The previous article dealt with John Ehlers’ AM and FM demodulating technology for separating signal and noise in price curves. In the S&C June issue he described a practical example. Applying his FM demodulator makes a strategy noticeably more robust – at least with parameter optimization.
Price curves consist of much noise and little signal. For separating the latter from the former, John Ehlers proposed in the Stocks&Commodities May 2021 issue an unusual approach: Treat the price curve like a radio wave. Apply AM and FM demodulating technology for separating trade signals from the underlying noise. Continue reading “The Price Wave Radio”
It is estimated that about 6000 different technical indicators have been meanwhile published, but few of them are based on volume. In his article in Stocks & Commodities April 2021, Markos Katsanos proposed a new indicator for detecting high-volume breakouts. And he tested it with a trading system that I believe is the most complex one ever posted on this blog.
Financial markets are not stationary: Price curves swing all the time between trending, mean reverting, or entirely random behavior. Without a filter for detecting trend regime, any trend following strategy will bite the dust sooner or later. In Stocks & Commodities February 2021, Richard Poster proposed a trend persistence indicator for avoiding unprofitable market periods.
Japanese rice merchants invented candle patterns in the eighteenth century. Some traders believe that those patterns are still valid today. But alas, it seems no one yet got rich with them. Still, trading book authors are all the time praising patterns and inventing new ones, in hope to find one pattern that is really superior to randomly entering positions. In the Stocks & Commodities January 2021 issue, Perry Kaufman presented several new candle patterns. Let’s repeat his pattern tests with major US stocks and indices, and with or without an additional trend filter. Continue reading “Petra on Programming: Short-Term Candle Patterns”
A major problem of indicator-based strategies is that most indicators produce more or less noisy output, resulting in false signals. The faster the indicator reacts on market situations, the noisier is it usually. In the S&C December issue, John Ehlers proposed a de-noising technology based on correlation. Compared with a lowpass filter, this method does not delay the signal. As an example, we will apply the noise elimination to Ehlers’ MyRSI indicator, a RSI variant that he presented in an earlier article. Continue reading “Petra on Programming: Get Rid of Noise”
Fortunately I could write this article without putting my witch hat on. Despite its name, the ‘Gann Hi-Lo Activator’ was not invented by the famous esotericist, but by Robert Krausz in a 1998 article in the Stocks&Commodities magazine. In a recent article, Barbara Star combined it with other indicators for a swing trading system. Will an indicator with the name ‘Gann’ work outside the realm of the supernatural? Continue reading “Petra on Programming: The Gann Hi-Lo Activator”
In the S&C September 2020 article “Tracking Relative Strength In Four Dimensions”, James Garofallou presents a metric for evaluating a security’s strength relative to 11 major market sectors and over several time periods. All this information is squeezed into a single value. Maybe at cost of losing other important information? In this article we’ll look into how to program such a beast, and how it fares when we use it for rebalancing a stock portfolio. Continue reading “Petra on Programming: Four Dimensions of Strength”
Vitali Apirine, inventor of the OBVM indicator, presented another new tool for believers in technical analysis. His new Compare Price Momentum Oscillator (CPMO), described in the Stocks & Commodities August 2020 issue, is based on the Price Momentum Oscillator (PMO) by Carl Swenlin. Yet another indicator with an impressive name. But has it any use? Continue reading “Petra on Programming: The Compare Price Momentum Oscillator”
Cumulative indicators, such as the EMA or the MACD, are affected by a theoretically infinite history of candles. In finite backtests, these indicators return slightly different results depending on the test period. This effect is often assumed negligible. But John Ehlers demonstrated in his July S&C article that it is not so. At least not for some indicators, such as a narrow bandpass filter. We have to truncate the indicator’s ‘internal history’ for getting consistent results. How do we do that in C? Continue reading “Petra on Programming: Truncated Indicators”