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1990issue C031-12

Diversify markets, not systems, to cut trend-system variance

A historical portfolio simulation applied five technical procedures, including a Price channel and a dual Moving-average crossover, to seven nearby futures series. Adding markets reduced profit variance more than adding similar Trend following procedures, whose aggregate monthly returns were highly correlated.

  • A historical simulation applied five technical procedures, including a Price channel and a dual Moving-average crossover, to seven nearby futures series that expanded from two markets in 1960 to equal weights by 1977.
  • Parameters were held constant across markets and time. The workflow blocked locked-limit opens, forbade pyramiding, rolled the nearby contract on the first trading day of the expiration month, and treated equity drawdowns as recapitalized.
  • Aggregating seven markets reduced return variability and narrowed monthly extremes, while aggregate monthly returns of the dual Moving-average crossover and the Price channel were correlated at 0.83.
  • The study concluded that adding markets reduces profit variance more than adding similar Trend following procedures because those procedures' returns are highly correlated.
Entries in this reading3 entries
Current trackPrice channel77 readingsTrend following89 readingsMoving-average crossover108 readings

A historical portfolio test

A historical portfolio simulation applied five technical procedures, including a Price channel and a dual Moving-average crossover, to seven nearby futures series. The portfolio expanded from two markets in 1960 to equal weights by 1977.

Parameters were held constant across markets and time rather than optimized. The stated grounds were that long-horizon results differ little among nearby moving-average pairs, and that longer settings tend to produce slightly higher net results through fewer trades.

How the simulation was run

The simulation blocked entries and exits on locked-limit opens, forbade pyramiding, rolled the nearby contract on the first trading day of the expiration month, and treated equity drawdowns as recapitalized.

Returns were computed on total allocated capital of 30 percent initial margin plus 70 percent reserve, using assumed historical margin rates that differed by market.

Returns, variability, and losing years

Across the 35 market-system combinations, mean annual percent returns averaged 65 percent before a 100-dollar round-turn cost assumption and about 30 percent after that deduction. Even the stronger procedures were negative in about one year in five on the seven-market aggregate.

Commodity-level mean annual percent returns ranged from 77 percent for Deutschemarks to 42 percent for silver. Ranking by coefficient of variation favored corn and sugar over Deutschemarks and cattle because the latter pair had higher return variability.

Among the five procedures, directional parabolic showed the highest mean annual percent return at 100 percent and the lowest coefficient of variation at 67. The Price channel and dual Moving-average crossover posted 70 percent and 63 percent, with coefficients of variation of 74 and 94.

Monthly evidence and single-market extremes

Monthly returns were positive and statistically distinguishable from zero at the 5 percent level in 20 of 35 individual market-system cases and in every aggregate case. Single-market extremes included a channel monthly low of -419 percent and a filter-rule monthly high of 322 percent, both in soybeans.

What aggregation changed

Aggregating seven markets reduced return variability. The Relative Strength Index coefficient of variation dropped from an individual-market average of 1236 to 499. The directional-parabolic coefficient of variation dropped from 610 to 242. Overall monthly extremes narrowed from -419 percent and 322 percent to -46 percent and 139 percent.

Aggregate monthly returns of the dual Moving-average crossover and the Price channel were correlated at 0.83. The study concluded that adding markets reduces profit variance more than adding similar Trend following procedures because those procedures' returns are highly correlated.

Annual return variability by market versus the seven-futures book

A 70-percent-class annual return on corn or cattle still came with a coefficient of variation of 142 to 218. T-bills were worse: a 46 percent average against a 457 coefficient. Booking the same five trend rules across all seven nearby futures cut that coefficient to 95. Values are the commodity-average column of the authors' 1960–86 annual-return table, not a re-average of the five system rows.
A 70-percent-class annual return on corn or cattle still came with a coefficient of variation of 142 to 218. T-bills were worse: a 46 percent average against a 457 coefficient. Booking the same five trend rules across all seven nearby futures cut that coefficient to 95. Values are the commodity-average column of the authors' 1960–86 annual-return table, not a re-average of the five system rows.Nearby futures: corn, soybeans, sugar, silver, live cattle, T-bills, Deutsche mark · Annual · 1960-01-01T00:00:00.000Z to 1986-12-31T00:00:00.000Z

Parameters were typical trader settings held fixed across markets and years, not optimized in sample. Each series starts when that contract is available (corn and soybeans in 1960; sugar and silver in 1964; live cattle in 1966; T-bills and the Deutsche mark in 1977), so the aggregate is time-weighted—half corn and half soybeans in 1960, one-seventh each from 1977—not a simple mean of the rows above it. Figures are gross of a $100 round-turn charge, which the authors say would cut the related mean returns by about half.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
9 of 55 in the Price channel track
19911-10 pp.Next on Price channelConstructing trendlines, price channels, and close-based breakoutsDraw an uptrend demand line through at least two, and preferably three, rising lows, and a downtrend supply line through descending highs.
All readings on this track · 55 readings
  1. 1988Constructing price channels from trendlines
  2. 1988Three-point curved trend channel construction
  3. 1988Least-squares construction of channel trendlines
  4. 1988Three-zone price channel from quadratic smoothing
  5. 1989A variable-sensitivity stochastic built on three-sigma bounds
  6. 1989Close-minus-average oscillator for channel extremes
  7. 1989The six-stage hunt as a critique of one-click heroics
  8. 1990Fair-value gaps and a copper moving-average channel
  9. 1990Diversify markets, not systems, to cut trend-system variance
  10. 1991Constructing trendlines, price channels, and close-based breakouts
  11. 1991Constructing seasonal-cycle overlays with channel confirmation
  12. 1993Lag-compensated exponential trend channel construction
  13. 1993Constructing a lead-lag filter and price channel as one stack
  14. 1993Three stochastic warnings still need price-channel confirmation
  15. 1993Lead-lag smoothing for weekly trend-channel construction
  16. 1993Constructing zero-net-lag price channels
  17. 1995From a downtrend-line break to a regression channel
  18. 1995Validated trendline and price channel construction
  19. 1995Constructing price envelopes from averages, volatility, and regression
  20. 1996Constructing trendlines and channels from explicit swings
  21. 1998Fifty percent retracement as a channel regime test
  22. 1998Close-based channel rails as daily scenario maps
  23. 1999Constructing support, resistance, trendlines, and price channels
  24. 2001Cycle composites, price channels, and two-sided signals
  25. 2001Testing horizontal price channels with stops and scale
  26. 2002A two-stage momentum-shift and price-channel process
  27. 2002Wave-by-wave channel construction for Elliott counts
  28. 2002Affine channels as reusable trade hypotheses
  29. 2004Stress-test seasonal windows across regimes, then add channels
  30. 2004Regime permission from trendlines, channels, and range edges
  31. 2004Weekly-average and price-channel states on sector depositary baskets
  32. 2005Oil services catch-up after channel resistance breaks
  33. 2005Constructing a volatility-normalized cycle index
  34. 2005How a Darvas channel becomes a complete entry and exit procedure
  35. 2005Clustered Fibonacci and channel levels in news-driven forex
  36. 2005Treat a consolidating currency market as a time-frame problem
  37. 2005Channel walls that flip roles or recapture price
  38. 2006Stacking candlesticks, crossovers, and price channels
  39. 2006Failed uptrend channel breakout left the euro rangebound
  40. 2006Constructing a Wilson relative price channel from a range-bound strength index
  41. 2007Range bars change when a Bollinger squeeze counts as a breakout
  42. 2009One testable SPY procedure for a price channel, a trend rule, and a seasonal overlay
  43. 2010A gold-miner channel plan from value to false breakouts
  44. 2010A multi-timeframe channel from value to an overvalued zone
  45. 2010Asymmetric price channel construction for congested markets
  46. 2011Phasing many cycles at once with nested envelopes
  47. 2012Constructing adaptive horizontal price channels
  48. 2014Confirming support with trendlines, channels, and retracements
  49. 2015News-sentiment confirmation for support, channel, and volume tests
  50. 2015A three-layer permission stack: moving averages, a price channel, and weekly levels
  51. 2016Entropy-diff as a regime switch between trend following and a price channel
  52. 2017Competing rulers on a pound chart after Brexit
  53. 2017Test consolidation channel breakouts as one procedure
  54. 2020Constructing late-trend longs with a price channel, gap breakout, and trailing stop
  55. 2025Using IBM's multi-year price channel as a breakout teaching case
By fieldSystem design117 readingsBy year199092 readings
All 77 readings tagged Price channel
Also on Price channel5 readings
2016Entropy-diff as a regime switch between trend following and a price channel1990Fair-value gaps and a copper moving-average channel1992Channel-height ratios for equity trend evaluation2002A two-stage momentum-shift and price-channel process2009One testable SPY procedure for a price channel, a trend rule, and a seasonal overlay