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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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If, however, we had seen a total net profit that exceeded our 1995 results by more than 150 percent, we would need to carefully examine such performance in attempt to ascertain whether the markets had undergone a paradigm shift.18 Although additional analysis in the face of such extraordinary profits seems counterintuitive, unprecedented profitability is likely to have been a byproduct of increased volatility (defined as the speed and magnitude of price movement).
Analyzing severe increases in volatility is not as simple as it might appear. The difficulty stems from the fact that volatility tends to trend and that it exhibits heteroskedasticity. (Heteroskedasticity means that volatility is not constant, but instead that it tends to cycle from periods of high volatility to low volatility ad infinitum.) Consequently, as long as the trend of volatility remains intact, we should reduce our position size to stay within prudent price risk management tolerances (see Chapter 8). However, once the uptrend in volatility has been violated, we can expect a cycle of low volatility to ensue, and so the position size assumptions established during our insample backtest are likely to be satisfactory.
The study of the volatility trend of a single asset can prove to be a daunting task, and application of those principles to an entire portfolio of diversified assets often is nightmarish. Thus, unless a person is a risk manager for a large financial institution with sophisticated software for analyzing volatility trends, it is prudent to scale back position size to stay within price risk management tolerances dictated by the out-of-sample results.
The other comparisons between our in-sample and out-of-sample data include categories such as maximum drawdown, profit to maximum drawdown, average trade duration, and win/loss ratio. Scanning through the “totals” rows of Tables 7.22 and 7.28 reinforces our initial year-by-year conclusions regarding the acceptability of the out-of-sample results.
Other interesting points to note in the out-of-sample results are the poor performance of IMM Japanese yen in 2003 and the stellar performance of Comex gold in this same year. Both performances were atypical based on nearly all of the in-sample studies shown in Chapter 3. I point this out to reinforce the importance of maintaining a diversified portfolio. The markets are not going to behave the same in the future as they have in the past. As a result, cherry picking certain asset classes in hopes of improving performance sometimes can lead to disastrous results.
System Development and Analysis

Most asset allocation firms and many institutional investors will ask traders for a trading methodology philosophy statement along with hypothetical and real-time trading results. I encourage traders and system developers to write a philosophy statement for each of their trading systems (and for their combined portfolio of systems, if applicable), irrespective of whether they are currently attempting to secure outside allocations of capital. By formulating a trading philosophy statement, we concretize trading strategies and/or mechanics and sometimes can identify flaws in logic or price risk management prior to committing capital in the markets. In addition, this document serves as an ideological benchmark of performance expectations through which we can compare our real-time results. The philosophy statement should include these items.
• Overall trading philosophy. The philosophy statement should outline explicitly the principles on which the strategy is based, what type of market behavior the methodology is attempting to capitalize on (e.g., trend-following or mean reversion), and why it is robust enough for similar results to be achieved in the future.
• Length of performance (and/or backtested) history. This section should explain to potential investors the length of performance (and/or backtested) history. The explanation should prove that the data history is robust enough to include all types of market environments (bullish, bearish, trending, choppy, volatile and neutral). In addition, the data history should include enough trades to be statistically significant.
• Liquidity risk. This section should include all assumptions regarding liquidity of the markets traded. It must detail allowances for round-turn slippage and/or commissions. Regardless of the superior liquidity of the assets traded, I always assume a minimum deduction of $75 per round-turn trade and routinely increase this assumption to $200 per round-turn trade for many of the asset classes highlighted in Chapters 3 and 4.
• Trade duration and average flat time. This section should include the estimated average duration of trades. Often I break this section down into average duration of winning and of losing trades. Familiarity with this measure sometimes can clue us in to paradigm shifts in market behavior.
Inclusion of average flat time shows prospective investors how actively their account will be traded. Like trade duration, it is also valuable in alerting traders to shifts in market behavior.
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