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1984issue C021-13

Evaluating mechanical systems beyond net profit

Mechanical systems turn price-tracking rules into buy, sell, hold, or exit plans. After parameter combinations are ranked, evaluation still has to define what best means, read the capital path, and check whether top sets cluster.

  • Mechanical trading systems existed before computers and generate buy, sell, hold, or exit plans from price-tracking rules, including moving-average trend followers.
  • Optimization ranks many parameter combinations on a chosen measure, but highest net profit, highest probability of a profitable trade, and lowest risk-to-return are different evaluation targets.
  • Total net profit omits the path of losses, drawdowns, and margin calls, has no direct link to the real rate of return, and is not comparable across periods of unequal length.
  • After ranking, inspect whether top combinations cluster, estimate capital needs from consecutive gain and loss strings and cumulative profit plots, and consider combining several criteria.
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Mechanical rules as a complete procedure

Mechanical trading systems existed before computers. They generate buy, sell, hold, or exit plans from price-tracking rules, including moving-average trend followers.

The rules are predetermined and applied without discretionary override during the test or the trade.

Computerizing mechanical rules reduces calculation and transcription error and can include bounds checks on incoming data. Computerization does not remove doubt that a system has stopped working after consecutive losses.

Optimization ranks combinations

Optimization consists of testing many parameter combinations, such as moving-average length, so the computer can rank which combination performed best on a chosen measure.

Static analysis searches a long series, described as at least three years, for one parameter set expected to remain useful. Dynamic analysis reoptimizes more often, generally over less than one year, because market character may change.

Define what best means

What counts as best must be defined explicitly, because highest net profit, highest probability of a profitable trade, and lowest risk-to-return are different evaluation targets.

Five core evaluation criteria are total net profit, average net profit, the coefficient of variation of average net profit, the risk-to-return ratio, and the probability of a profitable trade. These five expand into eighteen evaluative concepts.

Average profit, variation, and capital path

Average net profit, also called the expected value of a trade, supports comparison across unequal periods. A high average can come from few large trades and can be distorted by outliers.

The coefficient of variation of average net profit is formed by dividing the standard deviation of the mean by the mean. It is interpreted as risk relative to average return.

After ranking parameter sets, evaluation should inspect whether top combinations cluster in similar ranges. Capital needs can be estimated from consecutive gain and loss strings and cumulative profit plots. Several criteria may be combined into a composite measure.

What the ranking does not finish

TradersWeek editorial: clustering of top parameter sets, inspection of the capital path, and a composite of several criteria belong to the design of a testable system. They are not optional notes attached after a profit ranking.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
1 of 23 in the System optimization track
19871-8 pp.Next on System optimizationMechanical systems need more than pretty chartsA mechanical trading system is a fully specified set of entry, exit, and abstention rules that can be tested as one procedure under stated costs and constraints.
All readings on this track · 23 readings
  1. 1984Evaluating mechanical systems beyond net profit
  2. 1987Mechanical systems need more than pretty charts
  3. 1987Evaluating silver moving averages and RSI by scoring rule
  4. 1988Simple systems over optimized complexity
  5. 1988Cycle rule inputs until the parameter search stops
  6. 1990Split-sample tests for stochastic parameter grids
  7. 1993Four-subsystem trading system construction
  8. 1993Randomized price paths for stock system parameters
  9. 1994Mechanical timing with out-of-sample and filter checks
  10. 1995Evaluating asymmetric percent-reversal trading rules
  11. 1996Evaluating a daily volume-strength and new-high new-low timing procedure
  12. 1997Rule-based intraday system design with genetic search
  13. 1997Evolving three-rule systems with genetic templates
  14. 1997Audit the holdout and the exposure before the fitted entries
  15. 2001Inner weights are part of the candlestick signal
  16. 2003Mechanical short-term rules for news-sensitive rallies
  17. 2004Band construction as a testable trading procedure
  18. 2006Layered day-session automation from brackets to rules
  19. 2006Why mechanical rules must be retuned per security
  20. 2006A linear-regression next-bar forecast needs a searchable cutoff
  21. 2015Construct if-then rules, then backtest and optimize
  22. 2015Mechanical system design as a critique of discretion
  23. 2025Joint channel design with a trend filter and stop-loss orders
All 131 readings tagged System optimization
Also on System optimization5 readings