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Re: Adaptive Indicators for MT4

Black Sheep, Mon Mar 16, 2020 7:49 am

As we are on the theme of Cycles Analysis applied to trading, here is something worth mentioning IHMO.

Any experimental physicist would tell you that the number one tool for analyzing an electric signal is a Fast Fourier Transform (FFT). For those of you not familiar with the concept, an FFT can take a signal in the time domain, and break it apart into a frequency domain. Since electric signals are very much like price data (they oscillate and they're full of noise), I was wondering if anybody has ever tried to analyze price data using Fourier Analysis.

On a separate topic, there are also lots of noise-reducing techniques in experimental physics, like dithering, and adding white noise to a signal (in this case, price movement). Has anybody tried either of these techniques?

The Fourier Transform analysis can only be applied to periodic functions.

A periodic function is defined as a function which repeats itself every certain period of time. This, of course, is not applicable to the price action of any known financial instrument, simply because the price action does not repeat equally during certain time periods.

So, from the theoretical point of view, the Fourier Transform can't be used to analyze the price action of currencies or any other financial instrument.

However, I believe that it can be managed to apply the Fourier transform analysis but to portions of price actions. Let me explain this a little bit.

If the price action of a certain currency pair is considered, it must be cut down in which each piece must be confined within a certain known limit. For example, cutting the price action of the EUR/USD for 2 days based on the 1-hour chart provided that the price during these 2 days was oscillating between 1.1900 and 1.2000 for example.

Then applying a smoothing moving average for the extracted data, and then getting the time function of the moving average, and after all applying the Fourier Transform for the time function of the moving average.

The step of the moving average is important, as it will be very difficult to get the time function of the price data itself. It can be done by using curve fitting, but it's a very difficult and time-consuming issue. I don't even know if there is any software out there that do curve fitting for inserted data or not.

When you apply the Fourier Transform, you will get another time function which consists of only Sines and/or Cosines. The function will contain an infinite number of terms. The first term is called the fundamental component, and the rest are called the harmonics. That's what the Fourier Transform function is called when analyzing the Alternating Electric Current or any other waveform.

The Fundamental component is usually the most effective component, with the 3rd, 5th & the 7th components being taken into consideration. Usually, all the higher-order harmonics are neglected due to their minimal effect. Of course, I don't know what will be the analysis of the price action of currencies will result in.

Now the real question is: How can this improve trading and speculation?

If you are analyzing the most recent data, this can be a very useful tool to project price targets as well as defining market trend. Simply insert the required future time in the Fourier time function, calculate the fundamental, 3rd, 5th and 7th components, and you get a price. This price relative to what the price is right now will give an idea about the market next move.

Why this won't work as expected?

1- I don't believe that this will work as expected, just because the pair doesn't move in completely identical cycles. This deviation will result in errors in the Fourier Transform projections.

2- The market is trending during 60-70% of the time. These trending periods can't be analyzed using the Fourier Transform Analysis.

3- The Fourier Transform was created to analyze the behaviour of waves, electric signals and electric current. These phenomena are completely natural and are moving without any kind of emotions. On the other hand, the currencies and any financial market is being affected by many things, and emotions drive the markets sometimes, so there can be no fixed formula for the market, that's why trading systems that used to work in the past do not work in the future, because people change, but waves and electricity do not change their attitude because they don't like the way of their life for example, or because of terrorist attacks.

Again, IMHO, Mark Jurik has brought a substantial signal processing background from his military career developing missile tracking algorithms and other noise filtering techniques to processing price/time data for the financial markets. He currently builds the best smoothing algorithms I've seen in the financial industry. Whereas most indicators lag the more you add smoothing characteristics Juriks don't suffer the same problems. Quite clever really and you don't have to spend a lot of your time reinventing the wheel. He also references some other notables such as Kauffman. Also, Ehlers applied plenty of sound processing techniques used in filtering sound in amplifiers to his work and uses a lot of sound processing jargon as metaphors for filtering market noise.

For example, here is a chart of the current EURUSD M15 with the Mladen's Holt_double_exponential_smoothing_2.2, the red dotted line projecting the price trajectory for the next couple of hours also on the same chart as a matter of comparison I have placed a Fourier Extrapolator represented by the solid orange bold line.

By observing the predictions we should have the EURUSD appreciating at the beginning of the Asian Session, let's see how that unfolds in reality!
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