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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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Once the scan of performance tables has been completed, the spot-checking process should commence. Here we should seek answers to these questions:
• Were conditions for entry and exit met?
• Were trades initiated?
• Was it at the intended price level?
• Were commissions and/or slippage deducted from profits and added to
As with the data integrity issues, until indisputably satisfactory answers to these spot-checking questions are attained, continuation of trading system analysis is pointless.
An entirely different aspect of system checking is the process that Robert Pardo, pioneering author of the book on trading system develop-
ment, calls theory checking. Theory checking examines the performance of the system in terms of how the actual backtested results conformed to theoretical expectations. Divergence between expectations and actual results is not necessarily a fatal flaw in theory checking, as it is in spot checking, so long as the generated results are still attractive in terms of viability of the system and its compatibility to our own particular trading temperaments.
The main questions the theory checking process seeks to answer are: Did the system perform as intended? Did it profit in trending or choppy markets? Were the average trade duration, win/loss ratio, and profit to maximum drawdown ratios experienced similar to initial assumptions? And, if not, why did they differ from these expectations?3 Personally, I have always felt that the primary point in theory checking is to determine whether initial assumptions regarding the trading system in question were erroneous or if the particular data series examined was in some manner atypical.

An Overview
Optimization is the process of tweaking the raw trading system by adjusting its parameters (or variables—e.g., number of days, indicator-driven triggers such as moving averages, price-driven triggers such as channel breakout, etc.) and/or parameter sets (or combination of parameter values—e.g., a two moving average crossover system utilizing 9- and 26-day moving averages).4 Optimization is a valuable aspect of the system development process because, without this essential step, we would be forced simply to accept whatever performance results were generated by our system’s default parameters and parameter sets. Without optimization we might falsely believe a successful system to be unprofitable or, worse still, that a losing system is profitable.
Despite these undeniable benefits, optimization is not without its drawbacks. Modification of parameters and/or parameters sets can easily lead to false expectations regarding the future performance of a system. As with most tools, optimization has its utility, but this utility can be actualized only if the process is employed with diligence toward the scientific process and an awareness of its inherent limitations.
Optimization: Benefits
In addition to the benefits just outlined, optimization enables system developers to test out broad theoretical concepts regarding market behavior (e.g., the market’s propensity to trend, to revert to the mean following a parabolic directional move, etc.) prior to commitment of real money. Even if
System Development and Analysis
we are fairly confident in the robustness of a particular theory regarding market behavior, such confidence is a far cry from estimations of profit to maximum drawdown ratios, win/loss ratios, and the like.
Another key benefit to optimization studies is their ability to provide a historical benchmark of system performance that can then be used to compare against real-time trading results. Because the dynamics of markets are constantly changing, this ability to measure performance against the past can quickly clue us in to paradigm shifts requiring our revision and/or abandonment of trading systems.5
Optimization also shows us a trading system’s entire spectrum of expected performance results (over a wide variety of parameter sets) prior to the commitment of capital; this increases our odds of determining the best set of values for our particular personality traits in terms of average duration of trade, maximum consecutive losses, and win/loss ratio. Psychological trader profiles have been discussed at length already; here I simply reiterate that traders’ definitions of optimal performance results differ based on their own personalities. The optimization process helps them to avoid an incompatible system and/or parameter set.6
Finally optimization is an invaluable tool in the identification and avoidance of suboptimal parameter sets. In his book Schwager on Futures: Technical Analysis, Jack Schwager convincingly demonstrates a disturbingly low correlation between historically optimal parameter sets and the optimal parameters sets uncovered through the walk-forward (or out-of-sample) process. Despite such limitations, Schwager notes that one redeeming aspect of the optimization process is that it consistently identifies suboptimal parameter sets, and that such parameter sets remain suboptimal throughout the out-of-sample testing process.7
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