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Mechanical trading systems - Weissman R.L.

Weissman R.L. Mechanical trading systems - Wiley publishing , 2005 . - 240 p.
ISBN 0-471-65435-3
Download (direct link): mechanicaltradingsystems2005.pdf
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Dow) traded above 1,067.2 for first time in history. Traders buying that level were purchasing all-time new highs, which is in direct opposition to popular market wisdom admonishing us to buy low and sell high. Market participants focused solely on price reference points would have felt comfortable selling these historically “unsustainable” price levels. Therefore, the true contrarians were those following the trend and buying instead of selling these “high” prices. (The ultimate high of the market trend was not achieved until January 14, 2000, at 11,750 on the Dow).
By employing moving averages and other trend-following indicators, traders strive to attune themselves to the market’s assessment of an asset’s true value.
These indicators in turn help them to ignore psychological temptations inherent in fading what appears to be historically high or low prices. The success of trend-following indicators once again illustrates how the market rewards those who train themselves to do that which is unnatural and uncomfortable and punishes those desiring certainty, safety, and security.
Successful trend-following indicators not only force traders to abandon attempts to buy the bottom and sell the top, they reprogram traders away from destructive price reference points by forcing them to buy recent highs and sell recent lows.
Mean Reversion Indicators: Why They Work
If trend following is such a successful methodology, how can indicators based on the exact opposite philosophy generate consistent profits? The simple answer is that mean reversion indicators, such as RSI and other oscillators, work because they capitalize on the market’s tendency to overextend itself.
Whether the trend has matured and is approaching climactic reversal or is still in its infancy and simply correcting a temporarily overbought or oversold condition, the market has an uncanny knack for separating the less experienced from their money by exploiting their greed, lack of patience, and complacency.
Imagine speculators who saw the bull move early but allowed fear of losses to prevent them from buying the market. As the trend matures, their anxiety and regret magnify in lockstep with forfeited profits until they finally capitulate and buy at any price so that they can participate in this once-in-a-lifetime trend. Since the thought process that accompanied their ultimate trading decision was purely emotional and devoid of price risk management considerations, when the inevitable pullback or change in trend occurs, greed and hysteria quickly shift to panic and capitulation.
Although mean reversion indicators such as oscillators attempt to somehow quantify these unsustainable levels of market emotionalism, they
cannot do so as systematically as experienced traders with a “feel” for market psychology. For example, some on-floor traders are so attuned to the order flow entering their pit that they can consistently fade unsustainable emotionalism before it ever matures into a blip on a technician’s radar.1

Moving Averages
Simple Moving Averages and Popular Alternatives In Chapter 1 we examined two indicator-driven triggers that are also complete mechanical trading systems: the single moving average and the two moving average crossover. The variations on moving average indicators are so numerous that a book could be devoted exclusively to their various flavors; however, in the interest of completeness, I address what I believe are some of the most significant alternatives to the simple moving average.
As discussed in Chapter 1, simple moving averages are the most widely used and the easiest to calculate because they give equal weighting to each data point within the data set. This issue of equal weighting to each data point leads technicians to seek alternatives to the simple moving average.
The problem with using a moving average that gives equal weight to each data point is that with longer-term moving averages—such as the 200-day moving average—the lagging aspect of indicator means it will be slower to respond to changes in trend. Obviously slower response times to trend changes could mean less reward and greater risk. One solution to the problem of the lagging nature of the simple moving average is to give greater weight to the most recent price action. Linearly weighted and exponentially smoothed moving averages both attempt to address the equal weighting issue by giving a larger weighting factor to more recent data.2
An alternative to the moving average weighting paradigm is found through the use of a volume-adjusted moving average. The volume-adjusted moving average suggests that directional movement accompanied by strong or weak volume is often a better measure of trend strength than any of the time-driven weighting models.
Another problem with moving averages is choosing between shorter and longer time parameters. The smaller the data set, such as a 7-day moving average, the quicker the indicator’s ability to generate signals and the greater its reduction of lag time. But smaller data sets also result in more false trend-following signals during sideways, consolidation environments. As discussed, larger data sets, such as a 200-day moving average, will generate fewer, higher-quality entry signals, but those remaining signals will entail less reward and greater risk. Perry Kaufman, author of many books on
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