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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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Chapter 2 addressed the issue of how indicators could be turned into trading systems; it did not cover the process of system development and various considerations inherent in the backtesting of a trading strategy. Now that we are ready to analyze the success or failure of a particular trading system, we need to examine these issues.
Considerations with Any Indicator-Driven Triggers
Entry and exit levels are self-explanatory for price-driven triggers since the violation of a historical high or low signals entry or exit of a particular price-driven trading system, such as channel breakout. By contrast, indicator-driven triggers raise a myriad of entry and exit level questions for system developers. The first question is fairly subjective: Are we as traders able to watch the screen and place entry or exit orders as the indicator levels are violated intraday? If so, we run the risk of trading an intraday violation that could reverse itself and not trigger a signal at end of day. Of course, the advantage in taking an intraday signal is the potential for better prices (less risk and greater reward); however, most system developers prefer knowing that the signal will remain valid at end of day (since the results of all intermediate to long-term trading system are necessarily based on end-of-day signals only).
Because most system developers rely on end-of-day indicator-driven entry and exit triggers, the next question is: Do we assume our entry/exit price level to be the close or the following day’s open? Although either of these alternatives is acceptable in most instances, in choosing entry on the close, we run the risk of the indicator trading just beyond the trigger level in
Trend-Following Systems
the final minute of trading and then settling back to levels that would not generate a signal. This is not usually as severe of a problem as taking intraday signals because, for most markets (especially 24-hour ones), the price level for the following day’s open usually will be fairly close to our entry price. Nevertheless, the only surefire method of avoiding false entry and exit signals is to set the indicator trigger to the close (or settlement price) and the entry or exit level to the opening price of the following day.
Composition of Portfolios In determining the success of a particular trading system, ideally we would like to test our results on as many assets as possible. Unfortunately, many of these assets are highly correlated with each other. Inclusion of too many highly correlated assets (e.g., soybeans, corn, soybean meal, Chicago wheat, Kansas City wheat, soybean oil, Minneapolis wheat, and rough rice) could skew the backtested results of the system, leading us to believe either that a profitable system loses money or, more important, that a losing system is profitable.
Next we must make some assumptions regarding slippage and commissions that are both realistic and conservative. For example, it is unrealistic to assume that our stop price and our fill price will be identical. Because we will be forced to make assumptions regarding “reasonable" slippage and commission levels on our backtested portfolio, we want to ensure that these assumptions are conservative enough to have a high probability of replication when trading the system in real time. As a result, ideally our portfolio should contain only those assets that experience minimal slippage, in other words, those that are the most liquid. It is for this reason that low-liquidity instruments such as Nymex coal futures are not included in our portfolio. (Note that the liquidity of various assets changes over time. As a result, traders are strongly encouraged to monitor volume and open interest statistics provided by the various exchanges.)
Finally, if the market chosen for our backtesting produces consistent profits, but those profits are so small—due to either lack of volatility or value of contract—that commissions and slippage turn those paper profits into net losers, then those markets should be omitted. It is for this reason that I have chosen not to include Chicago Board of Trade (CBOT) corn in my backtested portfolio despite its excellent liquidity.
The other issue to consider regarding contract size is that just as we avoided inclusion of highly correlated assets in our portfolio to ensure the robustness of the system, as much as possible we should ensure that no single market within our portfolio has a contract size that dwarfs or enlarges the weighting of other portfolio components. It is for this reason that I have chosen the E-mini S&P 500 futures contract instead of the full-sized S&P 500 futures contract. Finally, many system developers include weighting matrices to address these issues. Although my portfolio does not employ
such a matrix, readers are strongly encouraged to experiment with various weightings to achieve portfolio component parity.
With these considerations in mind, I have chosen to include one asset from the asset classes shown in Table 3.1.
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