Tick StrategyTick Strategy:
Questions many pine coders/traders have is, How to enter/exit trade as soon as trade condition is met i.e. do not wait till candle completion to enter/exit the trade. This strategy will help you to understand one of the way to achieve it.
This is an educational strategy to demonstrate, how one can trade based on tick data. This being a strategy based on tick data, it can be tested only on real time candles. This strategy will not take any trades on historical candles and cannot be used for back testing. All the strategy trades taken on real time candles will disappear (repainting) once chart is refreshed and new trades will be entered on real time candles.
The strategy will do nothing during off market hours and will not take any trades.
The strategy has been designed based on rules/inputs below:
1. Count the ticks from start of a candle till end of candle
2. Bifurcate ticks as up-ticks and down-ticks. If tick price is above previous tick price the tick is considered as up-tick and vice versa
3. Count the successive up-ticks and successive down-ticks
Strategy rules:
1. Track candle type (green or red) continuously on each tick (green candle is when latest tick price > previous tick price)
2. Take a long trade if work in progress (WIP) candle is green candle and we get successive up-ticks equal to user input ticks for trade
3. Take a short trade if work in progress (WIP) candle is red candle and we get successive down-ticks equal to user input ticks for trade
4. Exit the trade when we get successive ticks equal to user input ticks in opposite direction
5. Optionally for trade entry, user can decide whether to calculate successive up-ticks/down-ticks from beginning of candle or successive up-ticks/down-ticks anytime during the candle formation
6. Optionally for trade exit/square off, user can decide whether to apply exit rules on the entry candle or only from subsequent candle
Strategy setting:
1. '' – This is just to describe when trades are entered. This parameter is not used for any calculation
2. 'No of successive ticks to enter the trade' – User input to decide, number of successive ticks for trade entry
3. 'Count successive ticks for trade only from start of candle' – check this to count successive ticks only from beginning of a candle
4. 'Exit if succussive ticks in opposite direction' - User input to decide, number of successive ticks in opposite direction for exiting the trade
5. 'Apply exit criteria on entry candle' – check to allow exit of trade on the entry candle, if un-checked, trade will not be exited on the entry candle i.e. opposite direction ticks will be counted from subsequent candle
Information below will be displayed continuously on the chart:
1. Candle no – Candles are counted from start of the trading session. This is current candle being formed on the chart
2. Candle now – This shows either ‘Green’ or ‘Red’ based on type of candle being formed
3. Tick count – This is current tick number being processed. Tick number starts from 1 for each new candle
4. Up-tick count – Number of up-ticks during formation of current candle
5. Down-tick count – Number of down-ticks during formation of current candle
6. Successive up-ticks – Current successive up-tick count
7. Successive down-ticks – Current successive down-tick count
8. Up-tick volume – Volume associated with up-ticks
9. Down-tick volume – Volume associated with down-ticks
10. Up-tick volume % - This is % of volume associated with up-ticks
11. Total volume – Candle volume till now. (Some times you might observe small difference between total volume and the volume shown by volume indicator. The difference could be because of refresh rate of your screen)
12. Candle completion % - This shows current candles completion %. This is candle progress from start of candle till close of candle
Educational
Tick StatisticsTick Statistics:
I have seen many questions/queries related to tick data in TV telegram channels. This script will help pine scripts to understand how ticks work, how to capture and process tick data.
This is an educational indicator script for pine scripters.
The indicator shall work only on real time candles. Tick data capture is initiated as soon as indicator is loaded on the chart. You might not get correct statistics on 1st candle in case indicator is loaded when real time candle is in progress, in such case you can monitor the statistics generated for subsequent candles.
Generated statistics is shown on the chart by placing 2 diamond shapes above and below the candle.
Diamond shape below the candle will have candles ‘tick data’ listed in a table. This can be view by placing mouse pointer on the diamond shape. Refer to point 1 below for more details.
Diamond shape above the candle will have statistics as mentioned in point no 2 onwards. To view the statistics place the mouse point on the diamond shape. The shape will appear in green color when both tick price and tick volume are both moving in the same direction. The diamond shape in red color means tick price and tick volume are moving in opposite direction.
The script captures tick by tick data and generate statistics below:
1. List of tick data with details below: (this is stored in the diamond shape placed below the candle)
a. Tick no
b. Tick type – Up tick (Up), Down tick (Dn), No change (--)
c. Tick price
d. Volume
e. Price difference (as compared to previous tick price)
f. Volume difference (as compared to previous tick volume)
2. Tick statistics
a. Total ticks
b. Number of up ticks
c. Number of down ticks
d. Number of No change ticks
3. Volume Statistics
a. Total volume
b. Up tick volume
c. Down tick volume
d. Volume associated with ticks where there is no change
e. Candle volume (just for reconciliation purpose)
4. Max-min statistics
a. Max volume = <> at price = <> at tick no = <>
b. Min volume = <> at price = <> at tick no = <>
c. Max price = <> at volume = <> at tick no = <>
d. Min price = <> at volume = <> at tick no = <>
5. Candle summary
a. Price << Up >> (if price is up as compared to 1st tick <> otherwise
b. Volume <> (if up tick volume is more than down tick volume <> otherwise
GT 5.1 Strategy═════════════════════════════════════════════════════════════════════════
█ OVERVIEW
People often look an indicator in their technical analysis to enter a position. We may also need to look at the signals of one or more indicators to verify the signals given by some indicators. In this context, I developed a strategy to test whether it really works by choosing some of the indicators that capture trend changes with the same characteristics. Also, since the subject is to catch the trend change, I thought it would be right to include an indicator using the heikin ashi logic. By averaging and smoothing the market noise, Heiken Ashi makes it easier to detect the direction of the trend helps to see possible reversal points on the chart. However, it should be noted that Heiken Ashi is a lagging indicator.
I picked 5 different indicators (but their purpose are similar) and combined them to produce buy and sell signals based on your choice(not repaint). First of all let's get some information about our indicators. So you will understand me why i picked these indicators and what is the meaning of their signals.
1 — Coral Trend Indicator by LazyBear
Coral Trend Indicator is a linear combination of moving averages, all obtained by a triple or higher order exponential smoothing. The indicator comes with a trend indication which is based on the normalized slope of the plot. the usage of this indicator is simple. When the color of the line is green that means the market is in uptrend. But when the color is red that means the market is in downtrend.
As you see the original indicator it is simple to find is it in uptrend or downtrend.
So i added a code to find when the color of the line change. When it turns green to red my script giving sell signals, when it turns red to green it gives buy signals.
I hide the candles to show you more clearly what is happening when you choose only Coral Strategy. But sometimes it is not enough only using itself. Even if green dots turn to red it continues in uptrend. So we need a to look another indicator to approve our signal.
2 — SSL channel by ErwinBeckers
Known as the SSL , the Semaphore Signal Level channel is an indicator that combines moving averages to provide you with a clear visual signal of price movement dynamics. In short, it's designed to show you when a price trend is forming. This indicator creates a band by calculating the high and low values according to the determined period. Simply if you decide 10 as period, it calculates a 10-period moving average on the latest 10 highs. Calculate a 10-period moving average on the latest 10 lows. If the price falls below the low band, the downtrend begins, if the price closes above the high band, the uptrend begins. Lets look the original form of indicator and learn how it using.
If the red line is below and the green band is above, it means that we are in uptrend, and if it is on the opposite side, it means that we are in downtrend. Therefore, it would be logical to enter a position where the trend has changed. So i added a code to find when the crossover has occured.
As you see in my strategy, it gives you signals when the trend has changed. But sometimes it is not enough only using this indicator itself. So lets look 2 indicator together in one chart.
Look circle SSL is saying it is in downtrend but Coral is saying it has entered in uptrend. if we just look to coral signal it can misleads us. So it can be better to look another indicator for validating our signals.
3 — Heikin Ashi RSI Oscillator by JayRogers
The Heikin-Ashi technique is used by technical traders to identify a given trend more easily. Heikin-Ashi has a smoother look because it is essentially taking an average of the movement. There is a tendency with Heikin-Ashi for the candles to stay red during a downtrend and green during an uptrend, whereas normal candlesticks alternate color even if the price is moving dominantly in one direction. This indicator actually recalculates the RSI indicator with the logic of heikin ashi. Due to smoothing, the bars are formed with a slight lag, reflecting the trend rather than the exact price movement. So lets look the original version to understand more clearly. If red bars turn to green bars it means uptrend may begin, if green bars turn to red it means downtrend may begin.
As you see HARSI giving lots of signal some of them is really good but some of them are not very well. Because it gives so much signals Now i will change time period and lets look same chart again.
Now results are better because of heikin ashi's logic. it is not suitable for day traders, it gives more accurate result when using the time period is longer. But it can be useful to use this indicator in short time periods using with other indicators. So you may catch the trend changes more accurately.
4 — MACD DEMA by ToFFF
This indicator uses a double EMA and MACD algorithm to analyze the direction of the trend. Though it might seem a tough task to manage the trades with the help of MACD DEMA once you know how the proper way to interpret the signal lines, it will be an easy task.
This indicator also smoothens the signal lines with the time series algorithm which eventually makes the higher time frame important. So, expecting better results in the lower time frame can result in big losses as the data reading from the MACD DEMA will not be accurate. In order to understand the function of this indicator, you have to know the functions of the EMA also.
The exponential moving average tends to give more priority to the recent price changes. So, expecting better results when the volatility is very high is a very risky approach to trade the market. Moreover, the MACD has some lagging issues compared to the EMA, so it is super important to use a trading method that focuses on the higher time frame only. What does MACD 12 26 Close 9 mean? When the DEMA-9 crosses above the MACD(12,26), this is considered a bearish signal. It means the trend in the stock – its magnitude and/or momentum – is starting to shift course. When the MACD(12,26) crosses above the DEMA-9, this is considered a bullish signal. Lets see this indicator on Chart.
When the blue line crossover red line it is good time to buy. As you see from the chart i put arrows where the crossover are appeared.
When the red line crossover blue line it is good time to sell or exit from position.
5 — WaveTrend Oscillator by LazyBear
This is a technical indicator that creates high and low bands between two values. It then creates a trend indicator that draws waves with highs and lows within these boundaries. WaveTrend is a widely used indicator for finding direction of an asset.
Calculation period: number of candles used to calculate WaveTrend, defaults to 10. Averaging period: number of candles used to average WaveTrend, defaults to 21.
As you see in chart when the lines crossover occured my strategy gives buy or sell signals.
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█ HOW TO USE
I hope you understand how the indicators I mentioned above work and what they are used for. Now, I will explain in detail how to use the strategy I have created.
When you enter the settings section, you will see 5 types of indicators. If you want to use the signals of the indicators, simply tick the box next to the indicators. Also, under each option there is an area where you can set the "lookback". This setting is a field that will make the signals overlap when you select more than one option. If you are going to trade with only one option, you should make sure that this field is 0. Otherwise, it may continue to generate as many signals as you choose.
Lets see in chart for easy understanding.
As you see chart, if i chose only HARSI with lookback 0 (HARSI and CORAL should be 1 minumum because of algorithm-we looking 1 bar before, others 0 because we are looking crossovers), it will give signals only when harsı bar's color changed. But when i changed Lookback as 7 it will be like this in chart.
Now i will choose 2 indicator with settings of their lookback 0.
As you see it will give signals when both of them occurs same time. But HARSI is an indicator giving very early signal so we can enter position 5-6 bars after the first bar color change. So i will change HARSI Lookback settings as 7. Lets look what happens when we use lookback option.
So it wil be useful to change lookback settings to find best signals in each time period and in each symbol. But it shouldnt be too high. Because you can be late to catch trend's starting.
this is an image of MACD and WAVE trend used and lookback option are both 6.
Now lets see an example with 3 options are chosen with lookback option 11-1-5
Now lets talk about indicators settings. After strategy options you will see each indicators settings, you can change their settings as you desired. So each indicators signal will be changed according to your adjustment.
I left strategy options with default settings. You can change it manually as if you want.
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█ LIMITATIONS: Don't rely on non-standard charts results. For example Heikin Ashi is a technical analysis method used with the traditional candlestick chart.Heikin Ashi vs. Candlestick Chart: The decisive visual difference between Heikin Ashi and the traditional chart is that Heikin Ashi flattens the traditional candlestick chart using a modified formula.
The primary advantage of Heikin Ashi is that it makes the chart more reader-friendly and helps users identify and analyze trends .
Because Heikin Ashi provides averaged price information rather than real-time price and reacts slowly to volatility — not suitable for scalpers and high-frequency traders. I added HARSI indicator as a supportive signal because it is useful with using CORAL and SSL channel indicators. If you change your candle types to Heikin Ashi , your profit will change in good way but dont rely on it.
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█ THANKS:
Special thanks to authors of the scripts that i used.
@LazyBear and @ErwinBeckers and @JayRogers and @ToFFF
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█ DISCLAIMER
Any trade decisions you make are entirely your own responsibility.
Equities Risk Tool [vnhilton]To quickly apply this indicator onto the chart, open the source code in Pine Editor & click 'Add to chart'. Perhaps in the future, TradingView will add a feature where you can have favourited indicators on the favourites toolbar alongside the favourited drawing tools. 🤔
Traders will need to calculate how many shares are needed for their position, where if price goes against them towards their stop loss, then they'll lose the amount that they risked on that trade. The formula for this is: Amount willing to risk / Stop loss distance. Traders can carry out these calculations via a calculator, spreadsheet or a simple program with outputs generated from inputs. These 3 methods have 1 thing in common, & it's that you have to manually input the the values, which isn't very convenient, especially for traders trading in a fast paced environment, where milliseconds matter. This indicator is similar to TradingView's Long & Short Position tools, & removes this inconvenience by allowing you to only click to submit your entry & stop out levels, without having to type a single thing (the only thing that would require typing is your account equity in the settings).
This indicator will display lines on the chart showing the entry, stop-out & several profit target levels. The entry & stop-out levels can be moved in any direction as desired, & the profit target levels following suit. You're able to adjust the different profit factors if you're aiming for different reward targets (e.g. You want a 1:2 RR trade, so the profit factor here will be 2 - 2 times the distance between the entry level & stop out level).
A table will also be displayed showing the direction of the position, alongside the shares required for several account risks which is useful if trading different quality setups from A-D for example. The calculated shares displayed are also shown in proportions as well. Here, you're able to see 25%-50%-75%-100% of calculated shares, which may be useful when scaling in/out of trades. All mentioned features are customisable.
Calculated shares for long & short positions can be rounded down to any decimal places. This can be useful if you intend to trade e.g. in batches of 100, then you would use a round down factor of -2.
Divergence Cheat Sheet'Divergence Cheat Sheet' helps in understanding what to look for when identifying divergences between price and an indicator. The strength of a divergence can be strong, medium, or weak. Divergences are always most effective when references prior peaks and on higher time frames. The most common indicators to identify divergences with are the Relative Strength Index (RSI) and the Moving average convergence divergence (MACD).
Regular Bull Divergence: Indicates underlying strength. Bears are exhausted. Warning of a possible trend direction change from a downtrend to an uptrend.
Hidden Bull Divergence: Indicates underlying strength. Good entry or re-entry. This occurs during retracements in an uptrend. Nice to see during the price retest of previous lows. “Buy the dips."
Regular Bear Divergence: Indicates underlying weakness. The bulls are exhausted. Warning of a possible trend direction change from an uptrend to a downtrend.
Hidden Bear Divergence: Indicates underlying weakness. Found during retracements in a downtrend. Nice to see during price retests of previous highs. “Sell the rallies.”
Divergences can have different strengths.
Strong Bull Divergence
Price: Lower Low
Indicator: Higher Low
Medium Bull Divergence
Price: Equal Low
Indicator: Higher Low
Weak Bull Divergence
Price: Lower Low
Indicator: Equal Low
Hidden Bull Divergence
Price: Higher Low
Indicator: Higher Low
Strong Bear Divergence
Price: Higher High
Indicator: Lower High
Medium Bear Divergence
Price: Equal High
Indicator: Lower High
Weak Bear Divergence
Price: Higher High
Indicator: Equal High
Hidden Bull Divergence
Price: Lower High
Indicator: Higher High
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
TrapulatorA position size, stop loss and take profit calculator to make forex trading easier.
Utilizes the symbol, payout rates, etc.
How to Use:
Go to the indicator's settings and change the "Settings" and "Entry" sections. After saving your settings, the values will be drawn in a table on the chart.
This has been republished, so that it has the correct name. You can't change the name of scripts once they've been published as far as I'm aware. So, this version of the script will receive updates, and the Trap Calculator will just be a 1.0 version, that's available.
There are default values within the calculator, so use at your own risk and do your own due diligence. This is public so that you can use it to make a calculator that better suits your needs if you want.
TradingView Alerts (Expo)█ Overview
The TradingView inbuilt alert feature inspires this alert tool.
TradingView Alerts (Expo) enables traders to set alerts on any indicator on TradingView, both public, protected, and invite-only scripts (if you are granted access). In this way, traders can set the alerts they want for any indicator they have access to. This feature is highly needed since many indicators on TradingView do not have the particular alert the trader looks for, this alert tool solves that problem and lets everyone create the alert they need. Many predefined conditions are included, such as "crossings," "turning up/down," "entering a channel," and much more.
█ TradingView alerts
TradingView alerts are a popular and convenient way of getting an immediate notification when the asset meets your set alert criteria. It helps traders to stay updated on the assets and timeframe they follow.
█ Alert table
Keep track of the average amount of alerts that have been triggered per day, per month, and per week. It helps traders to understand how frequently they can expect an alert to trigger.
█ Predefined alerts types
Crossing
The Crossing alert is triggered when the source input crosses (up or down) from the selected price or value.
Crossing Down / Crossing Up
The Crossing Down alert is triggered when the source input crosses down from the selected price or value.
The Crossing Up alert is triggered when the source input crosses up from the selected price or value.
Greater Than / Less Than
The Greater Than alert is triggered when the source input reaches the selected value or price.
The Less Than alert is triggered when the source input reaches the selected value or price.
Entering Channel / Exiting Channel
The Entering Channel alert is triggered when the source input enters the selected channel value.
The Exiting Channel alert is triggered when the source input exits the selected channel value.
Notice that this alert only works if you have selected "Channels."
Inside Channel / Outside Channel
The Inside Channel alert is triggered when the source input is within the selected Upper and Lower Channel boundaries.
The Outside Channel alert is triggered when the source input is outside the selected Upper and Lower Channel boundaries.
Notice that this alert only works if you have selected "Channels."
Moving Up / Moving Down
This alert is the same as "crossing up/down" within x-bars.
The Moving Up alert is triggered when the source input increases by a certain value within x-bars.
The Moving Down alert is triggered when the source input decreases by a certain value within x-bars.
Notice that you have to set the Number of Bars parameter!
The calculation starts from the last formed candlestick.
Moving Up % / Moving Down %
The Moving Up % alert is triggered when the source input increases by a certain percentage value within x-bars.
The Moving Down % alert is triggered when the source input decreases by a certain percentage value within x-bars.
Notice that you have to set the Number of Bars parameter!
The calculation starts from the last formed candlestick.
Turning Up / Turning Down
The Turning Up alert is triggered when the source input turns up.
The Turning Down alert is triggered when the source input turns down.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems 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, backtest, 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/Algos/Systems are only for educational purposes!
Time OffsetCompare ticker with time offset.
I couldn't find anything like this. I was hoping to use it to find a ticker that might act like a leading indicator for another one! Who knows?
In the settings you can choose any ticker to compare, input the the number of bars you want it to be offset (positive or negative), and select plot source.
Variance (Welford) [Loxx]The standard deviation is a measure of how much a dataset differs from its mean; it tells us how dispersed the data are. A dataset that’s pretty much clumped around a single point would have a small standard deviation, while a dataset that’s all over the map would have a large standard deviation. You can. use this calculation for other indicators.
Given a sample the standard deviation is defined as the square root of the variance
Here you can find a Welford’s method for computing (single pass method) that avoids errors in some cases (if the variance is small compared to the square of the mean, and computing the difference leads catastrophic cancellation where significant leading digits are eliminated and the result has a large relative error)
Read more here: jonisalonen.com
Incliuded
Loxx's Expanded Source Types
Minimalist Doji HighlighterThis minimalism focused indicator was designed specifically to highlight doji candles (gravestone, long-legged, and dragonfly) that generally signal indecision/neutrality within price structure to improve trading accessibility for the disabled/visually impaired, visual charting cues or pattern visibility, and educational/learning purposes.
HOW TO USE IT:
Highlight doji candles to improve visual cues on chart.
Default size of 0.15-0.02 works best, yet can be modified thus ensuring flexibility of use and experimentation.
Additionally, there is an option to add crosses above doji candles for additional visual cues; this feature is set to false by default to avoid cluttering charts.
MARKET USAGE:
All time frames and assets.
MARKET CONDITIONS:
All conditions.
Filtered, N-Order Power-of-Cosine, Sinc FIR Filter [Loxx]Filtered, N-Order Power-of-Cosine, Sinc FIR Filter is a Discrete-Time, FIR Digital Filter that uses Power-of-Cosine Family of FIR filters. This is an N-order algorithm that allows up to 50 values for alpha, orders, of depth. This one differs from previous Power-of-Cosine filters I've published in that it this uses Windowed-Sinc filtering. I've also included a Dual Element Lag Reducer using Kalman velocity, a standard deviation filter, and a clutter filter. You can read about each of these below.
Impulse Response
What are FIR Filters?
In discrete-time signal processing, windowing is a preliminary signal shaping technique, usually applied to improve the appearance and usefulness of a subsequent Discrete Fourier Transform. Several window functions can be defined, based on a constant (rectangular window), B-splines, other polynomials, sinusoids, cosine-sums, adjustable, hybrid, and other types. The windowing operation consists of multipying the given sampled signal by the window function. For trading purposes, these FIR filters act as advanced weighted moving averages.
A finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying).
The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero.
FIR filters can be discrete-time or continuous-time, and digital or analog.
A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed.
An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight.
What is a Standard Deviation Filter?
If price or output or both don't move more than the (standard deviation) * multiplier then the trend stays the previous bar trend. This will appear on the chart as "stepping" of the moving average line. This works similar to Super Trend or Parabolic SAR but is a more naive technique of filtering.
What is a Clutter Filter?
For our purposes here, this is a filter that compares the slope of the trading filter output to a threshold to determine whether to shift trends. If the slope is up but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. If the slope is down but the slope doesn't exceed the threshold, then the color is gray and this indicates a chop zone. Alternatively if either up or down slope exceeds the threshold then the trend turns green for up and red for down. Fro demonstration purposes, an EMA is used as the moving average. This acts to reduce the noise in the signal.
What is a Dual Element Lag Reducer?
Modifies an array of coefficients to reduce lag by the Lag Reduction Factor uses a generic version of a Kalman velocity component to accomplish this lag reduction is achieved by applying the following to the array:
2 * coeff - coeff
The response time vs noise battle still holds true, high lag reduction means more noise is present in your data! Please note that the beginning coefficients which the modifying matrix cannot be applied to (coef whose indecies are < LagReductionFactor) are simply multiplied by two for additional smoothing .
Whats a Windowed-Sinc Filter?
Windowed-sinc filters are used to separate one band of frequencies from another. They are very stable, produce few surprises, and can be pushed to incredible performance levels. These exceptional frequency domain characteristics are obtained at the expense of poor performance in the time domain, including excessive ripple and overshoot in the step response. When carried out by standard convolution, windowed-sinc filters are easy to program, but slow to execute.
The sinc function sinc (x), also called the "sampling function," is a function that arises frequently in signal processing and the theory of Fourier transforms.
In mathematics, the historical unnormalized sinc function is defined for x ≠ 0 by
sinc x = sinx / x
In digital signal processing and information theory, the normalized sinc function is commonly defined for x ≠ 0 by
sinc x = sin(pi * x) / (pi * x)
For our purposes here, we are used a normalized Sinc function
Included
Bar coloring
Loxx's Expanded Source Types
Signals
Alerts
Related indicators
Variety, Low-Pass, FIR Filter Impulse Response Explorer
STD-Filtered, Variety FIR Digital Filters w/ ATR Bands
STD/C-Filtered, N-Order Power-of-Cosine FIR Filter
STD/C-Filtered, Truncated Taylor Family FIR Filter
STD/Clutter-Filtered, Kaiser Window FIR Digital Filter
STD/Clutter Filtered, One-Sided, N-Sinc-Kernel, EFIR Filt
Variety, Low-Pass, FIR Filter Impulse Response Explorer [Loxx]Variety Low-Pass FIR Filter, Impulse Response Explorer is a simple impulse response explorer of 16 of the most popular FIR digital filtering windowing techniques. Y-values are the values of the coefficients produced by the selected algorithms; X-values are the index of sample. This indicator also allows you to turn on Sinc Windowing for all window types except for Rectangular, Triangular, and Linear. This is an educational indicator to demonstrate the differences between popular FIR filters in terms of their coefficient outputs. This is also used to compliment other indicators I've published or will publish that implement advanced FIR digital filters (see below to find applicable indicators).
Inputs:
Number of Coefficients to Calculate = Sample size; for example, this would be the period used in SMA or WMA
FIR Digital Filter Type = FIR windowing method you would like to explore
Multiplier (Sinc only) = applies a multiplier effect to the Sinc Windowing
Frequency Cutoff = this is necessary to smooth the output and get rid of noise. the lower the number, the smoother the output.
Turn on Sinc? = turn this on if you want to convert the windowing function from regular function to a Windowed-Sinc filter
Order = This is used for power of cosine filter only. This is the N-order, or depth, of the filter you wish to create.
What are FIR Filters?
In discrete-time signal processing, windowing is a preliminary signal shaping technique, usually applied to improve the appearance and usefulness of a subsequent Discrete Fourier Transform. Several window functions can be defined, based on a constant (rectangular window), B-splines, other polynomials, sinusoids, cosine-sums, adjustable, hybrid, and other types. The windowing operation consists of multipying the given sampled signal by the window function. For trading purposes, these FIR filters act as advanced weighted moving averages.
A finite impulse response (FIR) filter is a filter whose impulse response (or response to any finite length input) is of finite duration, because it settles to zero in finite time. This is in contrast to infinite impulse response (IIR) filters, which may have internal feedback and may continue to respond indefinitely (usually decaying).
The impulse response (that is, the output in response to a Kronecker delta input) of an Nth-order discrete-time FIR filter lasts exactly {\displaystyle N+1}N+1 samples (from first nonzero element through last nonzero element) before it then settles to zero.
FIR filters can be discrete-time or continuous-time, and digital or analog.
A FIR filter is (similar to, or) just a weighted moving average filter, where (unlike a typical equally weighted moving average filter) the weights of each delay tap are not constrained to be identical or even of the same sign. By changing various values in the array of weights (the impulse response, or time shifted and sampled version of the same), the frequency response of a FIR filter can be completely changed.
An FIR filter simply CONVOLVES the input time series (price data) with its IMPULSE RESPONSE. The impulse response is just a set of weights (or "coefficients") that multiply each data point. Then you just add up all the products and divide by the sum of the weights and that is it; e.g., for a 10-bar SMA you just add up 10 bars of price data (each multiplied by 1) and divide by 10. For a weighted-MA you add up the product of the price data with triangular-number weights and divide by the total weight.
What's a Low-Pass Filter?
A low-pass filter is the type of frequency domain filter that is used for smoothing sound, image, or data. This is different from a high-pass filter that is used for sharpening data, images, or sound.
Whats a Windowed-Sinc Filter?
Windowed-sinc filters are used to separate one band of frequencies from another. They are very stable, produce few surprises, and can be pushed to incredible performance levels. These exceptional frequency domain characteristics are obtained at the expense of poor performance in the time domain, including excessive ripple and overshoot in the step response. When carried out by standard convolution, windowed-sinc filters are easy to program, but slow to execute.
The sinc function sinc (x), also called the "sampling function," is a function that arises frequently in signal processing and the theory of Fourier transforms.
In mathematics, the historical unnormalized sinc function is defined for x ≠ 0 by
sinc x = sinx / x
In digital signal processing and information theory, the normalized sinc function is commonly defined for x ≠ 0 by
sinc x = sin(pi * x) / (pi * x)
For our purposes here, we are used a normalized Sinc function
Included Windowing Functions
N-Order Power-of-Cosine (this one is really N-different types of FIR filters)
Hamming
Hanning
Blackman
Blackman Harris
Blackman Nutall
Nutall
Bartlet Zero End Points
Bartlet-Hann
Hann
Sine
Lanczos
Flat Top
Rectangular
Linear
Triangular
If you wish to dive deeper to get a full explanation of these windowing functions, see here: en.wikipedia.org
Related indicators
STD-Filtered, Variety FIR Digital Filters w/ ATR Bands
STD/C-Filtered, N-Order Power-of-Cosine FIR Filter
STD/C-Filtered, Truncated Taylor Family FIR Filter
STD/Clutter-Filtered, Kaiser Window FIR Digital Filter
STD/Clutter Filtered, One-Sided, N-Sinc-Kernel, EFIR Filt
Automatic Order Block + Imbalance by D. BrigagliaThis script combines automatic orderblock and imbalance tracking.
Bullish OB - Blue
Bullish Imbalance - Green
Bearish OB - Red
Bearish Imbalance - Orange
Please note that the actual definitions of orderblock and imbalance are not respected in this script for the sake of simplicity. Scripts that are too complex may overfit some particular chart. Since there is no way to translate the actual ob and imb definitions into pinescript language, I decided to keep it simple.
Ideally, you want to see a bullish OB followed by buy side imbalance, or viceversa. OBs that are broken weakly are generally invalidated, ones that are broken strongly generally become breakers, and you can use them as good support/resistance levels.
Also, a good thing you can do when an OB and an imbalance match, is going in the lower timeframes and catching the structure reversal in the OB or imbalance zone. That may provide excellent RR trades. Always trade with OB that confirm the HTF trend.
Nothing in my content on tradingview is considerable investment advice.
Bollinger Pair TradeNYSE:MA-1.6*NYSE:V
Revision: 1
Author: @ozdemirtrading
Revision 2 Considerations :
- Simplify and clean up plotting
Disclaimer: This strategy is currently working on the 5M chart. Change the length input to accommodate your needs.
For the backtesting of more than 3 months, you may need to upgrade your membership.
Description:
The general idea of the strategy is very straightforward: it takes positions according to the lower and upper Bollinger bands.
But I am mainly using this strategy for pair trading stocks. Do not forget that you will get better results if you trade with cointegrated pairs.
Bollinger band: Moving average & standard deviation are calculated based on 20 bars on the 1H chart (approx 240 bars on a 5m chart). X-day moving averages (20 days as default) are also used in the background in some of the exit strategy choices.
You can define position entry levels as the multipliers of standard deviation (for exp: mult2 as 2 * standard deviation).
There are 4 choices for the exit strategy:
SMA: Exit when touches simple moving average (SMA)
SKP: Skip SMA and do not stop if moving towards 20D SMA, and exit if it touches the other side of the band
SKPXDSMA: Skip SMA if moving towards 20D SMA, and exit if it touches 20D SMA
NoExit: Exit if it touches the upper & lower band only.
Options:
- Strategy hard stop: if trade loss reaches a point defined as a percent of the initial capital. Stop taking new positions. (not recommended for pair trade)
- Loss per trade: close position if the loss is at a defined level but keeps watching for new positions.
- Enable expected profit for trade (expected profit is calculated as the distance to SMA) (recommended for pair trade)
- Enable VIX threshold for the following options: (recommended for volatile periods)
- Stop trading if VIX for the previous day closes above the threshold
- Reverse active trade direction if VIX for the previous day is above the threshold
- Take reverse positions (assuming the Bollinger band is going to expand) for all trades
Backtesting:
Close positions after a defined interval: mark this if you want the close the final trade for backtesting purposes. Unmark it to get live signals.
Use custom interval: Backtest specific time periods.
Other Options:
- Use EMA: use an exponential moving average for the calculations instead of simple moving average
- Not against XDSMA: do not take a position against 20D SMA (if X is selected as 20) (recommended for pairs with a clear trend)
- Not in XDSMA 1 DEV: do not take a position in 20D SMA 1*standart deviation band (recommended if you need to decrease # of trades and increase profit for trade)
- Not in XDSMA 2 DEV: do not take a position in 20D SMA 2*standart deviation band
Session management:
- Not in session: Session start and end times can be defined here. If you do not want to trade in certain time intervals, mark that session.(helps to reduce slippage and get more realistic backtest results)
PipMotionFXHi guys,
If you are looking to add some watermark into your charts. You can use this indicator.
You can add add a title and a subtitle, if you want to write in diferents lines, you can use as you can see in the script.
All the features are customizable: position, text size, text color, background.
Enjoy it.
[EDU] Close Open Estimation Signals (COE Signals)EN:
Close Open Estimation ( aka COE ) is a very simple swing-trading indicator based on even simpler idea. This indicator is from my educational series, which means that I just want to share with another way to look at the market in order to broaden your knowledge .
Idea :
Let's take n previous bars and make a sum a of close - open -values of each bar. Knowledgeable of you may already see the similarity to RSI calculation idea . Now let's plot this sum and see what we have now.
We can see, that whenever COE crosses over 0-level, uptrend begins, and if COE crosses under 0-level, downtrend begins. The speed of such signals can be adjusted by changing lookback period: the lower the lookback, the faster signals you get, but high-quality ones can be obtained only via not-so-fast lookback as when the market is consolidating or volatility is to high, there can be many garbage signals, like 95+% of other indicators have.
Let's explore more and calculate volatility of COE(v_coe in the code): current COE - previous CEO .
Now it appears that when v_coe crosses over 0-level, it's a signal, that this is a new low and soon the uptrend will follow. Analogically for crossing under 0-level .
I guess now you understood what these all are about: COE crossings show global trend signals , while Volatility COE ( v_coe or VCOE ) crossings show reversal points .
For signals I further calculated volatility of VCOE(VVCOE) and then volatility of VVCOE(VVVCOE). Why? Because for me they seem to be more accurate, but you are welcome to experiment and figure best setups for yourself and by yourself, I just share my opinion and experience .
COE can be helpful only in high liquidity markets with good trend or wide sideways .
If you want to experiment with COE, just copy the code and play with it. Curious of you will probably find it helpful eventhough the idea is way too simple.
By it's perfomance COE can probably beat QQE at open price settings.
(use open of the price at indicator to get zero repaint! )
Examples :
If you any questions, feel free to DM me or leave comments.
Good luck and take your profits!
- Fyodor Tarasenko
RU:
Close Open Estimation ( aka COE ) — это очень простой индикатор свинг-трейдинга, основанный на еще более простой идее. Этот индикатор из моей образовательной серии, а это значит, что я просто хочу поделиться с другим взглядом на рынок , чтобы расширить ваши знания .
Идея :
Возьмем n предыдущих баров и составим сумму a из close - open -значений каждого бара. Знающие люди могут уже заметить сходство с идеей расчета RSI . Теперь давайте построим эту сумму и посмотрим, что у нас сейчас есть.
Мы видим, что всякий раз, когда COE пересекает выше 0-уровня, начинается восходящий тренд , а если COE пересекает ниже 0-уровня, начинается нисходящий тренд. Скорость таких сигналов можно регулировать изменением ретроспективы: чем меньше ретроспектива, тем быстрее вы получаете сигналы, но качественные можно получить только через не- такой быстрый взгляд назад, как когда рынок консолидируется или волатильность слишком высока, может быть много мусорных сигналов, как у 95+% других индикаторов.
Давайте рассмотрим больше и рассчитаем волатильность COE(v_coe в коде): текущий COE - предыдущий CEO .
Теперь кажется, что когда v_coe пересекает уровень 0, это сигнал о том, что это новый минимум и вскоре последует восходящий тренд . Аналогично для пересечения под 0-уровнем .
Думаю, теперь вы поняли, о чем все это: COE пересечения показывают глобальные сигналы тренда , а пересечения Volatility COE ( v_coe или VCOE ) показывают точки разворота .
Для сигналов я дополнительно рассчитал волатильность VCOE(VVCOE), а затем волатильность VVCOE(VVVCOE). Почему? Потому что для меня они кажутся более точными, но вы можете поэкспериментировать и подобрать оптимальные настройки для себя и для себя, я просто делюсь своим мнением и опытом .
COE может быть полезен только на рынках с высокой ликвидностью и хорошим трендом или широким боковиком .
Если вы хотите поэкспериментировать с COE, просто скопируйте код и поэкспериментируйте с ним. Любознательные из вас, вероятно, сочтут это полезным, хотя идея слишком проста.
По своей результативности СОЕ может составить конкуренцию широко известному QQE, используя open цены.
(используйте open цены на индикаторе, чтобы получить нулевую перерисовку! )
Примеры :
Если у вас есть вопросы, пишите мне в личные сообщения или оставляйте комментарии.
Удачи и профита всем!
- Федор Тарасенко
ABC 123 Harmonic Ratio Custom Range Interactive█ OVERVIEW
This indicator was designed based on Harmonic Trading : Volume One written by Scott Carney.
This is about harmonic ratios which expanded through retracement and projection.
Derivation is pretty much explained here such as Primary, Primary Derivation, Secondary Derivation and Secondary Derivation Extreme.
Derivation value depends on minimum retracement or maximum projection.
This derivation value utilize Fibonacci value which later expand to Harmonic Ratio.
█ INSPIRATION
Inspired by design, code and usage of CAGR . Basic usage of custom range / interactive, pretty much explained here . Credits to TradingView.
This build is based and visualized upon Harmonic Trading Ratios.
This build also was stripped down from XABCD Harmonic Pattern Custom Range Interactive .
█ CREDITS
Scott Carney, Harmonic Trading : Volume One (Page 18)
█ FEATURES
Table can positioned by any position and font size can be resized.
Labels can be either changed to alphabets or numbers.
█ HOW TO USE
Draw points from Point A to Point C.
Dont worry about magnet, point will attached depends on High or Low of the candle.
█ USAGE / TIPS EXAMPLES (Description explained in each image)
Kalman Gain Parameter MechanicsFrequently asked question is to explain how Gain parameter works in kalman funtion. This script serves as a visual representation of Gain parameter of Kalman function used in HMA-Kalman & Trendlines script. (The function creator's name was misspeled in that script as Kahlman)
To see better results set your Chart's timeframe to Daily.
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
Fractal Dimension Index Adaptive Period [Loxx]Fractal Dimension Index Adaptive Period is the adaptive period out of Fractal Dimension Index Adaptivity. This isn't an indicator that shows a signal, instead, it's to be used as auxiliary support and an educational tool to create other indicators. This value can be injected into other indicators to make those indicators Fractal Dimension Index Adaptive.
What is the Fractal Dimension Index?
The goal of the fractal dimension index is to determine whether the market is trending or in a trading range. It does not measure the direction of the trend. A value less than 1.5 indicates that the price series is persistent or that the market is trending. Lower values of the FDI indicate a stronger trend. A value greater than 1.5 indicates that the market is in a trading range and is acting in a more random fashion.
Included
Loxx's Expanded Source Types
