This article introduces common recursive filters in TradeStation formulas, including FIR and IIR structures. It lists some common moving averages and filter types such as simple moving average, weighted moving average, exponential moving average, etc. Additionally, it mentions the principles and applications of the Kalman filter as a type of recursive filter. The article emphasizes that recursive filters are suitable for real-time systems and have the ability to adapt and adjust to changes in market conditions. Finally, it provides a list of common types and application areas for both recursive and non-recursive filters.
Adaptive filters are a type of filter that does not belong to traditional low-pass, high-pass, band-pass, or band-stop filters. They can automatically adjust the filtering effect based on market volatility and periodicity. This type of filter provides a more flexible and intelligent tool to help us better understand and analyze market dynamics. John F. Ehlers has developed various adaptive technical indicators, such as the MESA Adaptive Moving Average (MAMA) and the Adaptive Laguerre filter, which can better adapt to market changes and provide more timely and accurate signals. Building an adaptive filter requires in-depth analysis of market data, understanding its inherent dynamics, and using this knowledge to dynamically adjust the filter's parameters.
This article introduces how to use derivatives to calculate the second, third, and fourth derivatives of prices, and explains their applications in the financial market. The first derivative represents the change in velocity, the second derivative represents the change in acceleration, the third derivative represents the change in the rate of change of acceleration, and the fourth derivative represents the change in the rate of change of acceleration. The article also discusses the characteristics of derivatives as high-pass filters and the differences in cutoff frequency and corner frequency for different orders of derivatives. Finally, the article mentions some popular technical indicators that use the concept of derivatives and emphasizes the need for smoothing or other processing techniques to reduce the impact of errors and noise in practical applications.
Any moving average is an oscillator, and a bandpass filter can be generated by combining low-pass and high-pass filters. Converting a low-pass filter to a bandpass filter is more practical, while converting a high-pass filter to a bandpass filter may not be as practical. The conversion process is irreversible. John F. Ehlers has developed many technical indicators based on digital signal processing, including low-pass, high-pass, and bandpass filters.
This article introduces the common types of filters used in technical analysis, including low-pass filters, high-pass filters, band-pass filters, and notch filters. Although pure notch filters are not commonly seen in technical indicators, some indicators, such as the high-low difference, can be approximately considered as having notch characteristics. Additionally, the article lists some common technical indicators and explains their relationship with filters.