note that the line and label objects are reprinted as new data emerges. That's simply the nature of drawing objects vs standard plots.
I do not recommend or endorse basing your trading decisions based on the drawn curve. That component is merely to serve as a visual reference of the current polynomial relationship.
No repainting occurs with the Polynomial LSMA and bands though. Once the bar is closed, that bar's calculated values are set.
So when using the LSMA and bands for trading purposes, you can rest easy knowing that history won't change on you when you come back to view them.
- For those who intend on utilizing or modifying the functions and calculations in this script for their own scripts, I included debug dialogues in the script for all of the arrays to make the process easier.
To use the debugs, see the "Debugs" section at the bottom. All dialogues are commented out by default.
The debugs are displayed using label objects. By default, I have them all located to the right of current price.
If you wish to display multiple debugs at once, it will be up to you to decide on display locations at your leisure.
When using the debugs, I recommend commenting out the other drawing objects (or even all plots) in the script to prevent runtime issues and overlapping displays.
RSI with Self-Adjusting Linear Regression Bands (Expo)RSI with Self-Adjusting Linear Regression Bands (Expo) makes use of RSI and Linear Regression to create an RSI that follows the current trend. The indicator has an upper and lower self-adjusting Linear Regression Band that act as RSI boundaries.
HOW TO USE
The indicator can be used in multiple ways, for instance, to find overbought and oversold areas. Or to identify trends as well as pullbacks in trends.
INDICATOR IN ACTION
This indicator is a modification of RafaelZioni's work "Linear Regression Trend bands"
Credit to: RafaelZioni
I hope you find this indicator useful , and please comment or contact me if you like the script or have any questions/suggestions for future improvements. Thanks!
I will continually work on this indicator, so please share your experience and feedback as it will enable me to make even better improvements. Thanks to everyone that has already contacted me regarding my scripts. Your feedback is valuable for future developments!
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Disclaimer
Copyright by Zeiierman.
The information contained in my scripts/indicators/ideas does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, or individual’s trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My scripts/indicators/ideas are only for educational purposes!
Responsive Linear Regression ChannelsThis is a linear trend indicator that can:
- automagically adjust to different chart resolutions
- snap to day/session boundaries on intraday
- totally not do the above and use ye olde good fixed lookback window
- show the regression trend and its RMSE channels
- disregard the trend and just show the period average and standard deviation lines (often acting as support and resistance)
The main idea was to allow you to quickly view linear trends for different time periods without having to calculate the required amount of bars.
By default, you will be able to quickly switch between 1-3 months, 6 months, 1 year, 2 years and 5 years of data, as well as various daily lookback periods.
Please adjust resolution settings to your liking.
Caveats:
- More data means more noise and slower calculations.
- HLC3 source is recommended, especially at resolutions above hourly.
- Trend will be slightly different if you use extended hours.
- If you use extended hours and don't have a data subscription, trend will be SUBSTANTIALLY different, especially at smaller resolutions like 1m.
- You might want to use 3.5 days per week for crypto (it's 7 days by default).
Known problems:
- If you can't see the trendlines, scroll to the left or zoom out - the origin could be outside of your viewport.
- Futures' sessions start on the previous day. We currently snap only to day boundaries, not session breaks.
- UVXY and the like that have extremely high price datapoints. The calculations will be wrong on some resolutions.
- Replay can freeze circa bar_index 244 — disable smart lookback and keep your window under 240 bars if you encounter this.
ACKNOWLEDGEMENTS:
Many thanks to alexgrover for his extremely efficient functions (used with permission)
My BTC log curveLogarithmic regression of the USD price of Bitcoin , calculated according to the equation:
y=A*exp(beta*x^lambda + c) + m*x + b
where x is the number of days since the genesis block. All parameters are editable in the script options.
Linear Regression ChannelsThis script tries to find when a trading channel is developing and once is developed it will save it for later use on 2 variables up and down channel. You can access the last 2 channels for comparisons but this could be extensible for more channels.
You can choose the delta for the alignment accuracy of the channel.
The error for the difference between channels and the deviation for the width of the channel based on standard deviation.
Offset is for the alignment basically if the alignment is the same at the begining and at the offset the algo consider that a Channel.
Function Polynomial Regression StrategyTo be clear I'm using the code from Richard Santos for the functional polynomial regression. I really loved his script idea (given that I'm into linear regression myself). I took his code and made a strategy which applies to bitcoin on the 5 minute chart (but you could adjust this for any asset), and you could make this work on anytime frame by adjusting the 'length' property of the regression until you get good results.
This strategy is very simple.
I drew lines to represent the bottom most part of the regression (green line), and top most part (red line). If the close crosses under the red line then you short. The reverse if it crosses above the green line then you go long.
Very simple but effective. To understand this script be sure to add Richard santos function polynomial regression so you can see what is going on and what I'm trying to do.
For example, on BTC with a length of 61 if you bought and held you would have only had 5% gain, instead with this strategy you are looking at very close to 60% gains! Now this could be a weird fluke of over-optimizing but I'm not sure this is the case because you can adjust the length from there +/- about 10 and still have good results. It's just certain lengths are the correct trading rhythms for different assets.
I'm excited to see how you guys use this and if you have any success. Be sure to thank Richard Santos for his great work!
Trend Following with Moving AveragesHello Traders,
With the info "Trend is Your Friend ", you should not take position against the trend. This script checks multipte moving averages if they are above/below the closing price and try to find trend. The moving averages with the length 8, 13, 21, 34, 55, 89, 144, 233, 377 used. these are fibonacci numbers, but optionally you can change the lengths of each moving averages. while it's green you better take long positions, while it's red you better take short positions according to other indcators or tools.
Optionally you have "smoothing" option to get rid of whipsaws. it's enabled by default.
You have option to use following moving average types: EMA, SMA, RMA, WMA, VWMA. by default it's EMA
Also the script has "Resolution" option. with this option you can get the trend for other time frames, in following example 1h was set as for higher time frame on 15m chart:
This should not be used as buy/sell signal indicators as it's tries to find trend but not entry points, you should use other indicators (such RSI, Momentum) or other tools to find buy/sell signals.
Enjoy!
Angle & Price FlowThis is a simple end of the day indicator based on open and close values.
Angles:
Identifying narrow angles helps to find breakouts
Maximum angle = High chance of reversal
Minimum angle = Chance of breakouts on the next day or upcoming days.
Price Flow:
I personally use price flow to simplify the flow of candlestick chart and to identify breakouts using rounding bottoms and rounding top patterns, sharp corners for reversals.
Features:
Multi time frame
Labels
Hide previous period
Combine it with other indicators for better result.
Angle & Price FlowThis is a simple end of the day indicator based on open and close values.
Angles:
Identifying narrow angles helps to find breakouts
Maximum angle = High chance of reversal
Minimum angle = Chance of breakouts on the next day or upcoming days.
Price Flow:
I personally use price flow to simplify the flow of candlestick chart and to identify breakouts using rounding bottoms and rounding top patterns, sharp corners for reversals.
Features:
Multi time frame
Labels
Hide previous period
Combine it with other indicators for better result.
Linear Regression (All Data)The tool plots a linear regression line using the entire history of an instrument on chart. There are may be issues on intraday timeframes less then 1h. On daily, weekly and monthly charts it works without problem.
If an instrument has a lot of data points, you may not see the line (this is TV feature):
To fix that you need to scroll your chart to the left and find the starting point of the line:
And then do an auto-scroll to the last bar:
Bitcoin Block Height (Total Blocks)Bitcoin Block Height by RagingRocketBull 2020
Version 1.0
Differences between versions are listed below:
ver 1.0: compare QUANDL Difficulty vs Blockchain Difficulty sources, get total error estimate
ver 2.0: compare QUANDL Hash Rate vs Blockchain Hash Rate sources, get total error estimate
ver 3.0: Total Blocks estimate using different methods
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This indicator estimates Bitcoin Block Height (Total Blocks) using Difficulty and Hash Rate in the most accurate way possible, since
QUANDL doesn't provide a direct source for Bitcoin Block Height (neither QUANDL:BCHAIN, nor QUANDL:BITCOINWATCH/MINING).
Bitcoin Block Height can be used in other calculations, for instance, to estimate the next date of Bitcoin Halving.
Using this indicator I demonstrate:
- that QUANDL data is not accurate and differ from Blockchain source data (industry standard), but still can be used in calculations
- how to plot a series of data points from an external csv source and compare it with another source
- how to accurately estimate Bitcoin Block Height
Features:
- compare QUANDL Difficulty source (EOD, D1) with external Blockchain Difficulty csv source (EOD, D1, embedded)
- show/hide Quandl/Blockchain Difficulty curves
- show/hide Blockchain Difficulty candles
- show/hide differences (aqua vertical lines)
- show/hide time gaps (green vertical lines)
- count source differences within data range only or for the whole history
- multiply both sources by alpha to match before comparing
- floor/round both matched sources when comparing
- Blockchain Difficulty offset to align sequences, bars > 0
- count time gaps and missing bars (as result of time gaps)
WARNING:
- This indicator hits the max 1000 vars limit, adding more plots/vars/data points is not possible
- Both QUANDL/Blockchain provide daily EOD data and must be plotted on a daily D1 chart otherwise results will be incorrect
- current chart must not have any time gaps inside the range (time gaps outside the range don't affect the calculation). Time gaps check is provided.
Otherwise hardcoded Blockchain series will be shifted forward on gaps and the whole sequence become truncated at the end => data comparison/total blocks estimate will be incorrect
Examples of valid charts that can run this indicator: COINBASE:BTCUSD,D1 (has 8 time gaps, 34 missing bars outside the range), QUANDL:BCHAIN/DIFF,D1 (has no gaps)
Usage:
- Description of output plot values from left to right:
- c_shifted - 4x blockchain plotcandles ohlc, green/black (default na)
- diff - QUANDL Difficulty
- c_shifted - Blockchain Difficulty with offset
- QUANDL Difficulty multiplied by alpha and rounded
- Blockchain Difficulty multiplied by alpha and rounded
- is_different, bool - cur bar's source values are different (1) or not (0)
- count, number of differences
- bars, total number of bars/data points in the range
- QUANDL daily blocks
- Blockchain daily blocks
- QUANDL total blocks
- Blockchain total blocks
- total_error - difference between total_blocks estimated using both sources as of cur bar, blocks
- number_of_gaps - number of time gaps on a chart
- missing_bars - number of missing bars as result of time gaps on a chart
- Color coding:
- Blue - QUANDL data
- Red - Blockchain data
- Black - Is Different
- Aqua - number of differences
- Green - number of time gaps
- by default the indicator will show lots of vertical aqua lines, 138 differences, 928 bars, total error -370 blocks
- to compare the best match of the 2 sources shift Blockchain source 1 bar into the future by setting Blockchain Difficulty offset = 1, leave alpha = 0.01 =>
this results in no vertical aqua lines, 0 differences, total_error = 0 blocks
if you move the mouse inside the range some bars will show total_error = 1 blocks => total_error <= 1 blocks
- now uncheck Round Difficulty Values flag => some filled aqua areas, 218 differences.
- now set alpha = 1 (use raw source values) instead of 0.01 => lots of filled aqua areas, 871 differences.
although there are many differences this still doesn't affect the total_blocks estimate provided Difficulty offset = 1
Methodology:
To estimate Bitcoin Block Height we need 3 steps, each step has its own version:
- Step 1: Compare QUANDL Difficulty vs Blockchain Difficulty sources and estimate error based on differences
- Step 2: Compare QUANDL Hash Rate vs Blockchain Hash Rate sources and estimate error based on differences
- Step 3: Estimate Bitcoin Block Height (Total Blocks) using different methods in the most accurate way possible
QUANDL doesn't provide block time data, but we can calculate it using the Hash Rate approximation formula:
estimated Hash rate/sec H = 2^32 * D / T, where D - Difficulty, T - block time, sec
1. block time (T) can be derived from the formula, since we already know Difficulty (D) and Hash Rate (H) from QUANDL
2. using block time (T) we can estimate daily blocks as daily time / block time
3. block height (total blocks) = cumulative sum of daily blocks of all bars on the chart (that's why having no gaps is important)
Notes:
- This code uses Pinescript v3 compatibility framework
- hash rate is in THash/s, although QUANDL falsely states in description GHash/s! THash = 1000 GHash
- you can't read files, can only embed/hardcode raw data in script
- both QUANDL and Blockchain sources have no gaps
- QUANDL and Blockchain series are different in the following ways:
- all QUANDL data is already shifted 1 bar into the future, i.e. prev day's value is shown as cur day's value => Blockchain data must be shifted 1 bar forward to match
- all QUANDL diff data > 1 bn (10^12) are truncated and have last 1-2 digits as zeros, unlike Blockchain data => must multiply both values by 0.01 and floor/round the results
- QUANDL sometimes rounds, other times truncates those 1-2 last zero digits to get the 3rd last digit => must use both floor/round
- you can only shift sequences forward into the future (right), not back into the past (left) using positive offset => only Blockchain source can be shifted
- since total_blocks is already a cumulative sum of all prev values on each bar, total_error must be simple delta, can't be also int(cum()) or incremental
- all Blockchain values and total_error are na outside the range - move you mouse cursor on the last bar/inside the range to see them
TLDR, ver 1.0 Conclusion:
QUANDL/Blockchain Difficulty source differences don't affect total blocks estimate, total error <= 1 block with avg 150 blocks/day is negligible
Both QUANDL/Blockchain Difficulty sources are equally valid and can be used in calculations. QUANDL is a relatively good stand in for Blockchain industry standard data.
Links:
QUANDL difficulty source: www.quandl.com
QUANDL hash rate source: www.quandl.com
Blockchain difficulty source (export data as csv): www.blockchain.com
CBG Keltner ChannelsHere's an updated version of the CBG Keltner Channel indicator.
1. Added a new option for painting bars and backgrounds.
- Option 5 will paint up bars if the midline moving average is moving up and price is above the control moving average. It will paint down bars if the opposite is true. If neither are true, it will paint a neutral color. The neutral color defaults to gray bars and no color for backgrounds.
2. I've also added a 3rd band.
The chart here shows the default settings except for the inner band which is turned off.
Close Trade at end of day script serves as an example how we can close trades at end of day.
can be session controlled or the time controlled.
the session should be adjusted to be earlier than the candle time.
Range identifier by angleThis study tries to highlight ranges
as they are defined by
TradingLatino TradingView user.
A range according to its definition would consist of
periods when the price does not move a lot.
It's aimed at BTCUSDT pair and 4h timeframe.
HOW IT WORKS
Price source is the middle between open and close values.
For each of the bars a '5' periods regression line is calculated
The regression line has an angle with the 0-axis line
which might be between 0 and 90.
0 angle would mean price is always the same
90 angle would mean price going to infinity
When '2' of those periods in a row
have their angles between a maximum angle
and a minimum angle then you have the range
SETTINGS
Price range bar periods : How many previous periods are used to calculate the bar regression line
Price range range periods : How many periods in a row should be used to say that there is a range
Price range maximum angle ( < 90.0 !!! ): Maximum angle for range detection
Price range minimum angle ( > -90.0 !!! ): Mininum angle for range detection
Angles should be set from -89.99 for the minimum angle to +89.99 for the maximum angle
WARNING
Despite the name indicator does not identify the entire range.
It identifies when you realise you are inside a range.
E.g. If a range length is 8 periods length this range indicator
will highlight only the last 6 periods of the range.
USAGE
If your strategy signals you an upwards trend
then you might use this range detector
to know when to exit your long position
Why?
Because most of the times
the price is found to be
in a range it goes down
FEEDBACK 1
I'm quite sure this range approach by comparing angles is a novel one.
I have checked Range Identifier script from TradingView LazyBear user
and it uses another approach.
I'm interested on different approaches on how to detect ranges.
FEEDBACK 2
I have only used this identifier in BTCUSDT 4h timeframe.
I'm interested to know if applying it more traditional
stocks the degrees need to be adjusted to something
more smaller.
PINE SCRIPT TRICK
This script simulates looping an string char by char
in pine script.
CREDITS
I have reused and adapted some code from
'TF Segmented Linear Regression' script
which it's from TradingView alexgrover user.
