Skip to main content
Track Fourier analysis
25 / 26
Search the archiveLibrary
Library

2020issue C048-13

Constructing futures turnover time and urgency

This article reconstructs a historical futures workflow that turns open interest, volume, and a smoothed price path into inertia, momentum, turnover time, and a signed, dimensionless urgency series.

  • Inertia is total reported open interest across all contract months, equal to the number of longs and to the number of shorts, and is treated as resistance to starting or stopping a move.
  • Momentum multiplies open interest, quantity per contract, and a daily price change from a Fourier differential or another smoothed price curve, and is always expressed in dollars per day.
  • Turnover time is open interest divided by trading volume and is read as the number of days required for existing positions to recycle at the observed activity level.
  • Urgency judges that dollar momentum relative to turnover time, producing a signed, dimensionless series of how pressing it is for current longs or shorts to exit.
Entries in this reading3 entries
Current trackFourier analysis41 readingsExponential smoothing183 readingsTrend filter184 readings

Assembling the four series

The historical workflow starts from reported futures open interest, trading volume, and an ordered price series. From those inputs it constructs inertia, momentum, turnover time, and urgency.

Inertia is defined as total reported open interest across all contract months. That total is equal to the number of longs and also to the number of shorts. Open interest is treated as the market's resistance to starting or stopping a move.

Momentum in dollars per day

Market momentum is constructed as open interest times quantity per contract times the daily price change. The product always has units of dollars per day, the amount transferring between longs and shorts.

The signed rate of change is taken from a trend filter, the smoothed price path that keeps the direction of the move in both momentum and urgency.

Obtaining the daily price change

The daily price change used in momentum can be taken from a Fourier differential or from the derivative of another smoothed price curve.

Fourier analysis is a smoothing and differentiation method used to obtain a lagged-reduced daily price change from an ordered price series.

Exponential smoothing is an alternative smoother whose output can supply the daily price change when a Fourier differential is not used. Common exponential and other smoothers introduce lag and are less readily differentiated than a Fourier series, which affects how cleanly an urgency series can be built in practice.

Turnover time from open interest and volume

Turnover time is defined as open interest divided by trading volume. It is interpreted as the number of days required for open interest to recycle at the observed activity level, which is also the number of days needed for existing positions to recycle at the current activity level.

In a 256-trading-day euro FX sample, turnover time varied between one and four days and generally peaked when trading volume was smallest.

Urgency as momentum relative to turnover time

Urgency is constructed so that momentum is judged relative to turnover time. In this workflow, urgency is momentum scaled by turnover time. The result is a signed, dimensionless series that measures how pressing it is for current longs or shorts to exit.

In the euro FX example, that series oscillated by about 0.6, or 60 percent.

Educational research material, not investment advice. Historical source context does not establish present-day performance.
25 of 26 in the Fourier analysis track
20208-13 pp.Next on Fourier analysisStock power ratio as a growth trend filterA growth-forecast baseline maps a growth-rate input and an interpolated earnings path into a multi-year price-change projection and is used only as a quantitative baseline, not as a valuation verdict.
All readings on this track · 26 readings
  1. 1982Critiquing Fourier, Elliott, and seasonal commodity models
  2. 1982Evaluating half-hour crossovers with cycle priors and stops
  3. 1984Building moving-window spectral cycle forecasts
  4. 1988Fourier cycles versus calendar seasonal regimes
  5. 1989Constructing analog cycle signatures from price lows
  6. 1990Curve fits fail the forecast test
  7. 1991Auditing moving-average length with Fourier spectra
  8. 1991Closed-form phase for dominant-cycle spectra
  9. 1993Evaluating windowed spectral forecasts of index turning points
  10. 1994Window length for moving-window spectral forecasts
  11. 1994Constructing WAMI power and phase spectra
  12. 1994Fourier-tuned momentum filter design
  13. 1994Aliasing, leakage, and dominant-cycle reads from sampled prices
  14. 1995Constructing indicator lookbacks from Fourier spectra
  15. 1996Constructing phase from a dominant-cycle sine and cosine pair
  16. 1999Sinusoidal pseudo-securities for indicator evaluation
  17. 2000Constructing dominant cycle length from Hilbert phase
  18. 2000Constructing adaptive channel breakouts from a measured cycle period
  19. 2005Embedding forex prices into a singular-spectrum forecast basis
  20. 2005Constructing recurrent forecasts from grouped spectral components
  21. 2007Constructing a dominant cycle from transformed Fourier spectra
  22. 2010Why cycle indicators fail when parameters shift
  23. 2019Constructing futures momentum as open-interest-scaled rate of change
  24. 2019Judging a dominant cycle and a trend filter by sample, lag and the move they keep
  25. 2020Constructing futures turnover time and urgency
  26. 2020Stock power ratio as a growth trend filter
By fieldIndicators and filters874 readingsBy year2020165 readings
All 41 readings tagged Fourier analysis
Also on Fourier analysis5 readings
1999Sinusoidal pseudo-securities for indicator evaluation2019Judging a dominant cycle and a trend filter by sample, lag and the move they keep2020Stock power ratio as a growth trend filter1982Building FFT spectra to size cycle filters1982Critiquing Fourier, Elliott, and seasonal commodity models