The most common mistake in stock investing is to refrain from investing altogether, citing fear of pitfalls. This is because the person needs to be aware of the potential for compound interest. This means earning the interest on your interest of deposited amount, which guarantees a return much higher than any non-interest-bearing account. Among all the necessary tools used for online investment and trading, stock screeners play a significant role in analyzing the true potential of any equity and making an informed decision.

Several technical analysis indicators are conventionally used for filtering the stocks, such as Moving averages, Butter worth filter, Kalman filter, etc. All these filters have a consistent built-in flaw in them in that they treat the stock market price to be stationary and stock market fluctuations to be a linear phenomenon. However, all the variables associated with the stock market price data are constantly evolving and non-equilibrium. Hence, there is no point in declaring the market price data in a normal statistical representation, such as a bell-like curve.

Let’s have a sneak peek at the various filters used as the technical analysis indicator and how they impact the process of any stock screener.

  • A moving average filter is a digital tool that considers the average of a certain number of past prices and levels out the fluctuations in the process. The most common moving average filters are SMA’s and EMA’s (Simple moving averages and exponential moving averages, respectively).
  • The Butterworth filter is ideal for eliminating high-frequency noise from the price data by modifying or removing certain signal frequencies. Frequencies allowed to pass are known as passbands, and those blocked are called stop bands. Butterworth’s filters have a flat frequency for the passband and a cut-off for a stop band, thus successfully removing the unwanted noise from the market price data.
  • Kalman is a digital filter that uses a mathematical algorithm to determine the underlying trend, and it also helps eliminate the noise or errors from the market price data.
  • The Wavelet Transform filter is an advanced digital filtering that decomposes the signals into small frequency bands, as it is beneficial for determining the trends across multiple time scales. This type of filtration is effective for identifying long-term trends and short-term fluctuations, thus providing essential stock tips.
  • Ehlers filter is named after a prominent trader and author, John Ehler, and this mechanism uses the average of past prices and then compares it to the current price. If the result shows a higher value of the current price than the moving average, it is considered an uptrend, and otherwise, it is a downtrend. It is useful for various financial instruments such as stocks, bonds, and other commodities and generating buy and sell signals.

People like John Ehler, who come from a technical background and then switched to the trading landscape, have quite a scientific approach to utilizing digital signals in determining the stock market price movement. Non-linearity is a unique feature of the Ehlers filter that sets it apart from its counterparts because most digital filters follow a linear path. In this process, they tend to lose a lot of essential information. Trading stocks comes with its fair share of struggles and shortcomings; as long as advanced technology regulates the entire process online, it is always worth trying.

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