Candlestick Pattern Criteria and Analysis Indicator█ OVERVIEW
Define, then locate the presence of a candle that fits a specific criteria. Run a basic calculation on what happens after such a candle occurs.
Here, I’m not giving you an edge, but I’m giving you a clear way to find one.
IMPORTANT NOTE: PLEASE READ:
THE INDICATOR WILL ALWAYS INITIALLY LOAD WITH A RUNTIME ERROR. WHEN INITIALLY LOADED THERE NO CRITERIA SELECTED.
If you do not select a criteria or run a search for a criteria that doesn’t exist, you will get a runtime error. If you want to force the chart to load anyway, enable the debug panel at the bottom of the settings menu.
Who this is for:
- People who want to engage in TradingView for tedious and challenging data analysis related to candlestick measurement and occurrence rate and signal bar relationships with subsequent bars. People who don’t know but want to figure out what a strong bullish bar or a strong bearish bar is.
Who this is not for:
- People who want to be told by an indicator what is good or bad or buy or sell. Also, not for people that don’t have any clear idea on what they think is a strong bullish bar or a strong bearish bar and aren’t willing to put in the work.
Recommendation: Use on the candle resolution that accurately reflects your typical holding period. If you typically hold a trade for 3 weeks, use 3W candles. If you hold a trade for 3 minutes, use 3m candles.
Tldr; Read the tool tips and everything above this line. Let me know any issues that arise or questions you have.
█ CONCEPTS
Many trading styles indicate that a certain candle construct implies a bearish or bullish future for price. That said, it is also common to add to that idea that the context matters. Of course, this is how you end up with all manner of candlestick patterns accounting for thousands of pages of literature. No matter the context though, we can distill a discretionary trader's decision to take a trade based on one very basic premise: “A trader decides to take a trade on the basis of the rightmost candle's construction and what he/she believes that candle construct implies about the future price.” This indicator vets that trader’s theory in the most basic way possible. It finds the instances of any candle construction and takes a look at what happens on the next bar. This current bar is our “Signal Bar.”
█ GUIDE
I said that we vet the theory in the most basic way possible. But, in truth, this indicator is very complex as a result of there being thousands of ways to define a ‘strong’ candle. And you get to define things on a very granular level with this indicator.
Features:
1. Candle Highlighting
When the user’s criteria is met, the candle is highlighted on the chart.
The following candle is highlighted based on whether it breaks out, breaks down, or is an inside bar.
2. User-Defined Criteria
Criteria that you define include:
Candle Type: Bull bars, Bear bars, or both
Candle Attributes
Average Size based on Standard Deviation or Average of all potential bars in price history
Search within a specific price range
Search within a specific time range
Clarify time range using defined sessions and with or without weekends
3. Strike Lines on Candle
Often you want to know how price reacts when it gets back to a certain candle. Also it might be true that candle types cluster in a price region. This can be identified visually by adding lines that extend right on candles that fit the criteria.
4. User-Defined Context
Labeled “Alternative Criteria,” this facet of the script allows the user to take the context provided from another indicator and import it into the indicator to use as a overriding criteria. To account for the fact that the external indicator must be imported as a float value, true (criteria of external indicator is met) must be imported as 1 and false (criteria of external indicator is not met) as 0. Basically a binary Boolean. This can be used to create context, such as in the case of a traditional fractal, or can be used to pair with other signals.
If you know how to code in Pinescript, you can save a copy and simply add your own code to the section indicated in the code and set your bull and bear variables accordingly and the code should compile just fine with no further editing needed.
Included with the script to maximize out-of-the-box functionality, there is preloaded as alternative criteria a code snippet. The criteria is met on the bull side when the current candle close breaks out above the prior candle high. The bear criteria is met when the close breaks below the prior candle. When Alternate Criteria is run by itself, this is the only criteria set and bars are highlighted when it is true. You can qualify these candles by adding additional attributes that you think would fit well.
Using Alternative Criteria, you are essentially setting a filter for the rest of the criteria.
5. Extensive Read Out in the Data Window (right side bar pop out window).
As you can see in the thumbnail, there is pasted a copy of the Data Window Dialogue. I am doubtful I can get the thumbnail to load up perfectly aligned. Its hard to get all these data points in here. It may be better suited for a table at this point. Let me know what you think.
The primary, but not exclusive, purpose of what is in the Data Window is to talk about how often your criteria happens and what happens on the next bar. There are a lot of pieces to this.
Red = Values pertaining to the size of the current bar only
Blue = Values pertaining or related to the total number of signals
Green = Values pertaining to the signal bars themselves, including their measurements
Purple = Values pertaining to bullish bars that happen after the signal bar
Fuchsia = Values pertaining to bearish bars that happen after the signal bar
Lime = Last four rows which are your percentage occurrence vs total signals percentages
The best way I can explain how to understand parts you don’t understand otherwise in the data window is search the title of the row in the code using ‘ctrl+f’ and look at it and see if it makes more sense.
█ [b}Available Candle Attributes
Candle attributes can be used in any combination. They include:
[*}Bodies
[*}High/Low Range
[*}Upper Wick
[*}Lower Wick
[*}Average Size
[*}Alternative Criteria
Criteria will evaluate each attribute independently. If none is set for a particular attribute it is bypassed.
Criteria Quantity can be in Ticks, Points, or Percentage. For percentage keep in mind if using anything involving the candle range will not work well with percentage.
Criteria Operators are “Greater Than,” “Less Than,” and “Threshold.” Threshold means within a range of two numbers.
█ Problems with this methodology and opportunities for future development:
#1 This kind of work is hard.
If you know what you’re doing you might be able to find success changing out the inputs for loops and logging results in arrays or matrices, but to manually go through and test various criteria is a lot of work. However, it is rewarding. At the time of publication in early Oct 2022, you will quickly find that you get MUCH more follow through on bear bars than bull bars. That should be obvious because we’re in the middle of a bear market, but you can still work with the parameters and contextual inputs to determine what maximizes your probability. I’ve found configurations that yield 70% probability across the full series of bars. That’s an edge. That means that 70% of the time, when this criteria is met, the next bar puts you in profit.
#2 The script is VERY heavy.
Takes an eternity to load. But, give it a break, it’s doing a heck of a lot! There is 10 unique arrays in here and a loop that is a bit heavy but gives us the debug window.
#3 If you don’t have a clear idea its hard to know where to start.
There are a lot of levers to pull on in this script. Knowing which ones are useful and meaningful is very challenging. Combine that with long load times… its not great.
#4 Your brain is the only thing that can optimize your results because the criteria come from your mind.
Machine learning would be much more useful here, but for now, you are the machine. Learn.
#5 You can’t save your settings.
So, when you find a good combo, you’ll have to write it down elsewhere for future reference. It would be nice if we could save templates on custom indicators like we can on some of the built in drawing tools, but I’ve had no success in that. So, I recommend screenshotting your settings and saving them in Notion.so or some other solid record keeping database. Then you can go back and retrieve those settings.
#6 no way to export these results into conditions that can be copy/pasted into another script.
Copy/Paste of labels or tables would be the best feature ever at this point. Because you could take the criteria and put it in a label, copy it and drop it into another strategy script or something. But… men can dream.
█ Opportunities to PineCoders Learn:
1. In this script I’m importing libraries, showing some of my libraries functionality. Hopefully that gives you some ideas on how to use them too.
The price displacement library (which I love!)
Creative and conventional ways of using debug()
how to display arrays and matrices on charts
I didn’t call in the library that holds the backtesting function. But, also demonstrating, you can always pull the library up and just copy/paste the function out of there and into your script. That’s fine to do a lot of the time.
2. I am using REALLY complicated logic in this script (at least for me). I included extensive descriptions of this ? : logic in the text of the script. I also did my best to bracket () my logic groups to demonstrate how they fit together, both for you and my future self.
3. The breakout, built-in, “alternative criteria” is actually a small bit of genius built in there if you want to take the time to understand that block of code and think about some of the larger implications of the method deployed.
As always, a big thank you to TradingView and the Pinescript community, the Pinescript pros who have mentored me, and all of you who I am privileged to help in their Pinescripting journey.
"Those who stay will become champions" - Bo Schembechler
Forecasting
SMA 10/20/50 by Bull Bear Investing BabyThis script basically is a combination of 3 different simple moving averages line to determine the trend of the assets
The colour indicating which moving averages are as per following:
1) Green- 10MA
2) Red- 20MA
3) Blue- 50MA
When the moving averages are aligned as per following, the trend is indicating towards an uptrend:
---> 10ma > 20ma > 50ma
Likewise when the moving averages are aligned as per following, the trend is indicating towards a downtrend:
---> 10ma < 20ma < 50ma
Calculate target by Range [Wyckoff,PnF]First of all, I would like to thank the author @LonesomeTheBlue.
This indicator developed on the source code "Point and Figure (PnF)" by author @LonesomeTheBlue.
This indicator calculate the range (Cause) of Phase accumulation or distribution to calculate the taget (Effect) based on the Wyckoff Method.
Formula for calculate move value target : Col * BoxSize * Reversal
Col -> Number of Column (PnF) in the range (Cause)
BoxSize -> Value in one Box (PnF)
Reversal -> Reversal (PnF)
Fourier Spectrometer of Price w/ Extrapolation Forecast [Loxx]Fourier Spectrometer of Price w/ Extrapolation Forecast is a forecasting indicator that forecasts the sinusoidal frequency of input price. This method uses Linear Regression with a Fast Fourier Transform function for the forecast and is different from previous forecasting methods I've posted. Dotted lines are the forecast frequencies. You can change the UI colors and line widths. This comes with 8 frequencies out of the box. Instead of drawing sinusoidal manually on your charts, you can use this instead. This will render better results than eyeballing the Sine Wave that folks use for trading. this is the real math that automates that process.
Each signal line can be shown as a linear superposition of periodic (sinusoidal) components with different periods (frequencies) and amplitudes. Roughly, the indicator shows those components. It strongly depends on the probing window and changes (recalculates) after each tick; e.g., you can see the set of frequencies showing whether the signal is fast or slow-changing, etc. Sometimes only a small number of leading / strongest components (e.g., 3) can extrapolate the signal quite well.
Related Indicators
Fourier Extrapolator of 'Caterpillar' SSA of Price
Real-Fast Fourier Transform of Price w/ Linear Regression
Fourier Extrapolator of Price w/ Projection Forecast
Itakura-Saito Autoregressive Extrapolation of Price
Helme-Nikias Weighted Burg AR-SE Extra. of Price
***The period parameter doesn't correspond to how many bars back the drawing begins. Lines re rendered according to skipping mechanism due to TradingView limitations.
Price Bubble Meter (Moving Average to Price Distance)This indicator measures Price Distance (in %) from any given Moving Average.
It will help you see if the price is over extended or in the fair price zone.
Trend Analysis
How much % higher is the current price compared to 200W SMA
What % has been the maximum price rise from 200W SMA
What % has been the lowest price fall from 200W SMA
DCA Opportunity Finder
How much % higher is the current price compared to 2 year SMA
What % has been the maximum price rise from 2 year SMA
What % has been the lowest price fall from 2 year SMA
Yes you can manually measure it all using a ruler, but aint no one got time for that foo.
Fib Percentage Previous Day CloseIntraday regulated markets move within their circuit range above or below which the market activity is halted.
These levels are protected by the MM to accumulate or distribute. These levels are mostly same for all markets i.e. 2%, 4% , 5%, 10% and 20% of previous day close, crossing which the market activity halts.
So, from here the expectation of turning or breaking increases.
This indicator automatically plots the levels and helps understanding the price behavior at these points. This in turn helps taking better RR trades.
Killzone MTA ConceptsThis indicator indicates the Pre-Forex Market Killzones studied by our mentors at MTA Concepts. High volatility areas where you can take advantage of a great advantage when trading intraday.
Killzone: A killzone is an area, a time interval where there is high volatility and coincides with market pre-openings.
We have divided the Killzones into 3:
-London Killzone
-New York Killzone
-Asia Killzone
- Closing of operations: Time interval to take into account for the closing of intraday operations.
This indicator is prepared for intraday traders
Fourier Extrapolator of 'Caterpillar' SSA of Price [Loxx]Fourier Extrapolator of 'Caterpillar' SSA of Price is a forecasting indicator that applies Singular Spectrum Analysis to input price and then injects that transformed value into the Quinn-Fernandes Fourier Transform algorithm to generate a price forecast. The indicator plots two curves: the green/red curve indicates modeled past values and the yellow/fuchsia dotted curve indicates the future extrapolated values.
What is the Fourier Transform Extrapolator of price?
Fourier Extrapolator of Price is a multi-harmonic (or multi-tone) trigonometric model of a price series xi, i=1..n, is given by:
xi = m + Sum( a*Cos(w*i) + b*Sin(w*i), h=1..H )
Where:
xi - past price at i-th bar, total n past prices;
m - bias;
a and b - scaling coefficients of harmonics;
w - frequency of a harmonic ;
h - harmonic number;
H - total number of fitted harmonics.
Fitting this model means finding m, a, b, and w that make the modeled values to be close to real values. Finding the harmonic frequencies w is the most difficult part of fitting a trigonometric model. In the case of a Fourier series, these frequencies are set at 2*pi*h/n. But, the Fourier series extrapolation means simply repeating the n past prices into the future.
Quinn-Fernandes algorithm find sthe harmonic frequencies. It fits harmonics of the trigonometric series one by one until the specified total number of harmonics H is reached. After fitting a new harmonic , the coded algorithm computes the residue between the updated model and the real values and fits a new harmonic to the residue.
see here: A Fast Efficient Technique for the Estimation of Frequency , B. G. Quinn and J. M. Fernandes, Biometrika, Vol. 78, No. 3 (Sep., 1991), pp . 489-497 (9 pages) Published By: Oxford University Press
Fourier Transform Extrapolator of Price inputs are as follows:
npast - number of past bars, to which trigonometric series is fitted;
nharm - total number of harmonics in model;
frqtol - tolerance of frequency calculations.
What is Singular Spectrum Analysis ( SSA )?
Singular spectrum analysis ( SSA ) is a technique of time series analysis and forecasting. It combines elements of classical time series analysis, multivariate statistics, multivariate geometry, dynamical systems and signal processing. SSA aims at decomposing the original series into a sum of a small number of interpretable components such as a slowly varying trend, oscillatory components and a ‘structureless’ noise. It is based on the singular value decomposition ( SVD ) of a specific matrix constructed upon the time series. Neither a parametric model nor stationarity-type conditions have to be assumed for the time series. This makes SSA a model-free method and hence enables SSA to have a very wide range of applicability.
For our purposes here, we are only concerned with the "Caterpillar" SSA . This methodology was developed in the former Soviet Union independently (the ‘iron curtain effect’) of the mainstream SSA . The main difference between the main-stream SSA and the "Caterpillar" SSA is not in the algorithmic details but rather in the assumptions and in the emphasis in the study of SSA properties. To apply the mainstream SSA , one often needs to assume some kind of stationarity of the time series and think in terms of the "signal plus noise" model (where the noise is often assumed to be ‘red’). In the "Caterpillar" SSA , the main methodological stress is on separability (of one component of the series from another one) and neither the assumption of stationarity nor the model in the form "signal plus noise" are required.
"Caterpillar" SSA
The basic "Caterpillar" SSA algorithm for analyzing one-dimensional time series consists of:
Transformation of the one-dimensional time series to the trajectory matrix by means of a delay procedure (this gives the name to the whole technique);
Singular Value Decomposition of the trajectory matrix;
Reconstruction of the original time series based on a number of selected eigenvectors.
This decomposition initializes forecasting procedures for both the original time series and its components. The method can be naturally extended to multidimensional time series and to image processing.
The method is a powerful and useful tool of time series analysis in meteorology, hydrology, geophysics, climatology and, according to our experience, in economics, biology, physics, medicine and other sciences; that is, where short and long, one-dimensional and multidimensional, stationary and non-stationary, almost deterministic and noisy time series are to be analyzed.
"Caterpillar" SSA inputs are as follows:
lag - How much lag to introduce into the SSA algorithm, the higher this number the slower the process and smoother the signal
ncomp - Number of Computations or cycles of of the SSA algorithm; the higher the slower
ssapernorm - SSA Period Normalization
numbars =- number of past bars, to which SSA is fitted
Included:
Bar coloring
Alerts
Signals
Loxx's Expanded Source Types
Related Fourier Transform Indicators
Real-Fast Fourier Transform of Price w/ Linear Regression
Fourier Extrapolator of Variety RSI w/ Bollinger Bands
Fourier Extrapolator of Price w/ Projection Forecast
Related Projection Forecast Indicators
Itakura-Saito Autoregressive Extrapolation of Price
Helme-Nikias Weighted Burg AR-SE Extra. of Price
Related SSA Indicators
End-pointed SSA of FDASMA
End-pointed SSA of Williams %R
Machine Learning: kNN (New Approach)Description:
kNN is a very robust and simple method for data classification and prediction. It is very effective if the training data is large. However, it is distinguished by difficulty at determining its main parameter, K (a number of nearest neighbors), beforehand. The computation cost is also quite high because we need to compute distance of each instance to all training samples. Nevertheless, in algorithmic trading KNN is reported to perform on a par with such techniques as SVM and Random Forest. It is also widely used in the area of data science.
The input data is just a long series of prices over time without any particular features. The value to be predicted is just the next bar's price. The way that this problem is solved for both nearest neighbor techniques and for some other types of prediction algorithms is to create training records by taking, for instance, 10 consecutive prices and using the first 9 as predictor values and the 10th as the prediction value. Doing this way, given 100 data points in your time series you could create 10 different training records. It's possible to create even more training records than 10 by creating a new record starting at every data point. For instance, you could take the first 10 data points and create a record. Then you could take the 10 consecutive data points starting at the second data point, the 10 consecutive data points starting at the third data point, etc.
By default, shown are only 10 initial data points as predictor values and the 6th as the prediction value.
Here is a step-by-step workthrough on how to compute K nearest neighbors (KNN) algorithm for quantitative data:
1. Determine parameter K = number of nearest neighbors.
2. Calculate the distance between the instance and all the training samples. As we are dealing with one-dimensional distance, we simply take absolute value from the instance to value of x (| x – v |).
3. Rank the distance and determine nearest neighbors based on the K'th minimum distance.
4. Gather the values of the nearest neighbors.
5. Use average of nearest neighbors as the prediction value of the instance.
The original logic of the algorithm was slightly modified, and as a result at approx. N=17 the resulting curve nicely approximates that of the sma(20). See the description below. Beside the sma-like MA this algorithm also gives you a hint on the direction of the next bar move.
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.
FOMC & CPI DatesThis indicator plots vertical lines at the scheduled times of US Federal Reserve's FOMC Meeting Dates.
Data is based on U.S. Federal Open Market Committee (FOMC) Meeting Minutes
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).
Adaptive Rebound Line (ARL)The Adaptive Rebound Line (ARL) focuses on the rebound of price action according to the trend.
While it does not focus on showing the trend, it does help in anticipating price rebounds.
It achieves this by adapting quickly and by reducing lag.
It is recommended to use this with a trend-identifying indicator.
It was inspired by the Hull Moving Average and the KAMA.
Additional indicator show in the chart is Tide Finder Plus .
Itakura-Saito Autoregressive Extrapolation of Price [Loxx]The Itakura–Saito distance is a Bregman divergence generated by minus the logarithmic function, but is not a true metric since it is not symmetric and it does not fulfil triangle inequality.
In Non-negative matrix factorization, the Itakura-Saito divergence can be used as a measure of the quality of the factorization: this implies a meaningful statistical model of the components and can be solved through an iterative method.
The Itakura-Saito distance is the Bregman divergence associated with the Gamma exponential family where the information divergence of one distribution in the family from another element in the family is given by the Itakura-Saito divergence of the mean value of the first distribution from the mean value of the second distribution.
Fed LiquidityFed liquidity model based on #MaxJAnderson's work. Incorporates the Treasury General Account, Reverse Repo and Fed balance sheet to determine how much "net liquidity" is available to markets. Very much a beta version.
EmirindicatorLook at the data while at the level you entered. The line below where you entered should be your Stop Loss level. The first line above it represents that you need to bring your Stop Loss level to your entry level and take some profit if you want. The top line is the sales level recommended by the program.
HPK Crash IndicatorFrom Hari P. Krishnan's book, The Second Leg Down: Strategies for Profiting after a Market Sell-Off :
"We start by specifying the year on year (YoY) change in the index. Next, we calculate the 5 year trailing Z score of the YoY returns. We also calculate the 5 year trailing Z score of 1 month historical volatility for the index, using daily returns. Our crisis warning indicator flashes if both Z scores are above 2. In other words, recent price increases and current volatility need to be at least 2 standard deviations above normal.
It can be seen that this basic implementation is reasonably effective, accepting that the effective sample set is small. A false signal is given in mid-2006, but the signal is quickly washed away. The remaining signals occur fairly close to the point of collapse. The idea that elevated volatility is predictive of danger is not new and underpins many asset allocation schemes. However, Sornette deserves credit for moving away from a largely valuation-based approach to predicting crises to one that relies upon price action itself."
Faytterro EstimatorWhat is Faytterro Estimator?
This indicator is an advanced moving average.
What it does?
This indicator is both a moving average and at the same time, it predicts the future values that the price may take based on the values it has taken before.
How it does it?
takes the weighted average of data of the selected length (reducing the weight from the middle to the ends). then draws a parabola through the last three values, creating a predicted line.
How to use it?
it is simple to use. You can use it both as a regression to review past prices, and to predict the future value of a price. uptrends are in green and downtrends are in red. color change indicates a possible trend change.
HTC_Bollinger_Band_Strategy_By_CorbachoEste indicador te da la visión del mercado y sus posibles rebotes con unas bandas de bollinger a 3 dispersiones tipicas. Añadiendo al grafico la SMA200 podemos ver si operamos a favor o en contra de la tendencia
Chervolinos-Wave-PM-ForecastThe Wave PM (Whistler Active Volatility Energy – Price Mass) indicator is an oscillator described in Mark Whistler's book, Volatility Illuminated.
The Wave PM is specifically designed to help read volatility cycles. When we visualize volatility cycles as a chart, we can get a clear view of the market volatility phases in multiple time frames. This indicator forms an arithmetic mean over 30 observed periods. Traders can thus get a better insight into "potential" volatility from up to pent-up energy, the different zones give strong help to predict future price developments.
Possible interpretation patterns:
You are at the end of a long uptrend and you want to know if the price is going to go down, if the indicator shows red and the value is above 25, it is likely to do so.
You're in a downtrend and there's a bit of a recovery phase, so you might be wondering if it's going to continue when the indicator shows green. It would go further with yellow, but with green it can be assumed that it is going down rapidly.
Special thanks to sourcey who programmed the 3D Wave-PM.
This variant of sourcey looks very nice, but was too confusing for me. In order to get a strong overview, forming an arithmetic mean is very useful.
I hope you and the Mods like my version
Best regards, Chervolino
Pivot Parallel Channel by [livetrend]This script draws parallel channels using pivot points for trend analysis.
Script draws maximum 4 parallel channels if suitable up or down trend already exists on the chart according to chosen Pivot Length and Multiplier.
You can change Multiplier to draw Higher Time Frame Channels.
Good luck!
Blockchain Fundamentals: 200 Week MA Heatmap [CR]Blockchain Fundamentals: 200 Week MA Heatmap
This is released as a thank you to all my followers who pushed me over the 600 follower mark on twitter. Thanks to all you Kingz and Queenz out there who made it happen. <3
Indicator Overview
In each of its major market cycles, Bitcoin's price historically bottoms out around the 200 week moving average.
This indicator uses a color heatmap based on the % increases of that 200 week moving average. Depending on the rolling cumulative 4 week percent delta of the 200 week moving average, a color is assigned to the price chart. This method clearly highlights the market cycles of bitcoin and can be extremely helpful to use in your forecasts.
How It Can Be Used
The long term Bitcoin investor can monitor the monthly color changes. Historically, when we see orange and red dots assigned to the price chart, this has been a good time to sell Bitcoin as the market overheats. Periods where the price dots are purple and close to the 200 week MA have historically been good times to buy.
Bitcoin Price Prediction Using This Tool
If you are looking to predict the price of Bitcoin or forecast where it may go in the future, the 200WMA heatmap can be a useful tool as it shows on a historical basis whether the current price is overextending (red dots) and may need to cool down. It can also show when Bitcoin price may be good value on a historical basis. This can be when the dots on the chart are purple or blue.
Over more than ten years, $BTC has spent very little time below the 200 week moving average which is also worth noting when thinking about price predictions for Bitcoin or a Bitcoin price forecast.
Notes
1.) If you do not want to view the legend do the following: Indicator options > Style tab > Uncheck "Tables"
2.) I use my custom function to get around the limited historical data for bitcoin. You can check out the explanation of it here:
Gann Square of 144This indicator will create lines on the chart based on W.D. Gann's Square of 144. All the inputs will be detailed below
Why create this indicator?
I didn't find it on Tradingview (at least with open source). But the main reason is to study the strategy and be able to draw it fast. Manually drawing the square is not hard, but moving all together to the right spots and scale was time-consuming.
It has a lot of inputs...
Yes, each square point divisible by 6 has information with some options, so the user can create any configuration he wants. Also, it has the advantage of having the square built in seconds and adjusting itself on each new calculation.
About the inputs
Starting Date
This input will be used when the "Set Upper/Lower Prices and Start Bar Automatically" checkbox is not selected. The indicator will calculate all the line locations on the chart using the selected start date. When selecting this input, change the Manual Max and Min Prices to the better calculation
Manual Max/Min Price
This input will be used when the "Set Upper/Lower Prices and Start Bar Automatically" checkbox is not selected. The indicator will calculate all the line's locations on the chart using these prices
Set Upper/Lower Prices and Start Bar Automatically
Selects if the starting date will be automatically selected by the system or based on the input data. When it's set, the indicator will use the most recent bar as the middle point of the square, using the higher price as the Upper Price and the lowest price as the Lower Price in the latest 72 bars (or more based on the Candles Per Division parameter)
Update at a new bar
When this option is market, the indicator will update all created lines to match the new bar position, together with all the possible new Upper/Lower prices. Let it unchecked to watch the progression of the price while the square remains fixed in the chart.
Top X-Axis
When checked, it will display the labels on the Top of the square
Bottom X-Axis
When checked, it will display the labels on the Bottom of the square
Left X-Axis
When checked, it will display the labels on the left of the square
Right X-Axis
When checked, it will display the labels on the right of the square
Show Prices on the Right Y-Axis
When checked, it will display the prices together with the labels on the right of the square
Show Vertical Divisions
Show the lines that will divide the square into 9 equal parts
Show Extra Lines
Show unique lines that will come from the Top and bottom middle of the square, connecting the center to the 36 and 108 levels
Show Grid
When selected, it will display a grid in the square
Line Patterns
A selector with some options of built-in lines configuration. When any option besides None is selected, it will override the lines inputs below
Numbers Color
Select the color of each number on the Axis
Vertical Lines Color
Select the color of the vertical lines
Grid Color
Select the grid line color
Connections from corners to N
Each corner is represented by 2 characters, so they all fit in a single line
It will indicate where the line starts and where it ends
┏ ↓ = Top Left to Bottom
┏ → = Top Left to Right
┗ ↑ = Bottom Left to Top
┗ → = Bottom Left to Right
┓ ← = Top Right to Left
┓ ↓ = Top Right to Bottom
┛ ← = Bottom Right to Left
┛ ↑ = Bottom Right to Top
Besides selecting what line will be created, it's possible to select the color, the style, and the extension
How to use this indicator
When you dig into Gann's books for more information about the square of 144, you find that it was part of his setup with multiple indicators (technical and fundamental, and astrological). It is not a "one indicator" setup, so it's hard to say that you will find entries, exits, stop loss, and take profit in this. Still, it will help see trendiness, support, and resistance levels.
Mixing this with other indicators is probably a good idea, but some may find this indicator the only one needed.
Some aspects of the square
The end of the square is important, so where it starts is crucial. The end is important because it is where the price and time expire. The other parts of the square are defined based on their start and end, so placing them right is essential.
So, where to set the start of the square?
The last major low is the most indicated. The minimum price will be the lowest, and the max price will be the last major Top. Note that the indicator uses 1 candle on each point.
After finding the start, the minimum, and the maximum prices for the square, it will draw all lines. Another essential part of the square is The Midpoint.
The midpoint is the most crucial part of the square and is the best way to see if you positioned the square correctly. When the price is inside the square, using the starting candle as the start, a second higher low or a lower high occurs in that spot. When using the Vertical lines in the indicator, it's the middle square inside Gann's square.
The other divisions will be opposing each other most of the time. So if the price is rising in the 1/3 of the square, it's common to see the price fall in the 3/3 of the square.
More information about these aspects here
Considerations
This indicator was meant for price targets and a time calculator for possible support/resistances in the chart. It was created by William Delbert Gann and was part of his setup for trading almost a century ago. The lines will form geometric figures, which Gann used with high accuracy to predict tops/bottoms and when they would occur.
