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2017issue C0914-17

Two-wave monthly cycles as a regime filter

Over a 20-year SPY sample, first-of-month-indexed closes and average daily volume both formed two upward waves, near mid-month and near month-end. TradersWeek's editorial reading is to confirm that shared shape first, then keep the late-month window only as a monthly-cycle-filter in front of a short-lookback mean-reversion model.

  • First-of-month-indexed SPY closes and average daily volume both showed two intra-month upward waves, one near mid-month and one near month-end, on calendar-date and trading-date axes.
  • The cash-flow-rebalancing-hypothesis treats scheduled pension-fund inflows, a few days of rebalancing, no shorting, and a substantial equity sleeve as the reason those price and volume waves line up.
  • Long and short day-hold-cluster maps placed late-month buying on calendar days 20-28 and placed opposing short clusters earlier in each half of the month.
  • TradersWeek's editorial reading is to keep that late window as an abstention layer in front of short-lookback relative-strength-index and bollinger-bands entries, and to leave a five-day breakout in the same test as a mismatch check.
Entries in this reading3 entries
Seasonal trading60 readingsCurrent trackBollinger Bands84 readingsRelative Strength Index186 readings

Price and volume share the same intra-month shape

Over a 20-year SPY sample, first-of-month-indexed closes showed two upward waves, one near mid-month and one near month-end. The same two-wave shape appeared on both the calendar-date axis and the trading-date axis.

Average SPY daily volume over that same 20-year span also formed two intra-month waves aligned with the price waves. Lunar-phase explanations were set aside because they did not account for a two-wave intra-month price shape.

The cash-flow-rebalancing-hypothesis

The working mechanism was scheduled pension-fund cash inflows, a few days to rebalance, no shorting, and a substantial equity sleeve described as generally 20% to 80%. That timetable is the cash-flow-rebalancing-hypothesis: scheduled institutional inflows force equity purchases on a twice-monthly timetable and therefore leave aligned price and volume footprints.

Day-hold-cluster maps from a seasonal-trading grid

Seasonal-trading is a single testable procedure that treats day-of-month location as a rule input so entry, holding period, and abstention are evaluated together.

A long grid that bought the close on each calendar day and held one to eight days produced two day-hold-cluster groups: days 7-12 with four-to-eight-day holds, and days 20-28 with five-to-eight-day holds.

A short grid that sold the close on each calendar day and held one to eight days produced two day-hold-cluster groups: days 2-7 with two-to-six-day holds, and days 15-23 with one-to-five-day holds.

A monthly-cycle-filter in front of short-lookback entries

Three eight-day-hold entries were then applied to 20 years of SPY daily data. Relative-strength-index is a short-lookback bounded oscillator of closes; the version used here bought when the five-observation reading dropped below 30. Bollinger-bands is a volatility envelope around a moving average; the version used here bought on a cross through the lower band of lookback 10 and width 2, used here as a strength-confirmation buy condition. The third entry was a close through the prior five-day high.

Those same entries were also tested with a monthly-cycle-filter that allowed trades only from the 20th through the 28th of the month. A monthly-cycle-filter is a calendar constraint that lets an otherwise complete entry rule fire only inside pre-specified day-of-month windows.

TradersWeek's editorial reading is to use those windows only as an abstention layer in front of the short-lookback mean-reversion model.

A breakout rule as a mismatch check

The five-day breakout was retained as a contrast because a broad average was described as mean-reverting.

TradersWeek's editorial reading is to keep that breakout rule in the same test as a mismatch check on an averaged instrument.

Cycle filter versus no filter: average trade on 20-year SPY tests

Keeping only entries from calendar days 20–28 raised average trade on both short-lookback mean-reversion rules and only barely pulled the five-day breakout above zero. A trader should treat the late-month window as an abstention layer, not as a standalone signal: RSI and the Bollinger rule improve, while the breakout stays a weak mismatch check on an averaged instrument. The percentages are the article’s own stated Figure 6 backtest results, not a tracing of the printed bars.
Keeping only entries from calendar days 20–28 raised average trade on both short-lookback mean-reversion rules and only barely pulled the five-day breakout above zero. A trader should treat the late-month window as an abstention layer, not as a standalone signal: RSI and the Bollinger rule improve, while the breakout stays a weak mismatch check on an averaged instrument. The percentages are the article’s own stated Figure 6 backtest results, not a tracing of the printed bars.SPY · daily, 20-year sample

The cycle filter limits entries to the 20th through the 28th of the month; every rule holds eight days. Figure 6 labels the middle rule Bollinger Band (20,2) even though the trading-plan section specifies (10,2).

Educational research material, not investment advice. Historical source context does not establish present-day performance.
42 of 45 in the Bollinger Bands track
201948-56 pp.Next on Bollinger BandsConstructing exponential-deviation-bands from a midline-averageThe envelope is centered on a midline-average of close, simple or exponential, over a chosen lookback, and the first width measure is the absolute distance from that midline to the latest close.
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
By fieldSeasonality and regimes93 readingsBy year2017100 readings
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