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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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Although this may sound tempting, it is important to remember that the higher the peak-to-valley drawdown we experience, the lower our system’s likelihood of profitability. The probability of our returning to profitability decreases exponentially as the percentage of peak-to-valley drawdowns in equity increase. For example, a peak-to-valley drawdown in equity of 15 percent would require a subsequent gain of 17.6 percent to recapture the breakeven level. By contrast, a 50 percent peak-to-valley drawdown in equity requires a gain of 100 percent to regain the break-even level. Furthermore, this subsequent 100 percent gain would need to be accomplished with one-half of the original equity under management. The likelihood of a 100 percent gain in equity, after experiencing a 50 percent peak-to-valley equity drawdown, is so remote that some hedge funds employ a fund stop-loss level of 37.5 percent drawdown from the most recent equity peak.2
As a result, designation of a stop-loss level for the trading account or trading system is an unyielding prerequisite for successful price risk management. So how do we minimize our chances of ever experiencing a 37.5 percent drawdown in equity? We will seek to answer throughout the remainder of this chapter, but one commonly employed (although incomplete) solution adapted by system developers is to examine the worst drawdown of the backtested period and allow for a drawdown that exceeds this level by 50 percent. Based on this reasoning, if the worst drawdown of our backtested history was 20 percent, we should be prepared to endure a 30 percent drawdown and adjust our volumetric position sizing limits accordingly.

Read enough books on trading and price risk management, and one may come to the erroneous conclusion that there are two distinct schools of
price risk management: trader school and VaR/stress testing school. Although both schools sometimes imagine their theories regarding price risk management to be mutually exclusive, usually this is not the case. Furthermore, it is only through adaptation of the strengths of both approaches that a robust price risk management solution can be achieved.
One school dominates the books that have been written by and for traders. These books typically emphasize managing price risk based on two factors:
1. Volumetric price risk management, which is based on the size of the positions taken in the markets or how many volumetric units (e.g., contracts, shares, etc.) will be traded
2. Stop-loss price risk management, which determines the size of the risk assumed per position traded or how much capital will be risked per volumetric unit traded
The other school is composed primarily of risk management professionals and academicians who focus on price risk management on a portfolio-wide basis and utilize tools such as value at risk (VaR) and stress testing to aid in their development of a comprehensive price risk management strategy. VaR examines the standard deviation (or historical volatility) of a trading portfolio as well as the correlations between its various components. Stress testing attempts to illuminate weaknesses in VaR studies by analyzing the potential effect of price shocks and correlation breakdowns on the traded portfolio.
Although not always explicitly stated by the trader school of price risk management, both methods tend to measure price risk based on historical data. This is the reason why it is essential to ensure data integrity and robustness of trading systems and why I provided an in-depth explanation of these system development issues. Because both schools rely so heavily on historical data in developing price risk management strategies and because the markets will never behave exactly the same in the future as they have in the past, we must continuously assess and reassess future price risk.

Perhaps one of the most effective and fundamental aspects of managing price risk is the placement of stop-loss orders on a per-trade basis. Stop-loss placement forces us to exit positions when the market is no longer behaving as our trading models anticipated. Although there are no absolute answers regarding the placement of our stop-loss orders, it is generally agreed that they should be far enough from the current market price to prevent
Price Risk Management
“normal” fluctuations from resulting in realized losses, without being placed so far away that their election would result in endurance of a loss that jeopardizes our ability to return to profitability.
Studies of recent volatility on the markets traded, for our specific trading time frames, are probably the single most important element in successful minimization of losses due to normal market fluctuations. The second aspect of stop-loss placement, the jeopardizing of our ability to return to profitability, requires that this market volatility analysis be reviewed in relation to our total equity under management.
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