EasyLanguage computer code is used to calculate and display the indicators. From my viewpoint, Easy Language is just a dialect of Pascal with key words for trading. The important conclusion from this discussion is that we can think of the transfer response with equal validity in the time domain or in the frequency domain. SMA filters are a special case of moving average filters where all the filter coefficients have the same value.
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The vertical axis is the amplitude of the output relative to the amplitude of the input data in decibels. Figure 1.1 shows that there are zeros in the filter transfer response in the frequency domain as well as in the time domain. Cycles are a unique kind of trading analytics, being one of the few types of market data that can be accurately measured. But understanding what the cycles mean and which trades to make based on them is an extremely complex process. Cycle Analytics for Traders is a technical resource for self-directed traders that explains the scientific underpinnings of the filters and indicators used to make effective and profitable trading decisions.
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The output divided by the input is the transfer response of the system. It is this transfer response that describes the action of the system. The concepts are presented so they can be understood with only a background in algebra. The writing style in the book is intentionally terse so the reader doesn’t need to wade through a mountain of words to find the ideas being presented.
■ Nonrecursive Filters
This is a technical resource book written for self-directed traders who want to understand the scientific underpinnings of the filters and indicators they use in their trading decisions rather than to use the trading tools on blind faith. CyCycle Analytics for Traders will allow traders to think of their indicators and trading strategies in the frequency domain as well as their motions in the time domain. The descriptions are written for understanding at several different levels. Traders with little mathematical background will be able to assess general market conditions to their advantage. More technically advanced traders will be able to create indicators and strategies that automatically adapt to measured market conditions by using combinations of computer code that are described.
Advanced Technical Trading Concepts ·
This is a technical resource book written for self-directed traders who want to understand the scientific underpinnings of the filters and indicators they use in their trading decisions. There is plenty of theory and years of research behind the unique solutions provided in this book, but the emphasis is on simplicity rather than mathematical purity. In particular, the solutions use a pragmatic approach to attain effective trading results. Cycle Analytics for Traders will allow traders to think of their indicators and trading strategies in the frequency domain as well as their motions in the time domain. This new viewpoint will enable them to select the most efficient filter lengths for the job at hand.
Low-pass filters are data smoothers that remove the higher-frequency jitter in the input data that often makes the data hard to interpret. The penalty traders pay for this smoothing is the lag introduced in the transfer response. Though technical in nature, Cycle Analytics for Traders emphasizes simplicity rather than mathematical purity, taking a pragmatic real-world approach to attaining effective trading results. It allows traders to think of indicators and trading strategies in the frequency domain as well as their motions in the time domain, letting them select the most efficient filter lengths for the job at hand. One of the most important filter characteristics to a trader is how much lag the filter introduces at the output relative to the input. A nonrecursive filter whose coefficients are symmetrical about the center of the filter always has a lag equal to the degree of the filter divided by two.
Equation 1-7 shows that the zero in the transfer response occurs exactly at the Nyquist frequency. We have succeeded in completely canceling out the highest possible frequency in the four-bar SMA. In this case, the roots of the polynomial are called the poles of the transfer response because a zero in the denominator of the transfer response causes the transfer response to go to infinity at that point. One can visualize the transfer response as the canvas of a circus tent in the context of complex numbers, and the poles in the transfer response are analogous to the tent poles. While it is possible to choose coefficients that cause the transfer function to blow up, frequencies are constrained to be real numbers, and therefore it is relatively easy to avoid the complex pole locations. In this case, the higher frequencies are passed, and the lower frequencies are severely attenuated by the filter.
This is really bad for filters used in trading because using more data means the filter necessarily has more lag. Minimizing lag in trading filters is almost more important than the smoothing that is realized by using the filter. Therefore, filters used for trading best use a relatively small amount of input data and should be not be complex. In this chapter you will find the difference between nonrecursive filters and recursive filters, and combinations of the two, enabling you to select the best filter for each application.
Band-pass filters would pass only cycle components centered at the critical cycle period. Band-stop filters would reject only cycle components also centered at the critical cycle period. If that is the only term used in the filter, the filter is said to be nonrecursive.
- Parenthetically, the coefficient a0 is usually unity to keep things simple.
- This is a technical resource book written for self-directed traders who want to understand the scientific underpinnings of the filters and indicators they use in their trading decisions rather than to use the trading tools on blind faith.
- Further, that delay will be exactly half the degree of the transfer response polynomial.
- The only thing that differentiates one filter from another is the selection of the coefficients of the polynomials.
One of the first realizations that a trader must make is that cycles cannot be the basis of trades all the time. Sometimes the cycle swings are swamped by trends, and it is folly to try to fight the trend. However, the cyclic swings can be helpful to know when to buy on a dip in the direction of the trend. This equation completely describes the transfer response of any filter. The only thing that differentiates one filter from another is the selection of the coefficients of the polynomials. It is immediately apparent that the more fancy and complex the filter becomes, the more input data is required.
Cycle Analytics for Traders + Downloadable Software: Advanced Technical Trading Concepts Hardcover – November 18, 2013
The concept of thinking of how a filter works in the frequency domain as well as how it works in the time domain is central to the understanding of the indicators that will be developed. Low frequencies near zero are passed from input to output with little or no attenuation. Since higher frequencies are blocked from being passed to the output, the SMA is a type of low-pass filter—passing low frequencies and blocking higher frequencies.
- When we plot the response of the four-element SMA as a function of frequency in Figure 1.1, we see that we not only have a zero at the Nyquist frequency, but also at a frequency of 0.25.
- From my viewpoint, Easy Language is just a dialect of Pascal with key words for trading.
- It is important to remember that no filter is predictive—filter responses are computed on the basis of historical data samples.
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However, it is much more efficient to create the band-pass filter response simply by selecting the proper coefficients in Equation 1-3. This equation can be true only when the frequency is half the sampling frequency. Half the sampling frequency is the highest frequency that is allowable in sampled data systems without aliasing, and is called the Nyquist frequency. In our case, the sampling is done uniformly at once per bar, so the highest possible frequency we can filter cycle analytics for traders is 0.5 cycles per bar, or a period of two bars.
