Helme-Nikias Weighted Burg AR-SE Extra. of Price [Loxx]Helme-Nikias Weighted Burg AR-SE Extra. of Price is an indicator that uses an autoregressive spectral estimation called the Weighted Burg Algorithm, but unlike the usual WB algo, this one uses Helme-Nikias weighting. This method is commonly used in speech modeling and speech prediction engines. This is a linear method of forecasting data. You'll notice that this method uses a different weighting calculation vs Weighted Burg method. This new weighting is the following:
w = math.pow(array.get(x, i - 1), 2), the squared lag of the source parameter
and
w += math.pow(array.get(x, i), 2), the sum of the squared source parameter
This take place of the rectangular, hamming and parabolic weighting used in the Weighted Burg method
Also, this method includes Levinson–Durbin algorithm. as was already discussed previously in the following indicator:
Levinson-Durbin Autocorrelation Extrapolation of Price
What is Helme-Nikias Weighted Burg Autoregressive Spectral Estimate Extrapolation of price?
In this paper a new stable modification of the weighted Burg technique for autoregressive (AR) spectral estimation is introduced based on data-adaptive weights that are proportional to the common power of the forward and backward AR process realizations. It is shown that AR spectra of short length sinusoidal signals generated by the new approach do not exhibit phase dependence or line-splitting. Further, it is demonstrated that improvements in resolution may be so obtained relative to other weighted Burg algorithms. The method suggested here is shown to resolve two closely-spaced peaks of dynamic range 24 dB whereas the modified Burg schemes employing rectangular, Hamming or "optimum" parabolic windows fail.
Data inputs
Source Settings: -Loxx's Expanded Source Types. You typically use "open" since open has already closed on the current active bar
LastBar - bar where to start the prediction
PastBars - how many bars back to model
LPOrder - order of linear prediction model; 0 to 1
FutBars - how many bars you want to forward predict
Things to know
Normally, a simple moving average is calculated on source data. I've expanded this to 38 different averaging methods using Loxx's Moving Avreages.
This indicator repaints
Further reading
A high-resolution modified Burg algorithm for spectral estimation
Related Indicators
Levinson-Durbin Autocorrelation Extrapolation of Price
Weighted Burg AR Spectral Estimate Extrapolation of Price
Prediction
Weighted Burg AR Spectral Estimate Extrapolation of Price [Loxx]Weighted Burg AR Spectral Estimate Extrapolation of Price is an indicator that uses an autoregressive spectral estimation called the Weighted Burg Algorithm. This method is commonly used in speech modeling and speech prediction engines. This method also includes Levinson–Durbin algorithm. As was already discussed previously in the following indicator:
Levinson-Durbin Autocorrelation Extrapolation of Price
What is Levinson recursion or Levinson–Durbin recursion?
In many applications, the duration of an uninterrupted measurement of a time series is limited. However, it is often possible to obtain several separate segments of data. The estimation of an autoregressive model from this type of data is discussed. A straightforward approach is to take the average of models estimated from each segment separately. In this way, the variance of the estimated parameters is reduced. However, averaging does not reduce the bias in the estimate. With the Burg algorithm for segments, both the variance and the bias in the estimated parameters are reduced by fitting a single model to all segments simultaneously. As a result, the model estimated with the Burg algorithm for segments is more accurate than models obtained with averaging. The new weighted Burg algorithm for segments allows combining segments of different amplitudes.
The Burg algorithm estimates the AR parameters by determining reflection coefficients that minimize the sum of for-ward and backward residuals. The extension of the algorithm to segments is that the reflection coefficients are estimated by minimizing the sum of forward and backward residuals of all segments taken together. This means a single model is fitted to all segments in one time. This concept is also used for prediction error methods in system identification, where the input to the system is known, like in ARX modeling
Data inputs
Source Settings: -Loxx's Expanded Source Types. You typically use "open" since open has already closed on the current active bar
LastBar - bar where to start the prediction
PastBars - how many bars back to model
LPOrder - order of linear prediction model; 0 to 1
FutBars - how many bars you want to forward predict
BurgWin - weighing function index, rectangular, hamming, or parabolic
Things to know
Normally, a simple moving average is calculated on source data. I've expanded this to 38 different averaging methods using Loxx's Moving Avreages.
This indicator repaints
Included
Bar color muting
Further reading
Performance of the weighted burg methods of ar spectral estimation for pitch-synchronous analysis of voiced speech
The Burg algorithm for segments
Techniques for the Enhancement of Linear Predictive Speech Coding in Adverse Conditions
Related Indicators
Levinson-Durbin Autocorrelation Extrapolation of Price [Loxx]Levinson-Durbin Autocorrelation Extrapolation of Price is an indicator that uses the Levinson recursion or Levinson–Durbin recursion algorithm to predict price moves. This method is commonly used in speech modeling and prediction engines.
What is Levinson recursion or Levinson–Durbin recursion?
Is a linear algebra prediction analysis that is performed once per bar using the autocorrelation method with a within a specified asymmetric window. The autocorrelation coefficients of the window are computed and converted to LP coefficients using the Levinson algorithm. The LP coefficients are then transformed to line spectrum pairs for quantization and interpolation. The interpolated quantized and unquantized filters are converted back to the LP filter coefficients to construct the synthesis and weighting filters for each bar.
Data inputs
Source Settings: -Loxx's Expanded Source Types. You typically use "open" since open has already closed on the current active bar
LastBar - bar where to start the prediction
PastBars - how many bars back to model
LPOrder - order of linear prediction model; 0 to 1
FutBars - how many bars you want to forward predict
Things to know
Normally, a simple moving average is caculated on source data. I've expanded this to 38 different averaging methods using Loxx's Moving Avreages.
This indicator repaints
Included
Bar color muting
Further reading
Implementing the Levinson-Durbin Algorithm on the StarCore™ SC140/SC1400 Cores
LevinsonDurbin_G729 Algorithm, Calculates LP coefficients from the autocorrelation coefficients. Intel® Integrated Performance Primitives for Intel® Architecture Reference Manual
Fourier Extrapolation of Variety Moving Averages [Loxx]Fourier Extrapolation of Variety Moving Averages is a Fourier Extrapolation (forecasting) indicator that has for inputs 38 different types of moving averages along with 33 different types of sources for those moving averages. This is a forecasting indicator of the selected moving average of the selected price of the underlying ticker. This indicator will repaint, so past signals are only as valid as the current bar. This indicator allows for up to 1500 bars between past bars and future projection bars. If the indicator won't load on your chart. check the error message for details on how to fix that, but you must ensure that past bars + futures bars is equal to or less than 1500.
Fourier Extrapolation using the Quinn-Fernandes algorithm is one of several (5-10) methods of signals forecasting that I'l be demonstrating in Pine Script.
What is Fourier Extrapolation?
This indicator uses 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.
This indicator uses the Quinn-Fernandes algorithm to find the 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
The indicator has the following input parameters:
src - input source
npast - number of past bars, to which trigonometric series is fitted;
Nfut - number of predicted future bars;
nharm - total number of harmonics in model;
frqtol - tolerance of frequency calculations.
Included:
Loxx's Expanded Source Types
Loxx's Moving Averages
Other indicators using this same method
Fourier Extrapolator of Variety RSI w/ Bollinger Bands
Fourier Extrapolator of Price w/ Projection Forecast
Fourier Extrapolator of Price
Loxx's Moving Averages: Detailed explanation of moving averages inside this indicator
Loxx's Expanded Source Types: Detailed explanation of source types used in this indicator
Reverse Stoch [BApig Gift] - on PanelMssive credit to Motgench, Balipour and Wugamlo for this script. This script is all of their good work.
It is basically just the non-on chart version which I've slightly tweaked off their script. This can be useful to reduce the clutter on the chart itself. Releasing it in the hope that it can be useful for the community
Enjoy!
Ichimoku PeeksThis indicator uses the Ichimoku Tenkan / Kijun trend line formulas to predict what those values will be in the future if current price action does not violate the period highs and lows.
Because of the way Ichimoku formulates the trend, it contains (but does not visualize) this predictive information in a way that moving averages do not.
Sharp chart readers can infer upcoming changes by counting back candles, but the process can be automated, as I've shown here.
This description does not seem to be editable so implementation details and usage will be covered in code commentary.
Current bar predicted volumeDrag this indicator in the same panel with the volume in the object tree, then right click on the scale bar and set "merge all scale into one" for a correct visualization.
This indicator multiply the current traded volume of a candle with the total time of that candle. This offer a prediction of where, in case the volumes would keep trading at a comparable magnitude, the volume bar would close when the candle will close.
The predicted volume is indicated with a blue short line above the current volume bar, and updates in real time.
I find this indicator extremely useful to offer at a glance an idea of an ascending or descending volume pattern, that can serve as confirmation for a reversal or breakout for example.
Very suitable for short time frames, where decisions have to be taken fast.
Enjoy,
Luca.
BB Order BlocksUsing the Bollinger Band to mark areas of Support and Resistance
The scrip finds the highest and lowest levels of the bands to mark up futures areas of interest.
If the High/Lows are being broken on the Bollinger band, or if the look back range has expired without finding new levels, the script will stop plotting them until new levels are found
I have found many combinations which work well
Changing the band length to to levels 20,50,100 or 200 seem to give interesting results
Aswell as this changing the standard deviation to 3 instead of 2 marks up key levels.
The look back range seems to show better levels on 50,100 and 200
Let me know any changes or updates you think you could make an impact , this was just a quick basic script I wanted to share.
EMA PredictionThis script predicts future EMA values assuming that the price remains as configured (-50% to +50%).
Ehlers Optimum Predictor [CC]The Optimum Predictor was created by John Ehlers (Rocket Science For Traders pgs 209-210) and this indicator does a pretty good job of predicting major market moves. When the blue line crosses over the red line then this indicator is predicting an upcoming uptrend and when the blue line crosses under the red line then it is predicting an upcoming downtrend. Ehlers recommends using this indicator with an entire trading system to filter out any bad signals but most of the signals it gives are pretty accurate. He uses advanced digital signal processing to predict the future prices and uses it in an ema formula for the calculation. There are several ways to interpret this indicator: you can look for crossovers, you can also look for when the indicator goes above 0 for a general uptrend or below 0 for a general downtrend.
Let me know if there are any other scripts you would like to see me publish!
MACD Trendprediction Strategy V1A trend following indicator based on the MACD and EMA. In this case, signals are not generated by crossing the signal lines as with the MACD, but as soon as the distance between the signal lines increases or decreases. A profit factor of 1.6-3.5 is achieved.
Ein Trendfolge-Indikator, auf der Basis des MACD und EMA. Dabei werden Signale nicht wie bei dem MACD per Kreuzung der Signallinien generiert, sondern sobald ein der Abstand der Signallinien zu oder abnimmt.
Ehlers Voss Predictive Filter [CC]The Voss Predictive Filter was created by John Ehlers (Stocks and Commodities August 2019) and this is a unique indicator in that it tries to predict future price action. I have color coded the middle line to show buy and sell signals so buy when the line turns green and sell when it turns red.
Let me know if there are any other indicators you want me to publish!
Multi Moving Average with ForecastThis script allows to use 5 different MAs with prediction of the next five periods.
Similarity Search, Karobein and Seasonal Random IndexSimilarity Search, Karobein oscillator (KO) and Seasonal Random Index (SRI)
Description:
This indicator uses dynamic capabilities of Pinescript version 4 coupled with Seasonal Random Index (SRI) and Karobein Oscillator (KO). SRI (green/red areas) is employed to detect trends and KO (black curce) is used to find historical similarities to predict the next bar's direction. The midline arrows are the predictions produced by the similarity search algorithm.
Machine Learning: kNN-based Strategy (update)kNN-based Strategy (FX and Crypto)
Description:
This update to the popular kNN-based strategy features:
improvements in the business logic,
an adjustible k value for the kNN model,
one more feature (MOM),
a streamlined signal filter and
some other minor fixes.
Now this script works in all timeframes !
I intentionally decided to publish this script separately
in order for the users to see the differences.
Machine Learning: kNN-based StrategykNN-based Strategy (FX and Crypto)
Description:
This strategy uses a classic machine learning algorithm - k Nearest Neighbours (kNN) - to let you find a prediction for the next (tomorrow's, next month's, etc.) market move. Being an unsupervised machine learning algorithm, kNN is one of the most simple learning algorithms.
To do a prediction of the next market move, the kNN algorithm uses the historic data, collected in 3 arrays - feature1, feature2 and directions, - and finds the k-nearest
neighbours of the current indicator(s) values.
The two dimensional kNN algorithm just has a look on what has happened in the past when the two indicators had a similar level. It then looks at the k nearest neighbours,
sees their state and thus classifies the current point.
The kNN algorithm offers a framework to test all kinds of indicators easily to see if they have got any *predictive value*. One can easily add cog, wpr and others.
Note: TradingViews's playback feature helps to see this strategy in action.
Warning: Signals ARE repainting.
Style tags: Trend Following, Trend Analysis
Asset class: Equities, Futures, ETFs, Currencies and Commodities
Dataset: FX Minutes/Hours+++/Days
Momentum adjusted Moving Average by DGTA brand new Moving Average , calculated using Momentum, Acceleration and Probability (Psychological Effect).
Momentum adjusted Moving Average(MaMA) is an indicator that measures Price Action by taking into consideration not only Price movements but also its Momentum, Acceleration and Probability. MaMA, provides faster responses comparing to the regular Moving Average
Here is the math of the MaMA idea
Momentum measures change in price over a specified time period
momentum = source – source(length)
where,
source, indicates current bar’s price value
source(length), indicates historical price value of length bars earlier
Lets play with this formula and rewrite it by moving source(length) to other side of the equation
source = source(length) + momentum
to avoid confusion let’s call the source that we aim to predict as adjustedSource
adjustedSource = source(length) + momentum
looks nice the next value of source simply can be calculated by summing of historical value of the source value and value of the momentum. I wish it was so easy, the formula holds true only when the momentum is conserved/constant/steady but momentum move up or down with the price fluctuations (accelerating or decelerating)
Let’s add acceleration effects on our formula, where acceleration is change in momentum for a given length. Then the formula will become as (skipped proof part of acceleration effects, you may google for further details)
adjustedSource = source(length) + momentum + 1/2 * acceleration
here again the formula holds true when the acceleration is constant and once again it is not the case for trading, acceleration also changes with the price fluctuations
Then, how we can benefit from all of this, it has value yet requires additional approaches for better outcome
Let’s simulate behaviour with some predictive approach such as using probability (also known as psychological effect ), where probability is a measure for calculating the chances or the possibilities of the occurrence of a random event. As stated earlier above momentum and acceleration are changing with the price fluctuations, by using the probability approach we can add a predictive skill to determine the likelihood of momentum and acceleration changes (remember it is a predictive approach). With this approach, our equations can be expresses as follows
adjustedSource = source(length) + momentum * probability
adjustedSource = source(length) + ( momentum + 1/2 * acceleration ) * probability , with acceleration effect
Finally, we plot MaMA with the new predicted source adjustedSource, applying acceleration effect is made settable by the used from the dialog box, default value is true.
What to look for:
• Trend Identification
• Support and Resistance
• Price Crossovers
Recommended settings are applied as default settings, if you wish to change the length of the MaMA then you should also adjust length of Momentum (and/or Probability). For example for faster moving average such as 21 period it would be suggested to set momentum length to 13
Alternative usage , set moving average length to 1 and keep rest lengths with default values, it will produce a predictive price line based on momentum and probability. Experience acceleration factor by enabling and disabling it
Conclusion
MaMA provide an added level of confidence to a trading strategy and yet it is important to always be aware that it implements a predictive approach in a chaotic market use with caution just like with any indicator
Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
Disclaimer : The script is for informational and educational purposes only. Use of the script does not constitutes professional and/or financial advice. You alone the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
Elliott Wave Oscillator Signals by DGTElliott Wave Principle , developed by Ralph Nelson Elliott, proposes that the seemingly chaotic behaviour of the different financial markets isn’t actually chaotic. In fact the markets moves in predictable, repetitive cycles or waves and can be measured and forecast using Fibonacci numbers. These waves are a result of influence on investors from outside sources primarily the current psychology of the masses at that given time. Elliott wave predicts that the prices of the a traded currency pair will evolve in waves: five impulsive waves and three corrective waves. Impulsive waves give the main direction of the market expansion and the corrective waves are in the opposite direction (corrective wave occurrences and combination corrective wave occurrences are much higher comparing to impulsive waves)
The Elliott Wave Oscillator (EWO) helps identifying where you are in the 5-3 Elliott Waves, mainly the highest/lowest values of the oscillator might indicate a potential bullish/bearish Wave 3. Mathematically expressed, EWO is the difference between a 5-period and 35-period moving average based on the close. In this study instead 35-period, Fibonacci number 34 is implemented for the slow moving average and formula becomes ewo = ema(source, 5) - ema(source, 34)
The application of the Elliott Wave theory in real time trading gets difficult because the charts look messy. This study (EWO-S) simplifies the visualization of EWO and plots labels on probable reversals/corrections. The good part is that all plotting’s are performed on the top of the price chart including a histogram (optional and supported on higher timeframes). Additionally optional Keltner Channels Cloud added to help confirming the price actions.
What to look for:
Plotted labels can be used to follow the Elliott Wave occurrences and most importantly they can be considered as signals for possible trade setup opportunities. Elliott Wave Rules and Fibonacci Retracement/Extensions are suggested to confirm the patters provided by the EWO-S
Trading success is all about following your trading strategy and the indicators should fit within your trading strategy, and not to be traded upon solely
Disclaimer : The script is for informational and educational purposes only. Use of the script does not constitutes professional and/or financial advice. You alone the sole responsibility of evaluating the script output and risks associated with the use of the script. In exchange for using the script, you agree not to hold dgtrd TradingView user liable for any possible claim for damages arising from any decision you make based on use of the script
Squeeze Momentum Indicator [LazyBear] vX by DGTModified version of Squeeze Momentum Indicator visualizing on Price Chart
author: LazyBear, modified by KıvançÖZBİLGİÇ
[RS]predict player directionExperimental:
adapted from classical game movement prediction algo and how a crude recursive error classifier can be applied..
Magic 8-Ball [QuantNomad]Sometimes get tired and what to create something fun and useless )
Here I developed a magic 8-ball. You can apply it to the chart, and it randomly will show you a prediction unique for your symbol/candle time.
Please don't take this prediction seriously; there is 0 rationale behind it. However, I believe it can outperform some traders here on TradginView =)
So it will show you one of the following messages:
Buy
Strong Buy
HODL
Sell
Strong Sell
Ask again later
Better not tell you now
Neutral
Cannot predict now
Very doubtful
Running Average [EXPERIMENT]Predicting the future has nothing to do with trading, understanding your indicator and knowing how to use it does.
Probably not the most efficient code, oh well..
Let me know if it's useful :)
Grand Trend Forecasting - A Simple And Original Approach Today we'll link time series forecasting with signal processing in order to provide an original and funny trend forecasting method, the post share lot of information, if you just want to see how to use the indicator then go to the section "Using The Indicator".
Time series forecasting is an area dealing with the prediction of future values of a series by using a specific model, the model is the main tool that is used for forecasting, and is often an expression based on a set of predictor terms and parameters, for example the linear regression (model) is a 1st order polynomial (expression) using 2 parameters and a predictor variable ax + b . Today we won't be using the linear regression nor the LSMA.
In time series analysis we can describe the time series with a model, in the case of the closing price a simple model could be as follows :
Price = Trend + Cycles + Noise
The variables of the model are the components, such model is additive since we add the component with each others, we should be familiar with each components of the model, the trend represent a simple long term variation of high amplitude, the cycles are periodic fluctuations centered around 0 of varying period and amplitude, the noise component represent shorter term irregular variations with mean 0.
As a trader we are mostly interested by the cycles and the trend, altho the cycles are relatively more technical to trade and can constitute parasitic fluctuations (think about retracements in a trend affecting your trend indicator, causing potential false signals).
If you are curious, in signal processing combining components has a specific name, "synthesis" , here we are dealing with additive synthesis, other type of synthesis are more specific to audio processing and are relatively more complex, but could be used in technical analysis.
So what to do with our components ? If we want to trade the trend, we should estimate right ? Estimating the trend component involve removing the cycle and noise component from the price, if you have read stuff about filters you should know where i'am going, yep, we should use filters, in the case of keeping the trend we can use a simple moving average of relatively high period, and here we go.
However the lag problem, which is recurrent, come back again, we end up with information easier to interpret (here the trend, which is a simple fluctuation such as a line or other smooth curve) at the cost of decision timing, that is unfortunate but as i said the information, here the moving average output, is relatively simple, and could be easily forecasted right ? If you plot a moving average of high period it would be easier for you to forecast its future values. And thats what we aim to do today, provide an estimate of the trend that should be easy to forecast, and should fit to the price relatively well in order to produce forecast that could determine the position of future closing prices observations.
Estimating And Forecasting The Trend
The parameter of the indicator dealing with the estimation of the trend is length , with higher values of length attenuating the cycle and noise component in the price, note however that high values of length can return a really long term trend unlike a simple moving average, so a small value of length, 14 for example can still produce relatively correct estimate of trend.
here length = 14.
The rough estimate of the trend is t in the code, and is an IIR filter, that is, it is based on recursion. Now i'll pass on the filter design explanation but in short, weights are constants, with higher weights allocated to the previous length values of the filter, you can see on the code that the first part of t is similar to an exponential moving average with :
t(n) = 0.9t(n-length) + 0.1*Price
However while the EMA only use the precedent value for the recursion, here we use the precedent length value, this would just output a noisy and really slow output, therefore in order to create a better fit we add : 0.9*(t(n-length) - t(n-2length)) , and this create the rough trend estimate that you can see in blue. On the parameters, 0.9 is used since it gives the best estimate in my opinion, higher values would create more periodic output and lower values would just create a rougher output.
The blue line still contain a residual of the cycle/noise component, this is why it is smoothed with a simple moving average of period length. If you are curious, a filter estimating the trend but still containing noisy fluctuations is called "Notch" filter, such filter would depending on the cutoff remove/attenuate mid term cyclic fluctuations while preserving the trend and the noise, its the opposite of a bandpass filter.
In order to forecast values, we simply sum our trend estimate with the trend estimate change with period equal to the forecasting horizon period, this is a really really simple forecasting method, but it can produce decent results, it can also allows the forecast to start from the last point of the trend estimate.
Using The Indicator
We explained the length parameter in the precedent section, src is the input series which the trend is estimated, forecast determine the forecasting horizon, recommend values for forecast should be equal to length, length/2 or length*2, altho i strongly recommend length.
here length and forecast are both equal to 14 .
The corrective parameter affect the trend estimate, it reduce the overshoot and can led to a curve that might fit better to the price.
The indicator with the non corrective version above, and the corrective one below.
The source parameter determine the source of the forecast, when "Noisy" is selected the source is the blue line, and produce a noisy forecast, when "Smooth" is selected the source is the moving average of t , this create a smoother forecast.
The width interval control...the width of the intervals, they can be seen above and under the forecast plot, they are constructed by adding/subtracting the forecast with the forecast moving average absolute error with respect to the price. Prediction intervals are often associated with a probability (determining the probability of future values being between the interval) here we can't determine such probability with accuracy, this require (i think) an analysis of the forecasting distribution as well as assumptions on the distribution of the forecasting error.
Finally it is possible to see historical forecasts, that is, forecasts previously generated by checking the "Show Historical Forecasts" option.
Examples
Good forecasts mostly occur when the price is close to the trend estimate, this include the following highlighted periods on AMD 15TF with default settings :
We can see the same thing at the end of EURUSD :
However we can't always obtain suitable fits, here it is isn't sufficient on BTCUSD :
We can see wide intervals, we could change length or use the corrective option to get better results, another option is to use a log scale.
We will end the examples with the log SPX, who posses a linear trend, so for example a linear model such as a linear regression would be really adapted, lets see how the indicator perform :
Not a great fit, we could try to use an higher length value and use "Smooth" :
Most recent fits are quite decent.
Conclusions
A forecasting indicator has been presented in this post. The indicator use an original approach toward estimating the trend component in the closing price. Of course i should have given statistics related to the forecasting error, however such analysis is worth doing with better methods and in more advanced environment allowing for optimization.
But we have learned some stuff related to signal processing as well as time series analysis, seeing a time series as the sum of various components is really helpful when it comes to make sense of chaotic and noisy series and is a basic topic in time series analysis.
You can see that in this new year i work harder on the visual of my indicators without trying to fall in the label addict trap, something that i wasn't really doing before, let me know what do you think of it.
Thanks for reading !
