Trading Strategies Based On Digital Filters - page 138
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Good article was published -
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Practical evaluation of the adaptive market following method
The trading strategy presented in this article was first described by Vladimir Kravchuk in the "Currency speculator" magazine in 2001 - 2002. The system is based on the use of digital filters and spectral estimation of discrete time series.
A live chart of quote changes may have an arbitrary form. In mathematics, such functions are called non-analytic. The famous Fourier theorem implies that any function on a finite time interval can be represented as an infinite sum of sinusoidal functions. Consequently, any time signals can be uniquely represented by frequency functions, which are called their frequency spectra.
For non-random signals, the transition from the time-domain to frequency-domain representation (i.e., calculation of the frequency spectrum) is performed using the Fourier transform. Random processes are represented by the process' power spectral density (PSD), which is a Fourier transform not of the random process itself, but that of its autocorrelation function.
Digital Filters' Trading Systems
The beginning
3.1 T3 Digitals indicators are on this post. These are using t3 smoothing, they are mtf, and have alerts, 1 has arrows, if you prefer no smoothing just turn t3 period to 1 or zero.
3.2. T3 Dtm indicator is on this post. This is t3 dtm actually stlm and ftlm together they have mtf with alerts on slope change.
After
1.1. T3Digital_Martingale EA (for MT4) is on this post and the trading results with the settings uploaded on this post. It is the first version of Digital Martingale: this EA is using some of the indicators posted a couple weeks ago, with the exception of normalized t3 rbci. The rbci was optimized so it being used in this Ea as a long term trend watcher, but it seems to work equally as well in hourly timeframe. This Ea version is using Satl,Fatl,Stlm, and the before mentioned rbci all indicators you have the ability to change timeframes as desired.
The Articles
CodeBase
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New good indicators was coded for Metatrader 5 (this indicator is very well-known for MT4 for example) -
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Digital filters - separate - indicator for MetaTrader 5
Supported types are:
Forum on trading, automated trading systems and testing trading strategies
Trading Strategies Based On Digital Filters
Sergey Golubev, 2017.03.28 15:46
PRACTICAL IMPLEMENTATION OF DIGITAL FILTERS IN MQL5 FOR BEGINNERS
"So, in my previous article I've made a code analysis of simple indicator and slightly covered the interaction of this indicator with MetaTrader 5 Client Terminal. Now, before we go any further, we should take a closer look at the results of expert compilation in "Errors" tab of "Toolbox" window in MetaEditor. From here you can begin further study of SMA indicator's code, that I had proposed earlier."
Figure - indicator for MetaTrader 5
This is 3-in-1 indicator: FATL, RSTL and RFTL (3 lines on the main chart).
Three indicator buffers . Each buffer has its own calculation formula: 39 previous close prices are used for FATL, 99 for RSTL and 45 for RFTL. For each buffer, previous Close prices have their own factors. The coefficients themselves can be viewed in code.
Fir filters - indicator for MetaTrader 5
Some of the digital filters in one place :
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Hann filter - indicator for MetaTrader 5
This is a variation of a digital filter using Hann windowing for filtering
Forum on trading, automated trading systems and testing trading strategies
Fast Fourier Transform - Cycle Extraction
Sergey Golubev, 2026.09.22 11:35
Defining your Edge (Part 6): Harnessing Fourier Transform and a Spiking Neural Network in an Expert Advisor
We resume our series on Defining your Edge in the markets by introducing a variant to the article-format we have presented so far. In the first five parts we introduced algorithm and network pairings. That article-format was about discovering and testing novel algorithms and neural-networks that are not mainstream; for this article we are "doing the opposite" of this exploration by looking at how to possibly exploit algorithm and neural-network pairings that have already been introduced.
Therefore, this 6th part, returns to the algorithm-network pairing that started this series. This earlier article united a Discrete Fourier Transform with a small Leaky Integrate-and-Fire Spiking Neural Network. The Fourier computation searched price, MACD and RSI for recurring structure. The Neural Network served as an extra layer that let directional stimulation (or votes) accumulate across completed bars before a signal was launched.