Sequential Filter - An Original Filtering ApproachRemoving irregular variations in the closing price remain a major task in technical analysis, indicators used to this end mostly include moving averages and other kind of low-pass filters. Understanding what kind of variations we want to remove is important, irregular (noisy) variations have mostly a short term period, fully removing them can be complicated if the filter is not properly selected, for example we might want to fully remove variations with a period of 2 bars and lower, if we select an arithmetic moving average the filter output might still contain such variations because of the ripples in the frequency response passband, all it would take is a variation of high amplitude for that variation to be clearly visible.
Although all it would take for better filtering is a filter with better performance in the frequency domain (gaussian, Butterworth, Bessel...) we can design innovative approaches that does not rely on the model of classical moving averages, today a new technical indicator is proposed, the technical indicator fully remove variations lower than the selected period.
The Indicator Approach
In order for the indicator output to change the closing price need to produce length consecutive up's/down's, length control the variation threshold of the indicator, variations lower than length are fully removed. Lets see a visual example :
Here length = 3, the closing price need to make 3 consecutive up's/down's, when the sequence happen the indicator output is equal to src , here the closing price, else the indicator is equal to its precedent value, hence removing other variations. The value of 3 is the value by default, this is because i have seen in the past that the average smallest variations period where in general of 3 bars.
Because the indicator focus only on the variation sign, it totally ignore the amplitude of the movement, this provide an effective way to filter short term retracement in a fluctuation as show'n below :
The candle option of the indicator allow the indicator to only focus on the body color of a candle, thus ignoring potential gaps, below is an example with the candle option off :
If we activate the "candle" option we end up with :
Note that the candle option is based on the closing and opening price, if you use the indicator on another indicator output make sure to have the candle option off.
Length and Indicator Color
The closing price is infected by noise, and will rarely make a large sequence of consecutive up's/down's, the indicator can therefore be useful to detect consecutive sequence of length period, here 6 is selected on BTCUSD :
A consecutive up's/down's of period 6 can be considered a relatively rare event.
It is important to note that the color of the indicator used by default has nothing to do with the consecutive sequence detected, that is the indicator turning red doesn't necessarily mean that a consecutive down's sequence has occurred, but only that this sequence has occurred at a lower value than the precedent detected sequence. This is show'n below :
In order to make the indicator color based on the detected sequence check the "Color Based On Detected Sequence" option.
Conclusion
An original approach on filtering price variations has been proposed, i believe the indicator code is elegant as well as relatively efficient, and since high values of length can't really be used the indicator execution speed will remain relatively fast.
Thanks for reading !
חפש סקריפטים עבור "ha溢价率"
HEMA - A Fast And Efficient Estimate Of The Hull Moving AverageIntroduction
The Hull moving average (HMA) developed by Alan Hull is one of the many moving averages that aim to reduce lag while providing effective smoothing. The HMA make use of 3 linearly weighted (WMA) moving averages, with respective periods p/2 , p and √p , this involve three convolutions, which affect computation time, a more efficient version exist under the name of exponential Hull moving average (EHMA), this version make use of exponential moving averages instead of linearly weighted ones, which dramatically decrease the computation time, however the difference with the original version is clearly noticeable.
In this post an efficient and simple estimate is proposed, the estimation process will be fully described and some comparison with the original HMA will be presented.
This post and indicator is dedicated to LucF
Estimation Process
Estimating a moving average is easier when we look at its weights (represented by the impulse response), we basically want to find a similar set of weights via more efficient calculations, the estimation process is therefore based on fully understanding the weighting architecture of the moving average we want to estimate.
The impulse response of an HMA of period 20 is as follows :
We can see that the first weights increases a bit before decaying, the weights then decay, cross under 0 and increase again. More recent closing price values benefits of the highest weights, while the oldest values have negatives ones, negative weighting is what allow to drastically reduce the lag of the HMA. Based on this information we know that our estimate will be a linear combination of two moving averages with unknown coefficients :
a × MA1 + b × MA2
With a > 0 and b < 0 , the lag of MA1 is lower than the lag of MA2 . We first need to capture the general envelope of the weights, which has an overall non-linearly decaying shape, therefore the use of an exponential moving average might seem appropriate.
In orange the impulse response of an exponential moving average of period p/2 , that is 10. We can see that such impulse response is not a bad estimate of the overall shape of the HMA impulse response, based on this information we might perform our linear combination with a simple moving average :
2EMA(p/2) + -1SMA(p)
this gives the following impulse response :
As we can see there is a clear lack of accuracy, but because the impulse response of a simple moving is a constant we can't have the short increasing weights of the HMA, we therefore need a non-constant impulse response for our linear combination, a WMA might be appropriate. Therefore we will use :
2WMA(p/2) + -1EMA(p/2)
Note that the lag a WMA is inferior to the lag of an EMA of same period, this is why the period of the WMA is p/2 . We obtain :
The shape has improved, but the fit is poor, which mean we should change our coefficients, more precisely increasing the coefficient of the WMA (thus decreasing the one of the EMA). We will try :
3WMA(p/2) + -2EMA(p/2)
We then obtain :
This estimate seems to have a decent fit, and this linear combination is therefore used.
Comparison
HMA in blue and the estimate in fuchsia with both period 50, the difference can be noted, however the estimate is relatively accurate.
In the image above the period has been set to 200.
Conclusion
In this post an efficient estimate of the HMA has been proposed, we have seen that the HMA can be estimated via the linear combinations of a WMA and an EMA of each period p/2 , this isn't important for the EMA who is based on recursion but is however a big deal for the WMA who use recursion, and therefore p indicate the number of data points to be used in the convolution, knowing that we use only convolution and that this convolution use twice less data points then one of the WMA used in the HMA is a pretty great thing.
Subtle tweaking of the coefficients/moving averages length's might help have an even more accurate estimate, the fact that the WMA make use of a period of √p is certainly the most disturbing aspect when it comes to estimating the HMA. I also described more in depth the process of estimating a moving average.
I hope you learned something in this post, it took me quite a lot of time to prepare, maybe 2 hours, some pinescripters pass an enormous amount of time providing content and helping the community, one of them being LucF, without him i don't think you'll be seeing this indicator as well as many ones i previously posted, I encourage you to thank him and check his work for Pinecoders as well as following him.
Thanks for reading !
Red and Green Ignored Bar by Oliver VelezOn this occasion I present a script that detects Ignored Red Candles and Ignored Green Candles, basically it is a Price Action event that indicates a possible continuation of the current trend and gives the opportunity to climb it with a Very tight risk, before delving into detail I would like to leave this note:
Note: the detection of this event does not guarantee that the signal will be good, the trader must have the ability to determine its quality based on aspects such as trend, maturity, support / resistance levels, expansion / contraction of the market, risk / benefit, etc, if you do not have knowledge about this you should not use this indicator since using it without a robust trading plan and experience could cause you to partially or totally lose your money, if this is your case you should train before If you try to extract money from the market, this script was created to be another tool in your trading plan in order to configure the rules at your discretion, execute them consistently and have AUTOMATIC ALERTS when the event occurs, which is where I find more value because you can have many instruments waiting for the event to be generated, in the time frame you want and without having to observe the mer When the alert is generated, the Trader should evaluate the quality of the alert and define whether or not to execute it (higher timeframes, they can give you more time to execute the operation correctly).
Let's continue….
This event was created by Oliver Velez recognized trader / mentor of price action, the event has a very interesting particularity since it allows to take a position with a very limited risk in trend movements, this achieves favorable operations of good ratio and small losses when taking An adjusted risk, if the trade works, a good ratio is quickly achieved and we agree with a key point in the “Keep small losses and big profits” trading, this makes it easier to have a positive mathematical hope when your level of Success is not very high, so leave you in the field of profitability.
THE EVENT:
The event has a bullish configuration (Ignored Red Candle) and a bearish configuration (Ignored Green Candle), below I detail the “Hard” rules (later I explain why “Hard”):
1- Last 3 bars have to be GREEN-RED-GREEN (possible bullish configuration) or RED-GREEN-RED (possible bearish configuration), the first bar is called Control Bar, the second is called Ignored Bar and the third Signal Bar as shown in the following image:
2- Be in a trend determined by simple moving averages (Slow of 20 periods and Fast of 8 periods), as a general rule you can take the direction of MA20 but the Trader has to determine if there is a trend movement or not.
3- Control bar of good range, little tail and with a body greater than 55%.
4- Ignored bar preferably narrow range, little tail and that is located in the upper 1/3 of the control bar.
5- Signal bar cannot override the minimum of the ignored bar.
6- Activation / Confirmation of event by means of signal bar in overcoming the body of the ignored bar.
Some examples of ignored bars (with “Hard” and “Flexible” rules):
Features and configuration of the indicator:
To access the indicator settings, press the wheel next to the indicator name VVI_VRI "Configuration options".
- Operation mode (Filtering Type):
• Filtering Complete: all filters activated according to the configuration below.
• Without Filtering: all filters deactivated, all VRI / VVI are displayed without any selection criteria.
• Trend Filter only: shows only VRI / VVI that are in accordance with what is set in “Trend Settings”
- Configuration Moving Averages:
• See Slow Media: slow moving average display with direction detection and color change.
• See Fast Media: display of fast moving average with direction detection and color change.
• Type: possibility to choose the type of media: DEMA, EMA, HullMA, SMA, SSMA, SSMA, TEMA, TMA, VWMA, WMA, ZEMA)
• Period: number of previous bars.
• Source: possibility to choose the type of source, open, close, high, low, hl2 hlc3, ohlc4.
• Reaction: this configuration affects the color change before a change of direction, 1 being an immediate reaction and higher values, a more delayed reaction obtaining les false "changes of direction", a value of 3 filters the direction quite well.
- Trend Configuration
• Uptrend Condition P / VRI: possibility to select any of these conditions:
o Bullish MA direction
o Quick bullish MA direction
o Slow and fast bullish MA direction
o Price higher than slow MA
o Price higher than fast MA
o Price higher than slow and fast MA
o Price higher than slow MA and bullish direction
o Price higher than fast MA and bullish direction
o Price higher than slow, fast MA and bullish direction
o No condition
• Condition P / VVI bear trend: possibility of selecting any of these conditions:
o Slow bearish MA direction
o Fast bearish MA direction
o Slow and fast bearish MA direction
o Price less than slow MA
o Price less than fast MA
o Price less than slow and fast MA
o Price lower than slow MA and bearish direction
o Price less than fast MA and bearish direction
o Price less than slow, fast MA and bearish direction
o No condition
- Control bar configuration
• Minimum body percentage%: possibility to select what body percentage the bar must have.
• Paint control bar: when selected, paint the control bar.
• See control bar label: when selected, a label with the legend BC is plotted.
- Configuration bar ignored
• Above X% of the control bar: possibility to select above what percentage of the control bar the ignored bar must be located.
• Paint ignored bar: when selected, paint the ignored bar.
- Signal bar configuration
• You cannot override the minimum of the ignored bar: when selected, the condition is added that the signal bar cannot override the minimum of the ignored bar.
• Paint signal bar: when selected, paint the signal bar.
• See arrow: when selected it shows the direction arrow of the possible movement.
• See bear and arrow: when selected it shows bear and arrow label
• See bull and arrow: when selected it shows bull and arrow label
The following image shows the ignored bar and painted signal:
- Take profit / loss
The profit / loss taking varies depending on the trader and its risk / monetary plan, the proposal is a recommendation based on the nature of the event that is to have a small risk unit (stop below the minimum of the ignored bar), look for objectives in ratios greater than 2: 1 and eliminate the risk in 1: 1 by taking the stop to BE, all parameters are configurable and are the following:
• See recommended stop loss and take profit: trace the levels of Stop, BE, TP1 and TP2, as well as their prices to know them quickly based on the assumed risk
• To: select which event you want to draw the SL and TP (VRI, VVI)
• Extend stop loss line x bars: allows extending the stop line by x number of bars
• Extend take profit line x bars: allows extending the stop line by x number of bars
• Ratio to move to break even: allows you to select the minimum ratio to move stop to break even (default 1: 1)
• Take profit 1 ratio: allows you to select the ratio for take profit 1 (default 2: 1)
• Take profit 2 ratio: allows you to select the ratio for take profit 2 (default 4: 1)
- Alerts
• It is possible to configure the following alerts:
-VRI DETECTED
-VVI DETECTED
-VRI / VVI DETECTED
Final Notes:
- The term hard rules refers to the fact that an event is sought with the rules detailed above to obtain a high quality event but this brings 2 situations to consider, less
number of events and events that are generated in a strong impulse may be leaked, a very large control bar followed by an ignored narrow body away from moving averages, despite having a good chance of continuing, taking a stop very tight in a strong impulse you can touch it by the simple fact of the own volatility at that time.
- The setting of the parameters “Minimum body percentage% (control bar)”, “Above x% of the control bar (bar ignored)” and “Cannot override the minimum of the ignored bar” can bring large Benefits in terms of number of events and that can also be of high quality, feel free to find the best configuration for your instrument to operate.
- It is recommended to look for trending events, near moving averages and at an early stage of it.
- The display of several nearby VRIs or VVIs in an advanced trend may indicate a depletion of it.
- The alerts can be worked in 2 ways: at the closing of the candle (confirms event but the risk unit may be larger or smaller) or immediately the body of the ignored bar is exceeded, in case you are operating from the mobile and miss many events because of the short time I recommend that you operate in a superior time frame to have more time.
- The indicator is configured with “flexible” rules to have more events, but without any important criteria, each trader has to look for the best configuration that suits his instrument.
- It is recommended to partially close the operation based on the ratio and always keep a part of the position to apply manual trailing stop and try to maximize profits.
The code is open feel free to use and modify it, a mention in credits is appreciated.
If you liked this SCRIPT THUMB UP!
Greetings to all, I wish you much green!
Liquid RSI - Marrying The Relative Strength Index And The VolumeIntroduction
I recently derived the calculation of the relative strength index, an indicator that aim to spot overbought and oversold assets, but what is an overbought/sold asset ? Can such things be estimated with price alone ?
This why i propose a modification of the relative strength using my recently proposed efficient calculation including volume information in order to spot overbought/sold asset.
Scaling A Liquid Market
The relative strength index detect an overbought/sold asset when higher/lower than a certain level, often 80/20. An overbought asset, or better say over evaluated, is more attractive to sell because prices are no longer attractive to buy, it has reached its value of interest for traders looking to go long, we can then expect the price to correct and start a trend of opposite direction, while an oversold asset is more attractive to buy based on the same logic.
The idea of talking about over bought and over sold without taking into account the volume can be a bit strange, since volume is directly related to the quantity of contracts traded, an higher volume can show sign of a more active market, which can describe the terms : overbought/sold a bit better. Many indicators used the rsi framework with volume, the money flow index for example, but it can be interesting to provide other alternatives.
The Indicator
The indicator is based on the average positive changes in price multiplied by positive changes in volume divided by the average absolute change in price multiplied by the absolute changes in volume. The average is based on the wilder moving average which is a simple exponential filter with smoothing constant 1/length .
The indicator will react according to the volume magnitude, higher volumes will make the indicator go over/under the overbought/sold threshold more easily, in the image above, the indicator is currently saying that the market is under evaluated, which is not the case for the RSI. Such situation allow us to take a position that we could't take if we base our judgement only on price change magnitude.
The indicator has a tendency to be over/under the thresholds a longer period of time if the volume is relatively high.
An interesting effect the indicator has it to ignore movements with moderate volume, the indicator is less prone to cross under a threshold and to go back to it, this is shown in the image above. Another observation we can make is that the proposed indicator is smoother than the rsi, this is certainly due to the fact that the volume underweight small price changes.
Conclusions
I proposed a modification of the relative strength index that also take into account volume information. The proposed indicator is also smoother. Regarding its ability to detect overbought and oversold market, it has indeed the capacity to do it, however the problem remain the same, what is the extent of the correction following an overbought/oversold market ? We can see that the correction can be minor, and thus be followed by a large movements correlated with the main trend.
With those oscillators we are interested into knowing the end of the "whole trend", and they fail to do this because they use past information. I still hope the indicator find some creative usages amongst the community.
Thanks for reading ! And remember to ask before using the script code, it pains me to see minor changes on scripts i can pass 3 hours on.
Blackman Filter - The Smoother The BetterIntroduction
Who doesn't like smooth things? I'd like a smooth market price for christmas! But i can't get it, instead its so noisy...so you apply a filter to smooth it, such filters are called low-pass filters, they smooth and its great but they have lag, so nobody really use them, but they are pretty to look at.
Its on a childish note that i will introduce this indicator, so what it is all about? I propose a new FIR filter using a blackman function as filter kernel for financial time-series smoothing, do you prefer the childish tone ? Fear not its surprisingly easy!
The Blackman Function
The blackman function look like a bell shaped curve, look:
The blackman function will produce such curve. This function is called a cosine sum function because she is based on the sum of cosine functions, here only 2.
0.42 - 0.5 * cos(2 * pi * k) + 0.08 * cos(4 * pi * k)
Originally you use this function for windowing , what does it means? In signal processing you have a function called sync function , if you use this function as filter kernel you would get the ideal frequency domain response filter, sometime called brickwall filter, it would be extremely smooth.
Above the optimal low pass filter frequency response.
However the sync function has no ending values and goes on forever, therefore we can't use it for convolution, expect if we apply windowing. Filters using windowing are called windowed-sinc filters, i will describe the procedure below :
1 - Create a sync function = sin(pi*n)/(pi*n)
2 - Truncate it = I only keep the first length points of the sync function.
This create a abrupt end, the frequency of a filter using step 1 as kernel would contain ripples in the pass band and stop band, this is bad! The frequency response would look like this :
3 - I multiply my values of step 2 by a window function, it can the blackman window, i no longer have an abrupt end, its smooth!
The frequency response of the filter using this kernel would no longer have ripples! This is the power of windowing functions.
Here we are not using such thing, but we could in the future. Here instead we use the blackman function as filter kernel, because this function is bell shaped this mean that the filter will certainly be smooth (symmetrical weighting is a rule of thumb for kernels when we want really smooth filters).
The Filter
This filter is quite smooth, unlike the gaussian filter this filter give less weights to recent and past values, this is because the blackman function has fatter tails than the gaussian one. I could make a comparison of both, however they are quite alike, if you often use a gaussian filter its up to you to decide which one you prefer.
The filter can do a better job than the moving average when it comes to preserve the frequency components that constitute the cycles/trend.
We can see that the filter has a greater performance when it comes to keep the shape of the market price, thus it has a slightly better fit.
Conclusion
Ok so in this post you learned a bit about the sync function and windowing, those are basic subjects in signal processing, they allow us to approximate the filter with the ideal frequency response, i also showed you that those windowing function could be used as kernel and that they where pretty smooth on their own, there are many others, but the one i prefer is the blackman windowing function.
I know what you are thinking, "we want trailing stops, alerts, colors, arrows!", and i understand you pal, but sometimes its cool to take a break from all this stuff. However i can tell that i'am working on a side project that aim to estimate rolling maximum/minimum as fast as possible, any experiments will be published here, and i can ensure you that those indicators will make your day quite brighter, we will see that soon.
I hope you learned something from this post! I'am a bit tired (look i'am disappearing !)
Thanks for reading !
[RESEARCH] Heikin-Ashi Chart IdentifierA deterministic approach to identify Heikin-Ashi chart type.
The script checks the next statements about HA:
HA chart does not have any gaps in a classic sense
Every new HA open price is calculated using a specific recurrence formula. This fact also means that initial HA open price is used to calculate all the next and so on (a construction of Infinite Impulse Response filters)
The script works correctly being applied to other chart types:
Classic Candlestick
Range Bars
Line Break
Traditional Renko
ATR Renko
Traditional Point-and-Figure
ATR Point-and-Figure
Kagi
For special ones: this code allows you to check whether your script is being executed with Heikin-Ashi candles or not inside your script.
Ev sistr 'ta Laou!
How to avoid repainting when using security() - PineCoders FAQNOTE
The non-repainting technique in this publication that relies on bar states is now deprecated, as we have identified inconsistencies that undermine its credibility as a universal solution. The outputs that use the technique are still available for reference in this publication. However, we do not endorse its usage. See this publication for more information about the current best practices for requesting HTF data and why they work.
This indicator shows how to avoid repainting when using the security() function to retrieve information from higher timeframes.
What do we mean by repainting?
Repainting is used to describe three different things, in what we’ve seen in TV members comments on indicators:
1. An indicator showing results that change during the realtime bar, whether the script is using the security() function or not, e.g., a Buy signal that goes on and then off, or a plot that changes values.
2. An indicator that uses future data not yet available on historical bars.
3. An indicator that uses a negative offset= parameter when plotting in order to plot information on past bars.
The repainting types we will be discussing here are the first two types, as the third one is intentional—sometimes even intentionally misleading when unscrupulous script writers want their strategy to look better than it is.
Let’s be clear about one thing: repainting is not caused by a bug ; it is caused by the different context between historical bars and the realtime bar, and script coders or users not taking the necessary precautions to prevent it.
Why should repainting be avoided?
Repainting matters because it affects the behavior of Pine scripts in the realtime bar, where the action happens and counts, because that is when traders (or our systems) take decisions where odds must be in our favor.
Repainting also matters because if you test a strategy on historical bars using only OHLC values, and then run that same code on the realtime bar with more than OHLC information, scripts not properly written or misconfigured alerts will alter the strategy’s behavior. At that point, you will not be running the same strategy you tested, and this invalidates your test results , which were run while not having the additional price information that is available in the realtime bar.
The realtime bar on your charts is only one bar, but it is a very important bar. Coding proper strategies and indicators on TV requires that you understand the variations in script behavior and how information available to the script varies between when the script is running on historical and realtime bars.
How does repainting occur?
Repainting happens because of something all traders instinctively crave: more information. Contrary to trader lure, more information is not always better. In the realtime bar, all TV indicators (a.k.a. studies ) execute every time price changes (i.e. every tick ). TV strategies will also behave the same way if they use the calc_on_every_tick = true parameter in their strategy() declaration statement (the parameter’s default value is false ). Pine coders must decide if they want their code to use the realtime price information as it comes in, or wait for the realtime bar to close before using the same OHLC values for that bar that would be used on historical bars.
Strategy modelers often assume that using realtime price information as it comes in the realtime bar will always improve their results. This is incorrect. More information does not necessarily improve performance because it almost always entails more noise. The extra information may or may not improve results; one cannot know until the code is run in realtime for enough time to provide data that can be analyzed and from which somewhat reliable conclusions can be derived. In any case, as was stated before, it is critical to understand that if your strategy is taking decisions on realtime tick data, you are NOT running the same strategy you tested on historical bars with OHLC values only.
How do we avoid repainting?
It comes down to using reliable information and properly configuring alerts, if you use them. Here are the main considerations:
1. If your code is using security() calls, use the syntax we propose to obtain reliable data from higher timeframes.
2. If your script is a strategy, do not use the calc_on_every_tick = true parameter unless your strategy uses previous bar information to calculate.
3. If your script is a study and is using current timeframe information that is compared to values obtained from a higher timeframe, even if you can rely on reliable higher timeframe information because you are correctly using the security() function, you still need to ensure the realtime bar’s information you use (a cross of current close over a higher timeframe MA, for example) is consistent with your backtest methodology, i.e. that your script calculates on the close of the realtime bar. If your system is using alerts, the simplest solution is to configure alerts to trigger Once Per Bar Close . If you are not using alerts, the best solution is to use information from the preceding bar. When using previous bar information, alerts can be configured to trigger Once Per Bar safely.
What does this indicator do?
It shows results for 9 different ways of using the security() function and illustrates the simplest and most effective way to avoid repainting, i.e. using security() as in the example above. To show the indicator’s lines the most clearly, price on the chart is shown with a black line rather than candlesticks. This indicator also shows how misusing security() produces repainting. All combinations of using a 0 or 1 offset to reference the series used in the security() , as well as all combinations of values for the gaps= and lookahead= parameters are shown.
The close in the call labeled “BEST” means that once security has reached the upper timeframe (1 day in our case), it will fetch the previous day’s value.
The gaps= parameter is not specified as it is off by default and that is what we need. This ensures that the value returned by security() will not contain na values on any of our chart’s bars.
The lookahead security() to use the last available value for the higher timeframe bar we are using (the previous day, in our case). This ensures that security() will return the value at the end of the higher timeframe, even if it has not occurred yet. In our case, this has no negative impact since we are requesting the previous day’s value, with has already closed.
The indicator’s Settings/Inputs allow you to set:
- The higher timeframe security() calls will use
- The source security() calls will use
- If you want identifying labels printed on the lines that have no gaps (the lines containing gaps are plotted using very thick lines that appear as horizontal blocks of one bar in length)
For the lines to be plotted, you need to be on a smaller timeframe than the one used for the security() calls.
Comments in the code explain what’s going on.
Look first. Then leap.
ATR Squeeze Identifier + Last-Bar TR Stops1) Paints two lines based on previous bar's true range, has option for custom multiplier to make stop a factor of previous bar's TR. Used for quick identification of stop loss placement based on previous bar set-ups.
2) Identifies bar on close which has true range that is smaller than a period-chosen ATR criteria. Additionally, has an input for a raw "ATR Shave" which is used to narrow down bars with even smaller true range. Mostly used to identify potential entry zones where market is being squeezed, and expansion is likely to follow. Plots a character under the bar.
3) Identifies a close which includes and follows 5 consecutive closes which all exhibit smaller than average ATR. Includes customize-able ATR length and "ATR Shave" to narrow down tighter range's. Paints circle at bottom of chart. Mostly used to identify when market has been 'quiet' for some time and entries should be considered for likely expansion.
Help with SMA Crossover Demo scriptHi I'm currently in the process of learning to write a script. Here's a very basic SMA 34/4 crossover script. Is somebody able to help me with adding the following functions to the script.
1. Add an alert and indicator to close a short or long trade whenever any candle touches the SMA 34 line?
2. When a SMA 34/4 Crossover has been executed (a Short Trade condition) add an alert/indicator (Titled “Add”) every time a Green bullish candle has closed.
3. When a SMA 34/4 Crossunder has been executed (a Long Trade condition) add an alert/indicator (Titled “Add) every time a Red bearish candle has closed.
4. To used on 15m/30m/1hr/2hr/4hr/1D/1W timeframe charts?
SMA Crossover demoHi I'm currently in the process of learning to write a script. Here's a very basic SMA 34/5 crossover script. Is somebody able to help me with adding the following functions to the script.
1. Add an alert and indicator to close a short or long trade whenever any candle touches the SMA 34 line?
2. When a SMA 34/5 Crossover has been executed (a Short Trade condition) add an alert/indicator (Titled “Add”) every time a Green bullish candle has closed.
3. When a SMA 34/5 Crossunder has been executed (a Long Trade condition) add an alert/indicator (Titled “Add) every time a Red bearish candle has closed.
4. To used on 15m/30m/1hr/2hr/4hr/1D/1W timeframe charts?
Katana Gaps Bounty Hunter Pro (Show Gaps of All Types) by RRBKatana Gaps Bounty Hunter Pro (KGB Hunter Pro, Gap Exterminator) by RagingRocketBull 2018
Version 1.0
This indicator shows/counts/filters gaps on a chart.
There are several versions: Simple, Pro, Advanced and Zones. This is the Pro version. The Differences are listed below.
- Simple: shows/counts gaps, changes color based on gap dir (2 colors), filters out price gaps within session, large gaps, and high volume gaps
- Pro: +shows all types of gaps, multi color, pro filters (full/partial/overlapping time, price, large, candle, volume, doji, weekend gaps within delta ranges)
- Advanced: +session times mask, show/count gaps only for last N bars, +min/max/filled gaps stats, dark mode
- Zones: +shows gaps as dynamic horiz zones
KGB Hunter Pro Gap Exterminator focuses on showing you all possible types of gaps in multiple colors. Gap theory states that price tends to return and fill the gaps,
so you can use it to collect the bounty. You can apply any combination of complex filters to narrow down search results i.e., find only all:
- type 3 gaps up with allowed wick-candle overlapping of up to 10% and
- gap size larger than 200 and
- with at least one of the candles larger than 100 and
- volume change at least 40 and
- spanning less than 2 bar periods and
- excluding weekend gaps
Features:
- highlights gaps using barcolor and plotchar chars (8 colors x 2 dirs)
- supports all 3 types of gap overlapping: full gap (no overlapping), wick-wick and wick-body overlapping up to a specified % of candle body
- finds all types of gaps with pro filters for price, time, large, volume, timerange, candle size, doji gaps
- individual show/hide flags for each gap/char based on gap type
- can show/hide gaps/chars based on gap dir
- changes color of gaps/chars based on gap dir/type, multi color gap type combos
- displays chars above/below bar based on gap dir
- can show/hide weekend gaps
- counts all filtered gaps
Colors:
Basically There are 2 gap types (Price, Time) x 2 directions (Up, Down) x 2 modifiers (Large, Volume), Volume Gap is a separate class with its own modifiers, so more accurately:
- (Price, Time) x 2 directions (Up, Down) x Large modifier
- (Price Volume, Time Volume) x 2 directions (Up, Down) x Large modifier
using a total of 16+1 colors or 8+1 base colors + transparency modifier
depending on settings you can highlight gaps using any multi color combo from just 1 to all 16 colors (+1 gray color for weekends).
basic gap = 1 base color with normal transparency
price,time = 2 base colors (including basic gap) with normal transparency (+1 color)
* up,down dir = +2 new base colors with normal transparency (including 2 base colors), with a total of 2*2 = 4 price/time base colors (+2 colors)
* large = same 4 base colors with vivid transparency modifier (+4 colors)
* volume = +2 new base colors with normal transparency, a separate class (+2 colors)
* volume * up,down dir = +another 2 new base colors with normal transparency (including 2 volume base colors), with a total of 2*2 = 4 volume base colors (+2 colors)
* volume * large = 4 volume base colors with vivid transparency modifier (+4 colors)
weekend_gap = gray (+1 color)
doji gap, candle gap, timerange gap = no special color, inherits color from parent gap type
for more details, please see the Gap Color Hierarchy comments in code
_________________________________________________________________________
You can find the following gap related terminology in literature: full, partial, extreme, breakaway, runaway/continuation, common, exhaustion gaps.
There are no exact rules to distinguish between them, so this can't be implemented.
When defining a gap it all boils down to how do you plot a gap, which points between adjacent candles do you consider a gap. Different sources apply different methodology
but in practice only 3 types of gap overlapping can exist:
- full gap (no overlapping),
- partial (wick-wick overlapping) and
- extreme partial (wick-body overlapping up to a specified % of a candle body)
All these types are supported in this script. The only possible remaining option is candle-candle overlapping which is not a gap by definition.
Many other script specific subtypes are also supported. Please see description of each gap type below and comments in code.
General display modes
- gap has 3 possible overlapping modes: full gap (no overlapping), wick-wick overlapping, wick-candle overlapping up to a specified % of candle body size (for mode 3 only)
the remaining candle-candle overlapping implies not a gap by definition
full gap mode will find the least amount of gaps, wick-candle - the most
- gap can be either price or time, up or down, and shown above or below the candles (gap chars)
- by definition, a price gap is a smaller subset of a time gap, a gap within current session with a price gap and zero time lag between bars.
Therefore timerange filter is useless for price gaps, but can still be applied.
On the other hand, all price gap filters can be applied to time gaps without any distinction.
- gap can have multiple modifier subtypes: (price|time) * (up|down) * (large? + volume? + doji? + timerange? + weekend?)
i.e. price + large + volume + doji or time + large + volume + timerange + doji + weekend
- the gap is always counted only once no matter how many subtype modifiers it has
- if the gap does not satisfy any of the applied flags/filters it is not shown/counted (no gap bars/chars are shown)
- gap color can depend on a combo of gap type/dir and modifier subtypes or can be shown in a single base color
- char color can only depend on gap dir (not type/modifiers) or can be shown in a single base color
- char position can also depend on gap dir (above/below) the gap candle. Alternatively you can pin chars to the top/bottom of the screen in UI Styles.
- change_by_type = true - uses gap type base colors (2 colors + optional modifiers, up to 8 colors if volume and/or large filters are enabled)
- change_by_dir = true - uses gap dir base colors (2 colors + optional modifiers, up to 8 colors if volume and/or large filters are enabled)
- both change_by_type and change_by_dir = true - uses both gap type and dir base colors (4 colors + optional modifiers, up to 16 colors if volume and/or large filters are enabled)
- both change_by_type and change_by_dir = false - uses a single base gap color (1 color)
- don't need that much colors - disable filters
- highlight bars has priority over individual gap flags, when it is false all gaps are hidden regardless of their corresponding flag settings (does not affect dim weekend gaps)
- show chars has priority over individual gap char flags, when it is false all char flags are hidden regardless of their corresponding flag settings
- price gaps are only shown/counted when show_price_gaps flag is true. The large or volume filters can be used to narrow down results further.
- time gaps are only shown/counted when show_time_gaps flag is true. The large, volume, and timerange filters can be used to narrow down results further.
- doji gaps are only shown/counted when show_doji_gaps flag is true. The doji candle size and other filters can be used to narrow down results further.
- show weekend gaps = true and dim weekend gaps = false - shows/counts weekend gaps
- show weekend gaps = true and dim weekend gaps = true - dims weekend gaps, doesn't show/count weekwend gaps
- show/dim weekend gaps do just that - show the gap if it happens on a weekend, not all weekends
- large gaps are only shown/counted when the large filter is enabled != 0. positive values 5 (>= 5), negative -5 (<=5) are used to switch between <>
- volume gaps are only shown/counted when the volume filter is enabled != 0. positive values 5 (>= 5), negative -5 (<=5) are used to switch between <>
- timerange gaps are only shown/counted when the timerange filter is enabled != 0. positive values 5 (>= 5), negative -5 (<=5) are used to switch between <>
- candle size gaps are only shown/counted when the candle size filter is enabled != 0. positive values 5 (>= 5), negative -5 (<=5) are used to switch between <>
- candle size filter is the only filter with 2 arguments, use_and_for_delta to enable AND condition for the args (OR is the default)
Good Luck! Feel free to explore and learn from the code
Renko CandlesticksRenko charts are awesome . They reduce noise by only painting a brick on the chart when price moves by a specified amount up/down. When the price reverses, it must go twice the specified amount before a brick is painted. Time is not a factor, just price movement. Sometimes however, you want the pros of a renko chart, but on a regular candlestick chart. This indicator attempts to do just that.
A band is placed around price action showing the upper and lower bounds of what would be the current renko brick. The band only goes up/down when the price action itself moves up/down by the amount you specify. There are several ways of specifying the amount:
Fixed Price Amount: As the name says, you enter the brick size amount, i.e. the amount the price has to move before being in a new brick.
% of Price: This method will calculate the amount the price has to move as a percentage of the price itself. This way as price goes up/down, your brick size will adjust accordingly. Recommended values would be around 1% or less.
% of ATR: This option will make the brick size a percentage of the Average True Range. You can specify the ATR time frame to be different from your current time frame as well as the ATR length. For instance you could be on a 10 minute chart but specify the ATR to be daily with a length of 3 and a percentage amount of 15. This would make your brick size 15% of the Average True Range for the last 3 days. Recommended values are 10 to 20%.
Use this indicator on any time frame, even the 1 minute as the renko bands span the price action the same way on any time frame easily letting you know whether or not the price has moved appreciably, regardless of how much time has passed.
You can also set alerts easily, simply set the alert to crossing and choose “Renko Candlesticks” instead of “Value”. You will then see the options for the renko upper and lower bounds.
Tested on Bitcoin with the following values:
Fixed Price Amount: 30 ($30)
% of Price: 0.45 (if Bitcoin is $7000 then the brick size would be $31.50)
% of ATR: 15%, ATR Time Frame: 1D, ATR Length: 3 (3 days)
Ease of Movement (EOM) Backtest This indicator gauges the magnitude of price and volume movement.
The indicator returns both positive and negative values where a
positive value means the market has moved up from yesterday's value
and a negative value means the market has moved down. A large positive
or large negative value indicates a large move in price and/or lighter
volume. A small positive or small negative value indicates a small move
in price and/or heavier volume.
A positive or negative numeric value. A positive value means the market
has moved up from yesterday's value, whereas, a negative value means the
market has moved down.
You can change long to short in the Input Settings
WARNING:
- For purpose educate only
- This script to change bars colors.
Ease of Movement (EOM) Strategy This indicator gauges the magnitude of price and volume movement.
The indicator returns both positive and negative values where a
positive value means the market has moved up from yesterday's value
and a negative value means the market has moved down. A large positive
or large negative value indicates a large move in price and/or lighter
volume. A small positive or small negative value indicates a small move
in price and/or heavier volume.
A positive or negative numeric value. A positive value means the market
has moved up from yesterday's value, whereas, a negative value means the
market has moved down.
WARNING:
- This script to change bars colors.
Salty GRaB Wave with Highlights for Squeeze CCI-Arrows SlowStochThis indicator shows GRaB candles and allows several moving averages to be displayed at the same time.
It uses background coloring to identify momentum shifts. Wide bands of color can be used to identify trends while short bands of color can be used to identify reversals.
It has arrows above or below the candles to show CCI values above 100 or below -100 with the arrow pointing in the direction of the momentum.
It has red background coloring to show slow stochastic Overbought ranges and dark red signals indicating a cross of the fast and slow lines.
It has green background coloring to show slow stochastic Oversold ranges and dark green signals indicating a cross of the fast and slow lines.
It has yellow background to show squeezes with additional Squeeze information shown at the bottom of the chart in the form of letters and momentum arrows.
Ichimoku-Hausky Trading systemThis is a indicator with some parts of the ichimoku and EMA. It's my first script so i have used other peoples script (Chris Moody and DavidR) as reference cause I really have no idea myself on how to script with pinescript.
Hope that is okay!
I use 20M timeframe but it should work with any timeframe! I have not tested this system much so I would really appreciate feedback and tips for better entries, settings etc..
Tenken-sen: green line
Kijun-sen: blue line
EMA: Purple
Rules:
Buy:
IF price crosses or bounce above Kijun-sen
THEN see if market has closed above EMA
IF Market has closed above EMA
THEN see if EMA is above Kijun-sen
IF EMA is above Kijun-sen
THEN buy and set trailing stop 5 pips below EMA
Sell:
IF price crosses or bounce below Kijun-sen
THEN see if market has closed below EMA
IF Market has closed below EMA
THEN see if EMA is below Kijun-sen
IF EMA is below Kijun-sen
THEN sell and set trailing stop 5 pips above EMA
Ease of Movement (EOM) This indicator gauges the magnitude of price and volume movement.
The indicator returns both positive and negative values where a
positive value means the market has moved up from yesterday's value
and a negative value means the market has moved down. A large positive
or large negative value indicates a large move in price and/or lighter
volume. A small positive or small negative value indicates a small move
in price and/or heavier volume.
A positive or negative numeric value. A positive value means the market
has moved up from yesterday's value, whereas, a negative value means the
market has moved down.
Staggered Exponential PullbacksIndicator Description: Staggered Exponential Pullbacks (Final)
Core Concept
This indicator is designed to dynamically track and visualize price pullbacks from a recent high. It serves as an intelligent alert system and a tool for visualizing potential support levels that follow a predefined, non-linear logic.
Instead of a fixed percentage interval, the indicator calculates the levels based on a fixed, exponentially increasing sequence of percentages. The distance between the levels increases as the price falls further. This models a strategy where larger price movements are tolerated as a pullback deepens before the next signal level is reached. The basis for this calculation is always the highest close of the last x candles.
Key Features
This indicator goes far beyond a simple calculation, offering a range of intelligent features for professional use:
Cascading, Fixed Levels: The levels are based on a fixed sequence of percentage distances (3.0%, 3.6%, 4.3%, etc.), where each new level is calculated from the previous level.
Persistent Support Levels ("Floors"): Once an alert level is breached, it transforms into a fixed support line ("floor"). This line will never move down, even if the market high subsequently drops.
Automatic Upward Adjustment: Established floors are automatically pulled upwards when the market shows new strength and makes higher highs. A once-reached -3% floor will therefore rise with the market.
Intelligent, Self-Cleaning Reset Logic: The indicator recognizes when a pullback sequence has ended and a new one has begun. "Ghost lines" from old, irrelevant price movements are automatically removed from the chart to ensure maximum clarity.
Cascade-Proof Alerts: Even during extremely fast sell-offs that break through multiple levels in a single candle, the indicator correctly captures every single level breach.
Customizable Visualization: All key parameters, such as the lookback period and the colors of the lines, can be easily adjusted in the settings.
Visual Elements on the Chart
The Orange Line (Highest Close): This is the reference line. It always shows the highest closing price within the defined lookback period and has a step-line shape.
The 'Floor' Lines (Default: Yellow): These are solid lines that indicate which percentage levels have already been breached in the current sequence. They function as established support levels.
The 'Next Due' Line (Default: Purple): This is a step-line that displays the next expected alert level. It moves dynamically with the calculation. As soon as the price crosses this line, an alert is triggered, and it transforms into a yellow "Floor" line.
Settings (Inputs)
Number of Candles (Lookback): Defines how many past candles are used to determine the highest closing price.
Displayed Alert Levels (Max 10): Determines the maximum number of levels the indicator will calculate and display.
Color of Floors: Allows you to freely choose the color for the solid, established support lines.
Color of Next Due Line: Allows you to freely choose the color for the next, untriggered alert line.
Setting Up Alerts (Important!)
Since the indicator uses dynamic alert messages, the alert must be set up as follows:
Add the indicator to the chart.
Click the clock icon ("Alert") in the top toolbar.
In the "Condition" field, select the name of this indicator: Staggered Exponential Pullbacks.
In the second dropdown menu, you must select the option "Any alert() function call".
Message: The message box can be left empty. The indicator automatically generates a detailed message (e.g., "Price Alert: Level 2 (3.6%) reached!").
Click "Create".
You only need one single alert to cover all 10 levels.
Important Disclaimer: Not Financial Advice
This indicator is purely a technical analysis tool for visualizing price movements. The displayed lines and triggered alerts do not constitute buy or sell recommendations and are not a form of financial or investment advice. They serve for informational and analytical purposes only.
Trading decisions based on the information from this indicator are made solely at your own risk and responsibility. The author and developer of this script assume no liability for any trading losses. Always conduct your own comprehensive analysis and, if necessary, consult a qualified financial advisor before making any trading decisions.
Trend dealing rangeHi all!
This indicator will help you find the current dealing range according to the trend. If the trend is bullish the indicator will look for a range between the latest low pivot to the latest high pivot. Vice versa in a bearish trend. The code uses my new library 'FibonacciRetracement' () that has the same code as my other indicator 'Fibonacci retracement' ().
It plots 5 lines from the low to the high and labels them 0 %, 25 %, 50 %, 75 % and 100 %. A trendline can be drawn between the two pivots (dashed and gray by default). Firstly you can define the pivot lengths used, this setting is in the 'Market structure' section but it also applies to the dealing range (it defaults to 5 (left) and 2 (right)). You can show prices if you want to (shown in parantheses, off by default). You can change the default labels position (from left) and the font size (12 by default and higher up it's 7 for market structure text). Lastly you can change the alert frequency (defaults to once per bar close) and the price that has to enter a zone for alert to be sent. 'Close' means that the closing price (or current price if you change the alert frequency to all or once per bar) has to be inside the zone and 'Wick' means that the entire candle needs to be inside the zone.
It's very useful for traders to find the current dealing range and this indicator will help you to do so.
So, this indicator will give you the dealing range and basic market structure through break of structures and change of characters.
If you have any input or suggestions on future features or bugs, don't hesitate to let me know!
Best of trading luck!
Mutanabby_AI | ATR+ | Trend-Following StrategyThis document presents the Mutanabby_AI | ATR+ Pine Script strategy, a systematic approach designed for trend identification and risk-managed position entry in financial markets. The strategy is engineered for long-only positions and integrates volatility-adjusted components to enhance signal robustness and trade management.
Strategic Design and Methodological Basis
The Mutanabby_AI | ATR+ strategy is constructed upon a foundation of established technical analysis principles, with a focus on objective signal generation and realistic trade execution.
Heikin Ashi for Trend Filtering: The core price data is processed via Heikin Ashi (HA) methodology to mitigate transient market noise and accentuate underlying trend direction. The script offers three distinct HA calculation modes, allowing for comparative analysis and validation:
Manual Calculation: Provides a transparent and deterministic computation of HA values.
ticker.heikinashi(): Utilizes TradingView's built-in function, employing confirmed historical bars to prevent repainting artifacts.
Regular Candles: Allows for direct comparison with standard OHLC price action.
This multi-methodological approach to trend smoothing is critical for robust signal generation.
Adaptive ATR Trailing Stop: A key component is the Average True Range (ATR)-based trailing stop. ATR serves as a dynamic measure of market volatility. The strategy incorporates user-defined parameters (
Key Value and ATR Period) to calibrate the sensitivity of this trailing stop, enabling adaptation to varying market volatility regimes. This mechanism is designed to provide a dynamic exit point, preserving capital and locking in gains as a trend progresses.
EMA Crossover for Signal Generation: Entry and exit signals are derived from the interaction between the Heikin Ashi derived price source and an Exponential Moving Average (EMA). A crossover event between these two components is utilized to objectively identify shifts in momentum, signaling potential long entry or exit points.
Rigorous Stop Loss Implementation: A critical feature for risk mitigation, the strategy includes an optional stop loss. This stop loss can be configured as a percentage or fixed point deviation from the entry price. Importantly, stop loss execution is based on real market prices, not the synthetic Heikin Ashi values. This design choice ensures that risk management is grounded in actual market liquidity and price levels, providing a more accurate representation of potential drawdowns during backtesting and live operation.
Backtesting Protocol: The strategy is configured for realistic backtesting, employing fill_orders_on_standard_ohlc=true to simulate order execution at standard OHLC prices. A configurable Date Filter is included to define specific historical periods for performance evaluation.
Data Visualization and Metrics: The script provides on-chart visual overlays for buy/sell signals, the ATR trailing stop, and the stop loss level. An integrated information table displays real-time strategy parameters, current position status, trend direction, and key price levels, facilitating immediate quantitative assessment.
Applicability
The Mutanabby_AI | ATR+ strategy is particularly suited for:
Cryptocurrency Markets: The inherent volatility of assets such as #Bitcoin and #Ethereum makes the ATR-based trailing stop a relevant tool for dynamic risk management.
Systematic Trend Following: Individuals employing systematic methodologies for trend capture will find the objective signal generation and rule-based execution aligned with their approach.
Pine Script Developers and Quants: The transparent code structure and emphasis on realistic backtesting provide a valuable framework for further analysis, modification, and integration into broader quantitative models.
Automated Trading Systems: The clear, deterministic entry and exit conditions facilitate integration into automated trading environments.
Implementation and Evaluation
To evaluate the Mutanabby_AI | ATR+ strategy, apply the script to your chosen chart on TradingView. Adjust the input parameters (Key Value, ATR Period, Heikin Ashi Method, Stop Loss Settings) to observe performance across various asset classes and timeframes. Comprehensive backtesting is recommended to assess the strategy's historical performance characteristics, including profitability, drawdown, and risk-adjusted returns.
I'd love to hear your thoughts, feedback, and any optimizations you discover! Drop a comment below, give it a like if you find it useful, and share your results.
Market Sessions By Zcointv/ScottfdxThis code has been writted By Zcointv/Scottfdx traders
This is a Market Volatility Box Breakout Strategy designed for intraday trading on 5-minute charts.
How it Works:
Volatility Box: The strategy defines a "volatility box" by capturing the price range (High and Low) around the New York market open.
The box begins one hour before the market open and ends 30 minutes after the market open.
The High and Low of this box are locked for the rest of the day.
Breakout Entry: A trade is opened only after this session period has ended.
Long: A 5-minute candle must close above the High of the box.
Short: A 5-minute candle must close below the Low of the box.
Risk Management:
1% Risk: Each trade risks a maximum of 1% of the total account equity. The position size is calculated dynamically based on this risk.
Stop Loss: The initial stop-loss is placed just outside the opposite side of the box.
1:1 Take Profit: The target is set at a 1:1 risk-to-reward ratio.
Partial Exit & Breakeven: When the take-profit target is hit, 50% of the position is closed. The stop-loss for the remaining 50% is then immediately moved to the entry price (breakeven).
Key Features:
The strategy is limited to one trade per day.
The indicator also has options to display configurable boxes for the Tokyo and London sessions.
The High and Low levels of the volatility box are plotted on the chart for visual reference.
TimeSeriesBenchmarkMeasuresLibrary "TimeSeriesBenchmarkMeasures"
Time Series Benchmark Metrics. \
Provides a comprehensive set of functions for benchmarking time series data, allowing you to evaluate the accuracy, stability, and risk characteristics of various models or strategies. The functions cover a wide range of statistical measures, including accuracy metrics (MAE, MSE, RMSE, NRMSE, MAPE, SMAPE), autocorrelation analysis (ACF, ADF), and risk measures (Theils Inequality, Sharpness, Resolution, Coverage, and Pinball).
___
Reference:
- github.com .
- medium.com .
- www.salesforce.com .
- towardsdatascience.com .
- github.com .
mae(actual, forecasts)
In statistics, mean absolute error (MAE) is a measure of errors between paired observations expressing the same phenomenon. Examples of Y versus X include comparisons of predicted versus observed, subsequent time versus initial time, and one technique of measurement versus an alternative technique of measurement.
Parameters:
actual (array) : List of actual values.
forecasts (array) : List of forecasts values.
Returns: - Mean Absolute Error (MAE).
___
Reference:
- en.wikipedia.org .
- The Orange Book of Machine Learning - Carl McBride Ellis .
mse(actual, forecasts)
The Mean Squared Error (MSE) is a measure of the quality of an estimator. As it is derived from the square of Euclidean distance, it is always a positive value that decreases as the error approaches zero.
Parameters:
actual (array) : List of actual values.
forecasts (array) : List of forecasts values.
Returns: - Mean Squared Error (MSE).
___
Reference:
- en.wikipedia.org .
rmse(targets, forecasts, order, offset)
Calculates the Root Mean Squared Error (RMSE) between target observations and forecasts. RMSE is a standard measure of the differences between values predicted by a model and the values actually observed.
Parameters:
targets (array) : List of target observations.
forecasts (array) : List of forecasts.
order (int) : Model order parameter that determines the starting position in the targets array, `default=0`.
offset (int) : Forecast offset related to target, `default=0`.
Returns: - RMSE value.
nmrse(targets, forecasts, order, offset)
Normalised Root Mean Squared Error.
Parameters:
targets (array) : List of target observations.
forecasts (array) : List of forecasts.
order (int) : Model order parameter that determines the starting position in the targets array, `default=0`.
offset (int) : Forecast offset related to target, `default=0`.
Returns: - NRMSE value.
rmse_interval(targets, forecasts)
Root Mean Squared Error for a set of interval windows. Computes RMSE by converting interval forecasts (with min/max bounds) into point forecasts using the mean of the interval bounds, then compares against actual target values.
Parameters:
targets (array) : List of target observations.
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - RMSE value for the combined interval list.
mape(targets, forecasts)
Mean Average Percentual Error.
Parameters:
targets (array) : List of target observations.
forecasts (array) : List of forecasts.
Returns: - MAPE value.
smape(targets, forecasts, mode)
Symmetric Mean Average Percentual Error. Calculates the Mean Absolute Percentage Error (MAPE) between actual targets and forecasts. MAPE is a common metric for evaluating forecast accuracy, expressed as a percentage, lower values indicate a better forecast accuracy.
Parameters:
targets (array) : List of target observations.
forecasts (array) : List of forecasts.
mode (int) : Type of method: default=0:`sum(abs(Fi-Ti)) / sum(Fi+Ti)` , 1:`mean(abs(Fi-Ti) / ((Fi + Ti) / 2))` , 2:`mean(abs(Fi-Ti) / (abs(Fi) + abs(Ti))) * 100`
Returns: - SMAPE value.
mape_interval(targets, forecasts)
Mean Average Percentual Error for a set of interval windows.
Parameters:
targets (array) : List of target observations.
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - MAPE value for the combined interval list.
acf(data, k)
Autocorrelation Function (ACF) for a time series at a specified lag.
Parameters:
data (array) : Sample data of the observations.
k (int) : The lag period for which to calculate the autocorrelation. Must be a non-negative integer.
Returns: - The autocorrelation value at the specified lag, ranging from -1 to 1.
___
The autocorrelation function measures the linear dependence between observations in a time series
at different time lags. It quantifies how well the series correlates with itself at different
time intervals, which is useful for identifying patterns, seasonality, and the appropriate
lag structure for time series models.
ACF values close to 1 indicate strong positive correlation, values close to -1 indicate
strong negative correlation, and values near 0 indicate no linear correlation.
___
Reference:
- statisticsbyjim.com
acf_multiple(data, k)
Autocorrelation function (ACF) for a time series at a set of specified lags.
Parameters:
data (array) : Sample data of the observations.
k (array) : List of lag periods for which to calculate the autocorrelation. Must be a non-negative integer.
Returns: - List of ACF values for provided lags.
___
The autocorrelation function measures the linear dependence between observations in a time series
at different time lags. It quantifies how well the series correlates with itself at different
time intervals, which is useful for identifying patterns, seasonality, and the appropriate
lag structure for time series models.
ACF values close to 1 indicate strong positive correlation, values close to -1 indicate
strong negative correlation, and values near 0 indicate no linear correlation.
___
Reference:
- statisticsbyjim.com
adfuller(data, n_lag, conf)
: Augmented Dickey-Fuller test for stationarity.
Parameters:
data (array) : Data series.
n_lag (int) : Maximum lag.
conf (string) : Confidence Probability level used to test for critical value, (`90%`, `95%`, `99%`).
Returns: - `adf` The test statistic.
- `crit` Critical value for the test statistic at the 10 % levels.
- `nobs` Number of observations used for the ADF regression and calculation of the critical values.
___
The Augmented Dickey-Fuller test is used to determine whether a time series is stationary
or contains a unit root (non-stationary). The null hypothesis is that the series has a unit root
(is non-stationary), while the alternative hypothesis is that the series is stationary.
A stationary time series has statistical properties that do not change over time, making it
suitable for many time series forecasting models. If the test statistic is less than the
critical value, we reject the null hypothesis and conclude the series is stationary.
___
Reference:
- www.jstor.org
- en.wikipedia.org
theils_inequality(targets, forecasts)
Calculates Theil's Inequality Coefficient, a measure of forecast accuracy that quantifies the relative difference between actual and predicted values.
Parameters:
targets (array) : List of target observations.
forecasts (array) : Matrix with list of forecasts, ordered column wise.
Returns: - Theil's Inequality Coefficient value, value closer to 0 is better.
___
Theil's Inequality Coefficient is calculated as: `sqrt(Sum((y_i - f_i)^2)) / (sqrt(Sum(y_i^2)) + sqrt(Sum(f_i^2)))`
where `y_i` represents actual values and `f_i` represents forecast values.
This metric ranges from 0 to infinity, with 0 indicating perfect forecast accuracy.
___
Reference:
- en.wikipedia.org
sharpness(forecasts)
The average width of the forecast intervals across all observations, representing the sharpness or precision of the predictive intervals.
Parameters:
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - Sharpness The sharpness level, which is the average width of all prediction intervals across the forecast horizon.
___
Sharpness is an important metric for evaluating forecast quality. It measures how narrow or wide the
prediction intervals are. Higher sharpness (narrower intervals) indicates greater precision in the
forecast intervals, while lower sharpness (wider intervals) suggests less precision.
The sharpness metric is calculated as the mean of the interval widths across all observations, where
each interval width is the difference between the upper and lower bounds of the prediction interval.
Note: This function assumes that the forecasts matrix has at least 2 columns, with the first column
representing the lower bounds and the second column representing the upper bounds of prediction intervals.
___
Reference:
- Hyndman, R. J., & Athanasopoulos, G. (2018). Forecasting: principles and practice. OTexts. otexts.com
resolution(forecasts)
Calculates the resolution of forecast intervals, measuring the average absolute difference between individual forecast interval widths and the overall sharpness measure.
Parameters:
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - The average absolute difference between individual forecast interval widths and the overall sharpness measure, representing the resolution of the forecasts.
___
Resolution is a key metric for evaluating forecast quality that measures the consistency of prediction
interval widths. It quantifies how much the individual forecast intervals vary from the average interval
width (sharpness). High resolution indicates that the forecast intervals are relatively consistent
across observations, while low resolution suggests significant variation in interval widths.
The resolution is calculated as the mean absolute deviation of individual interval widths from the
overall sharpness value. This provides insight into the uniformity of the forecast uncertainty
estimates across the forecast horizon.
Note: This function requires the forecasts matrix to have at least 2 columns (min, max) representing
the lower and upper bounds of prediction intervals.
___
Reference:
- (sites.stat.washington.edu)
- (www.jstor.org)
coverage(targets, forecasts)
Calculates the coverage probability, which is the percentage of target values that fall within the corresponding forecasted prediction intervals.
Parameters:
targets (array) : List of target values.
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - Percent of target values that fall within their corresponding forecast intervals, expressed as a decimal value between 0 and 1 (or 0% and 100%).
___
Coverage probability is a crucial metric for evaluating the reliability of prediction intervals.
It measures how well the forecast intervals capture the actual observed values. An ideal forecast
should have a coverage probability close to the nominal confidence level (e.g., 90%, 95%, or 99%).
For example, if a 95% prediction interval is used, we expect approximately 95% of the actual
target values to fall within those intervals. If the coverage is significantly lower than the
nominal level, the intervals may be too narrow; if it's significantly higher, the intervals may
be too wide.
Note: This function requires the targets array and forecasts matrix to have the same number of
observations, and the forecasts matrix must have at least 2 columns (min, max) representing
the lower and upper bounds of prediction intervals.
___
Reference:
- (www.jstor.org)
pinball(tau, target, forecast)
Pinball loss function, measures the asymmetric loss for quantile forecasts.
Parameters:
tau (float) : The quantile level (between 0 and 1), where 0.5 represents the median.
target (float) : The actual observed value to compare against.
forecast (float) : The forecasted value.
Returns: - The Pinball loss value, which quantifies the distance between the forecast and target relative to the specified quantile level.
___
The Pinball loss function is specifically designed for evaluating quantile forecasts. It is
asymmetric, meaning it penalizes underestimates and overestimates differently depending on the
quantile level being evaluated.
For a given quantile τ, the loss function is defined as:
- If target >= forecast: (target - forecast) * τ
- If target < forecast: (forecast - target) * (1 - τ)
This loss function is commonly used in quantile regression and probabilistic forecasting
to evaluate how well forecasts capture specific quantiles of the target distribution.
___
Reference:
- (www.otexts.com)
pinball_mean(tau, targets, forecasts)
Calculates the mean pinball loss for quantile regression.
Parameters:
tau (float) : The quantile level (between 0 and 1), where 0.5 represents the median.
targets (array) : The actual observed values to compare against.
forecasts (matrix) : The forecasted values in matrix format with at least 2 columns (min, max).
Returns: - The mean pinball loss value across all observations.
___
The pinball_mean() function computes the average Pinball loss across multiple observations,
making it suitable for evaluating overall forecast performance in quantile regression tasks.
This function leverages the asymmetric Pinball loss function to evaluate how well forecasts
capture specific quantiles of the target distribution. The choice of which column from the
forecasts matrix to use depends on the quantile level:
- For τ ≤ 0.5: Uses the first column (min) of forecasts
- For τ > 0.5: Uses the second column (max) of forecasts
This loss function is commonly used in quantile regression and probabilistic forecasting
to evaluate how well forecasts capture specific quantiles of the target distribution.
___
Reference:
- (www.otexts.com)
[Pandora][Swarm] Rapid Exponential Moving AverageENVISIONING POSSIBILITY
What is the theoretical pinnacle of possibility? The current state of algorithmic affairs falls far short of my aspirations for achievable feasibility. I'm lifting the lid off of Pandora's box once again, very publicly this time, as a brute force challenge to conventional 'wisdom'. The unfolding series of time mandates a transcendental systemic alteration...
THE MOVING AVERAGE ZOO:
The realm of digital signal processing for trading is filled with familiar antiquated filtering tools. Two families of filtration, being 'infinite impulse response' (EMA, RMA, etc.) and 'finite impulse response' (WMA, SMA, etc.), are prevalently employed without question. These filter types are the mules and donkeys of data analysis, broadly accepted for use in finance.
At first glance, they appear sufficient for most tasks, offering a basic straightforward way to reduce noise and highlight trends. Yet, beneath their simplistic facade lies a constellation of limitations and impediments, each having its own finicky quirks. Upon closer inspection, identifiable drawbacks render them far from ideal for many real-world applications in today's volatile markets.
KNOWN FUNDAMENTAL FLAWS:
Despite commonplace moving average (MA) popularity, these conventional filters suffer from an assortment of fundamental flaws. Most of them don't genuinely address core challenges of how to preserve the true dynamics of a signal while suppressing noise and retaining cutoff frequency compliance. Their simple cookie cutter structures make them ill-suited in actuality for dynamic market environments. In reality, they often trade one problem for another dilemma, forsaking analytics to choose between distortion and delay.
A deeper seeded issue remains within frequency compliance, how adequately a filter respects (or disrespects) the underlying signal’s spectral properties according to it's assigned periodic parameter. Traditional MAs habitually distort phase relationships, causing delayed reactions with surplus lag or exaggerations with excessive undershoot/overshoot. For applications requiring timely resilience, such as algorithmic trading, these shortcomings are often functionally unacceptable. What’s needed is vigorous filters that can more accurately retain signal behaviors while minimizing lag without sacrificing smoothness and uniformity. Until then, the public MA zoo remains as a collection of corny compromises, rather than a favorable toolbelt of solutions.
P.S.: In PSv7+, in my opinion, many of these geriatric MAs deserve no future with ease of access for the naive, simply not knowing these filters are most likely creating bigger problems than solving any.
R.E.M.A.
What is this? I prefer to think of it as the "radical EMA", definitely along my lines of a retire everything morte algorithm. This isn't your run of the mill average from the petting zoo. I would categorize it as a paradigm shifting rampant economic masochistic annihilator, sufficiently good enough to begin ruthlessly executing moving averages left and right. Um, yeah... that kind of moving average destructor as you may soon recognize with a few 'Filters+' settings adjustments, realizing ordinary EMA has been doing us an injustice all this time.
Does it possess the capability to relentlessly exterminate most averaging filters in existence? Well, it's about time we find out, by uncaging it on the loose into the greater economic wilderness. Only then can we truly find out if it is indeed a radical exponential market accelerant whose time has come. If it is, then it may eventually become a reality erasing monolithic anomaly destined for greatness, ultimately changing the entire landscape of trading in perpetuity.
UNLEASHING NEXT-GEN:
This lone next generation exoweapon algorithm is intended to initiate the transformative beginning stages of mass filtration deprecation. However, it won't be the only one, just the first arrival of it's alien kind from me. Welcome to notion #1 of my future filtration frontier, on this episode of the algorithmic twilight zone. Where reality takes a twisting turn one dimension beyond practical logic, after persistent models of mindset disintegrate into insignificance, followed by illusory perception confronted into cognitive dissonance.
An evolutionary path to genuine advancement resides outside the prison of preconceptions, manifesting only after divergence from persistent binding restrictions of dogmatic doctrines. Such a genesis in transformative thinking will catalyze unbounded cognitive potential, plowing the way for the cultivation of total redesigns of thought. Futuristic innovative breakthroughs demand the surrender of legacy and outmoded understandings.
Now that the world's largest assembly of investors has been ensembled, there are additional tasks left to perform. I'm compelled to deploy this mathematical-weapon of mass financial creation into it's rightful destined hands, to "WE THE PEOPLE" of TV.
SCRIPT INTENTION:
Deprecate anything and everything as any non-commercial member sees desirably fit. This includes your existing code formulations already in working functional modes of operation AND/OR future projects in the works. Swapping is nearly as simple as copying and pasting with meager modifications, after you have identified comparable likeness in this indicators settings with a visual assessment. Results may become eye opening, but only if you dare to look and test.
Where you may suspect a ta.filter() is lacking sufficient luster or may be flat out majorly deficient, employing rema, drema, trema, or qrema configurations may be a more suitable replacement. That's up to you to discern. My code satire already identifies likely bottom of the barrel suspects that either belong in the extinction record or have already been marked for deprecation. They are ordered more towards the bottom by rank where they belong. SuperSmoother is a masterpiece here to stay, being my original go-to reference filter. Everything you see here is already deprecated, including REMA...
REMA CHARACTERISTICS
- VERY low lag
- No overshoot
- Frequency compliant
- Proper initialization at bar_index==0
- Period parameter accepts poitive floating point numerics (AND integers!)
- Infinite impulse response (IIR) filter
- Compact code footprint
- Minimized computational overhead
