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a good example is the Divergengine software distributed by Ruggiero Associates. An example of divergence appears in Figure 7- 1. There are a number of issues to consider when using oscillators to generate entries, e.g., smoothing of data and timeliness of entry. The MACD, for example, can sometimes provide the smoothing of a moving average with the timeliness of raw prices. The combination of timeliness and good smoothing may yield entries that are more profitable than those obtained when using moving average entry models. The peaks and valleys in a moving average come significantly after the corresponding peaks and valleys in prices. Consequently, entries generated by looking for these peaks and valleys, or Yarning points, are excruciatingly late. Conversely, when cyclic activity in the market has a periodicity that matches the particular MACD used, the peaks and valleys in the output from the MACD come at roughly the same time as the peaks and valleys in the prices; the smoothing of a moving average is achieved without the lag of a moving average, Because the MACD smooths the data, numerous noise-induced trades will be eliminated, as happens with moving averages. Because the MACD can be timely, trades generated may be profitable. In addition to the MACD, many other oscillators tend to move concurrently with prices or even lead them. For reasons to be discussed later, leading or coincident
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indicators do not necessarily generate more profitable entries than such lagging indicators as moving averages. Having coincident indicators does not necessarily mean highly profitable signals. The problem is that even though some signals will occur with precise timing, many spurious signals can result, especially in the context of developing trends. When strong trends are present, the anticipated or coincident reversals may simply never take place, leading to entries in the wrong direction. Timeliness may be gained, but reliability may be lost. The question of which tradeoff provides a more profitable model-getting in reliably but late, or on time but unreliably-is a matter for empirical study. Such issues are present with any entry method or pattern detection or forecasting model: The greater the delay, the more accurate (but less useful) the detection or indication; the lesser the delay, or the further ahead a forecast must be made, the less accurate (but more useful) the detection or indication, The logic is not unlike that of the Heisenbetg Uncertainty Principle. As an example of how oscillators may be used to generate entries, consider the Stochastic: A simple entry model might buy when this indicator drops below the traditional oversold threshold of 20 and then rises above that threshold. It might sell when the indicator goes beyond the traditional overbought threshold of 80 and then drops back under. The trader must not wait for another signal to close out the current position, as one might do when using a moving average crossover; such a signal might not occur for a long time, so an independent exit is essential. Traders also look for the so-called Stochastic hook, a pattern in which the Stochastic reaches a tirst low, moves up a little, and then reaches a second low at a higher level than the first. A buy signal is generated as soon as the second low becomes detined. A sell is generated with the exact same pattern flipped over; i.e., a lower high follows a higher high. As in the case of breakouts and moving averages, oscillator-generated entries can be effected using any of several orders, such as a market at open, limit, or stop. The advantages and disadvantages of these orders have been discussed thoroughly in the previous two chapters. CHARACTERISTICS OF OSCILLATOR ENTRIES
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Oscillator-based entries have the positive characteristic of leading or being coincident with price activity; therefore, they lend themselves to countertrend entries and have the potential to provide a high percentage of winning trades. Oscillators tend to do best in cycling or nontrending (trading range) markets. When they work, oscillators have the appeal of getting the trader in the market close to the bottom or top, before a move has really begun. For trades that work out this way, slippage is low or even negative, good fills are easily obtained, and the trade turns profitable with very little adverse excursion. In such cases it becomes easy to capture a good chunk of the move, even with a suboptimal exit strategy. It is said that the markets trend only about 30% of the time. Our experience suggests that many markets trend even less frequently. With appropriate filtering to prevent taking oscillator-
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based signals during strong trends, a great entry model could probably be developed. The kind of filtering is exactly the opposite of what was sought when testing breakout systems, where it was necessary to detect the presence, rather than the absence, of trends. The primary weakness of simple oscillator-based entries is that they perform poorly in sustained trends, often giving many false reversal signals. Some oscillators can easily become stuck at one of their extremes; it is not uncommon to see the Stochastic, for instance, pegged near 100 for a long period of time in the course of a significant market movement. Finally, most oscillator entry models do not capture trends, unlike moving averages or breakouts, which are virtually guaranteed to capture any meaningful trend that comes along. Many traders say that the trend is your friend, that most money is made going after the big wave, and that the profits earned on such trends can make up for the frequent smaller losses of trend-following systems. Because oscillator entries go after smaller, countertrend moves, it is essential to have a good exit strategy to minimize the damage that will occur when a trend goes against the trades.
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