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2016issue C0146-56

Trend-channel entry rules from stacked moving averages

A 50-day market filter, stacked moving-average alignment, and an 8-day high-low channel can be written as one Rule-based entry procedure. The same conditions can screen names and time the trade.

  • A 50-day simple moving average on a broad market proxy can sort candidate stocks into uptrend or downtrend setups before any entry is considered.
  • Long-side screening can require price above its 50-day average, a 20-day average above the 50-day average, and a 50-day average above the 200-day average.
  • Long entry and exit can sit on a two-line channel from an 8-day average of highs and an 8-day average of lows.
  • Turning the market-trend screen or the 50-versus-200 alignment off changes the trade list and can block an entire class of short entries.
Entries in this reading3 entries
Current trackBollinger Bands84 readingsRule-based entry174 readingsMoving average449 readings

One testable procedure

The archive workflow uses a market-direction filter, stacked moving-average alignment, and a two-line channel to decide which stocks are eligible and when a long trade is opened or closed.

Editorial: treat those pieces as one Rule-based entry procedure rather than as three separate ideas. Candidate selection and trade execution then stay on the same conditions.

Market direction and stacked averages

A 50-day simple moving average on a broad market proxy can be used as the first filter for whether candidate stocks are treated as uptrend or downtrend setups.

Long-side screening can require price above its 50-day average, a 20-day average above the 50-day average, and a 50-day average above the 200-day average.

The same stacked-average alignment can be inverted for short-side setups: a 20-day average below the 50-day average and a 50-day average below the 200-day average, only when the market-direction filter is negative.

Channel entry and exit

Entry and exit can be reduced to a two-line channel built from an 8-day average of highs and an 8-day average of lows, with long entry on a close above the upper line and long exit when price undercuts the lower line.

Shared rules and optional filters

The procedure can be coded as both a scanner and indicator and a backtestable strategy so that candidate selection and trade execution are the same rule set.

Volume can be added as a liquidity screen, such as requiring 50-day average volume greater than one million shares before a name is eligible.

Turning individual setup filters on or off, including the market-trend screen and the 50-versus-200 average alignment, changes which trades appear and can block an entire class of short entries.

Average trade result versus the NASDAQ 100, 2000–2015

The combined 50-day market filter, stacked 20/50/200 averages, and eight-day high-low channel made 2.76 percent on the average trade from 2 January 2000 through 6 November 2015, against 1.37 percent for the NASDAQ 100 on the same dates. Winners averaged 7.12 percent and losers minus 17.59 percent. Every figure is taken from the AIQ account-statistics table for that test.
The combined 50-day market filter, stacked 20/50/200 averages, and eight-day high-low channel made 2.76 percent on the average trade from 2 January 2000 through 6 November 2015, against 1.37 percent for the NASDAQ 100 on the same dates. Winners averaged 7.12 percent and losers minus 17.59 percent. Every figure is taken from the AIQ account-statistics table for that test.NASDAQ 100 constituents · 2 January 2000 to 6 November 2015 · 2000-01-02T00:00:00.000Z to 2015-11-06T00:00:00.000Z

The AIQ run added an 80 percent profit-protect exit once a position was up 5 percent, used NASDAQ 100 names, and applied the authors’ band and trend filters plus extra exits not in the original article.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
38 of 45 in the Bollinger Bands track
201617-20 pp.Next on Bollinger BandsA permission stack for Bollinger, RSI, and the 50-period averageFour band-RSI pairings treat outer-band contact plus an RSI reading relative to 30 or 70 as the joint entry conditions.
All readings on this track · 45 readings
  1. 1992Constructing volatility-scaled bands with relative strength index confirmation
  2. 1994Implied volatility as a band-defined regime filter for index options
  3. 1995Constructing projection bands from least-squares slopes
  4. 1995Constructing regression projection bands and range oscillators
  5. 1996Constructing Bollinger bands, percent-b, and stochastics
  6. 1996Constructing mechanical rules from Bollinger Bands and stochastics
  7. 1996Constructing a standard-error envelope around a linear regression
  8. 1996Dual-horizon ratio envelopes and regression error channels
  9. 1997Rational group structure with a trend screen, RSI, and bands
  10. 1997Asymmetric volatility band construction
  11. 1998Constructing three-state filters from Bollinger band envelopes
  12. 1999Combination filters with Bollinger Bands and the relative strength index
  13. 1999Constructing stochastic timed exits and band-RSI reversals
  14. 1999Evaluating Bollinger Bands against fixed-width and range-based envelopes
  15. 2000Constructing a Bollinger Band target as a forward price
  16. 2001Numeric candlestick encoding with local size bands
  17. 2001Ranked candlestick sentiment to band-cross entries
  18. 2002Combining Bollinger Bands, RSI, and a stop-loss
  19. 2002Bollinger Bands remain filters, not forecasts
  20. 2002Constructing a stochastic RSI with Bollinger bands
  21. 2002Constructing a StochRSI and Bollinger mechanical system
  22. 2003Constructing volatility-scaled Bollinger envelopes
  23. 2003Why tick breadth fails as a market personality
  24. 2005Constructing Bollinger bands versus fixed trading bands
  25. 2006Squared versus absolute deviation in envelope construction
  26. 2006Confirming yen crossovers with implied volatility and bands
  27. 2006A daily candle reversal is a hypothesis until shorter sessions fail at the same zone
  28. 2008Rebuild the Relative Strength Index as price-scale bands
  29. 2008Reading Relative Strength Index extremes on one price axis with Bollinger Bands and moving averages
  30. 2011Three-filter confirmation for short-swing futures
  31. 2011Constructing an inverse Fisher stochastic with bands and averages
  32. 2012Constructing a Bollinger Band indicator suite
  33. 2012Stacking price extremes, crossovers, bands, and MACD
  34. 2012Adaptive Bollinger band impulse, trend, and momentum filters
  35. 2013Rescaling stochastic, percent-B, and wave-count parameters
  36. 2014Industry-group quartile pivots as a Bollinger Bands case study
  37. 2014Bollinger Bands as adaptive price envelopes: a 2014 classroom case
  38. 2016Trend-channel entry rules from stacked moving averages
  39. 2016A permission stack for Bollinger, RSI, and the 50-period average
  40. 2017Constructing weighted Bollinger bands and volume averages
  41. 2017Four swing-entry rules that share a timed exit
  42. 2017Two-wave monthly cycles as a regime filter
  43. 2019Constructing exponential-deviation-bands from a midline-average
  44. 2020Critiquing exponential variants of Bollinger Bands
  45. 2020Constructing selectable volatility and moving-average bands
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