Physics still exists in blackcat's eyes

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The tale of my encounters with the subject of physics is one of dramatic twists and turns, filled with moments of uproarious laughter. During my undergraduate years, I miraculously achieved perfect scores in both semesters of college physics, making me a dazzling star in the academic feline world. Years later, as a graduate student at the same institution, I ran into the young woman who once taught me physics. Time had altered her once youthful appearance. In our conversation, she nostalgically mentioned that since my time, no student had achieved full marks in college physics. This instilled in me a sense of pride; it seemed I had transformed physics into an unrivaled discipline, giving me a sense of unparalleled prowess.

My brilliance shone so brightly, as if I were the sun of the feline academic world! Similarly, my enthusiasm for Digital Signal Processing (DSP) during my university years was equally intense. Scoring 98 in the undergraduate exam filled me with immense pride, as if DSP was my personal dominion. Years later, as a postgraduate student dismayed to retake the subject, I cleverly borrowed DSP revision materials from my juniors.

On those extensively photocopied, slightly faded pages, I recognized a familiar handwriting. It was exhilarating to discover it was the study material I had prepared for my classmates during my undergraduate years. This material had been preserved and passed down among the juniors over the years. Reflecting on this, I felt like a legend in the Automation department. My academic legacy seemed to have impacted many, much like a torch that has been burning for ages. To sum up, while my academic journey resembled an extinct dinosaur, my capabilities shone like a torch, lighting the path for others. The allure of my academic brilliance, it seems, is simply irresistible!

My intense engagement with physics in trading began with John F. Ehlers' four English textbooks. When confronting the intricate market environment, my scientific background inclined me to trust theories with a solid foundation over anecdotal or experiential 'holy grails.' Ehlers' intricate theories were made accessible through the physics knowledge I had accumulated in school, enabling me to swiftly translate complex ideas into code. Thus, for those seeking the most comprehensive collection of Ehlers' technical indicators, my TradingView page, under the search term 'Ehlers,' houses a vast array. These are the scripts I meticulously translated from Ehlers' books, a testament to my persistence and dedication.

Embarking on a journey through physics in the trading world is no small undertaking. Beginning with John F. Ehlers' (JFE) four seminal textbooks, I plunged into the depths of physics. In the convoluted market landscape, my predilection for scientifically-grounded conclusions over anecdotal or so-called 'holy grails' guided my approach.

Ehlers' theories, though complex, became approachable thanks to my extensive physics background from my academic years, greatly aiding my trading endeavors. Not only did I grasp the theories, but I could also rapidly transform them into actionable code. For those curious about the most exhaustive Ehlers' technical indicators on the internet, a visit to my TradingView page and a search for 'Ehlers' will reveal a wealth of resources. All these are scripts I translated from Ehlers' textbooks into TradingView. Even now, I admire my own determination and grit! Heh, are you surprised? I'm not just a physics whiz but also a sage in transforming theory into practical application! I am a prominent figure in the Chinese cat community, having taken my theoretical studies to their zenith. Regardless, I will always treasure this special ability and continue to harness the magic of physics in trading. Who knows what lies ahead?

First, let's go back to the main topic after all the introduction. After learning about Ehlers' theory, what do markets and technical indicators look like? Firstly, Ehlers is an expert in the field of digital signal processing (DSP), and he introduced this method into technical analysis. Here are his main reasons and viewpoints for using this method:

Market prices as time series data: Ehlers believes that price data in financial markets can be seen as a discrete time series system. This means that market prices are similar to digital signals (such as audio signals) composed of a series of data points that change over time.

Market periodicity: Ehlers observed that financial markets often exhibit periodicity, and digital signal processing is used to analyze periodicity and other components in signals. By appropriately applying DSP techniques, we can more accurately determine market cycles and predict future price movements.

Noise filtering: Digital signal processing techniques help analysts filter out random noise in price data, allowing clearer observation of true market trends and patterns.

Adaptation to cycles: Unlike traditional fixed-period indicators, indicators developed using DSP techniques can adapt to the current market cycle, providing more accurate and timely trading signals.

Filters and transforms: He designed a series of filters and transforms, such as MESA, Fisher transform, etc., to help identify and utilize market periodicity.

Nonlinear systems: He also believes that since the market is a nonlinear system, traditional linear methods may not always be effective. Therefore, his techniques often take into account this nonlinear characteristic of the market.

Regarding the question of whether this methodology is correct or not, like all technical analysis methods, there is no method that is absolutely correct. However, Ehlers' methods and indicators have been accepted by many traders and analysts and are considered to be very effective tools under certain market conditions. But like all trading tools, they should be used in conjunction with other analysis methods and strategies, always considering risk management.

Ehlers considers market prices as a discrete time series system because price data is usually reported at fixed time intervals (such as daily, hourly, or minute intervals), and each time point has a specific value. This is very similar to discrete signals in digital signal processing, where signals are also composed of a series of data points with a fixed interval in time.

If the above is too difficult, we can explain the concept of Ehlers' market physics with a simple example: Do you think the essence of moving averages and oscillators in technical indicators is the same?

According to Ehlers' viewpoint, moving averages and oscillators are essentially similar. His view is that all technical indicators, whether moving averages or oscillators, are filters that extract certain information from market data. In some of his work, Ehlers points out that oscillators can be seen as variants of moving averages, or more specifically, they are different types of filters. For example, a simple moving average (SMA) is a low-pass filter that allows low-frequency price variations to pass through while filtering out high-frequency noise. Oscillators like RSI or MACD can be seen as band-pass filters that extract signals within specific frequency ranges. Therefore, Ehlers' view is that different technical indicators only apply different mathematical methods to process and interpret market data, but they are fundamentally filters. This is also why he employs advanced signal processing techniques like Hilbert transform in designing indicators, as these methods can extract meaningful signals from market data more accurately.

Thus, according to John F. Ehlers' viewpoint, filters can be classified. He believes that all technical indicators in the market are some form of filters. According to his classification method, filters can be classified into the following types:

Low-pass filters (LPF): These filters allow low-frequency components to pass through while filtering out high-frequency components. The simple moving average (SMA) is an example of a low-pass filter.

High-pass filters (HPF): In contrast to low-pass filters, these filters allow high-frequency components to pass through while filtering out low-frequency components.

Band-pass filters (BPF): These filters only allow components within a specific frequency range to pass through. Many oscillators, such as RSI or MACD, can be seen as some form of band-pass filters as they typically focus on price variations within a specific range.

Band-stop filters (BSF): These filters block components within a specific frequency range while allowing signals of other frequencies to pass through.

Here are some common low-pass filter technical indicators:

Simple Moving Average (SMA)

Exponential Moving Average (EMA)

Weighted Moving Average (WMA)

Triple Exponential Moving Average (TEMA)

Double Exponential Moving Average (DEMA)

Hull Moving Average (HMA)

These indicators work by averaging or weighted averaging the historical data of prices or other indicators in some form, so they are all called low-pass filters. The goal of a low-pass filter is to allow low-frequency (long-term) price trends to pass through while filtering out high-frequency (short-term) noise or fluctuations.

On the other hand, high-pass filters (HPF) are not as common in technical analysis as low-pass filters (such as moving averages) because they primarily focus on the fast and transient changes in prices, which are often considered as "noise" in the market. However, in certain applications, this "noise" or short-term variation may be valuable. The following are indicators that may be considered as high-pass filters or at least have high-pass filter characteristics:

Rate of Change (ROC): ROC is an indicator that measures the rate of price change and focuses on rapid price changes.

Momentum: Similar to ROC, the momentum indicator measures the change in price relative to a past period.

First Derivative: Although not a common indicator, calculating the first derivative of prices or other indicators can be seen as a high-pass filter. This is an interesting fusion of mathematics and physics, and I plan to write an article about it separately. Learning about trading can greatly benefit from the application of mathematical and physical knowledge.

It should be noted that the above indicators may not be true high-pass filters, but they focus on the rapid and short-term changes in prices, so they have some characteristics of high-pass filters to some extent.

Then, the application of bandpass filters in technical analysis may not be as obvious as low-pass and high-pass filters, but they do exist. Bandpass filters only allow signals within a specific frequency range to pass through, while filtering out other frequencies. This can help analysts focus more on specific market cycles. Many technical indicators, although not strictly bandpass filters (not 100% conforming to the physics definition of a bandpass filter), have similar characteristics because they focus on specific price change cycles or ranges. Here are some technical indicators that may have bandpass filter characteristics:

Stochastic Oscillator: This indicator measures the current price relative to its past range. Although it is not a pure bandpass filter, it does focus on a specific range of price changes.

Relative Strength Index (RSI): The RSI measures the relative strength of price movements and typically ranges from 0 to 100.

MACD (Moving Average Convergence Divergence): Although MACD is based on the difference between two moving averages, it focuses on the specific relationship between these two averages, thus having a certain bandpass characteristic.

Commodity Channel Index (CCI): CCI measures the deviation of commodity or stock prices from their average prices.

Some of the indicators listed above may not be pure bandpass filters, but they all to some extent focus on a specific range or period of price data. These bandpass filter indicators are often used to identify overbought or oversold conditions in the market, or to identify potential market turning points. Overbought and oversold signals are often the signs used to identify them.

Bandstop filters (also known as notch filters) are not commonly used directly in technical analysis. The purpose of these filters is to suppress or filter out signals within a specific frequency range while allowing signals of other frequencies to pass through. 99% of indicators do not have pure bandstop filter characteristics. However, considering the working principle of bandstop filters, there are indeed similar ones, such as: Range: Some strategies and indicators may use the difference between high and low prices to measure intra-day price changes. This method can filter out intra-day noise to some extent, especially when the market has a narrow trading range. However, the above-mentioned indicators are not specifically designed as bandstop filters. In practical financial market analysis, bandstop filters are less commonly used, and there is more application of low-pass, high-pass, or bandpass filters. If you have specific needs or purposes, you may need customized strategies or indicators to achieve similar effects to bandstop filters.

The reason why I consider the range as a bandstop filter to some extent is that it removes specific price movements within the day and retains a wider range of fluctuations. However, this analogy is relative because in traditional signal processing, bandstop filters are usually frequency-based rather than time-based like the range. In summary, although the range is not a bandstop filter in the traditional sense, it does provide a similar effect to some extent from a functional perspective, which is to filter out specific intra-day price dynamics and only retain their range of fluctuations.

Based on our previous discussion and the typical characteristics of technical indicators, the technical indicators I see can be classified as follows:

Low-pass filters: These filters allow long-term (low-frequency) trends to pass through while suppressing short-term (high-frequency) noise.

Moving averages (e.g., SMA, EMA, WMA, etc.)

Bollinger Bands

High-pass filters: These filters allow short-term (high-frequency) fluctuations to pass through while reducing long-term (low-frequency) trends.

Some difference or rate of change indicators, such as Momentum or Rate of Change (these indicators can be seen as emphasizing short-term changes in price)

Bandpass filters: These filters emphasize price changes within a specific time range or period while suppressing changes that are too long or too short.

Stochastic Oscillator (focuses on a specific range of price changes)

RSI (considers price dynamics within a specific time window)

MACD (although its core is based on the difference between two EMAs, it to some extent emphasizes price dynamics within a specific time range)

Bandstop filters: In commonly used technical indicators, true bandstop filters are not common. However, the range can be approximated as having bandstop characteristics because it aims to remove long-term trends to better analyze short-term or periodic fluctuations in price.

It should be noted that these classifications are based on a broad interpretation of the concept of filters, combined with the common uses and characteristics of technical indicators. Many technical indicators are not primarily designed with filtering in mind, so strictly categorizing them into a specific type of filter might introduce some ambiguity. However, regardless, has today's article provided you with a different perspective? If so, please give a like to this cat.