Open interest without shortcuts · 4 / 5
Build an open interest profile and state its limits
An OI profile can highlight where increases in outstanding exposure occurred. It cannot automatically tell you where every remaining trader entered. To use the picture responsibly, make the allocation rule visible and preserve what the observations cannot resolve.
Athenum8 minUpdated:
Choose the allocation rule before seeing the outcome
Start with a fixed time range and price bucket size. One transparent teaching method assigns each positive OI change to that interval's volume-weighted price bucket. Another distributes it across traded prices using volume weights. These methods can produce different profiles because OI is sampled over time, while trades occur at many prices between observations.
A net decrease is harder to locate. The feed does not identify which earlier cohort closed. Removing it from the oldest, newest or nearest-price bucket imposes a model rather than revealing a fact. A conservative display can keep gross additions by price and show unallocated reductions separately. Label it as such; do not title it “remaining entries.”
Separate three different distributions
A volume profile measures traded activity by price under its chosen data method. An OI-change profile allocates changes in outstanding exposure. A true inventory cost-basis ledger would require participant-level position history that public aggregate data does not provide. Similar-looking histograms do not make these quantities interchangeable.
Flat OI does not imply that the same cohort remains. Transfers can replace a position holder without changing the total. Openings and closures can also offset within a sample. Therefore even a careful OI profile is a map of an estimation procedure, useful for generating hypotheses but unable to certify who is trapped or where stops are placed.
Keep 90 closures unallocated
During a fixed range, one interval adds 120 contracts around the 100 price bucket and another adds 180 around 102. A later observation shows a decline of 90 contracts, with no information identifying their original entries. Gross observed additions are 300; net growth is 210.
The defensible profile shows 120 and 180 additions, plus a separate −90 reduction. Subtracting all 90 from the 100 bucket would leave 30 there; subtracting them from the 102 bucket would leave 90 there. Both are possible model choices, but neither is established by this dataset. Retain the ambiguity when planning a retest.
| Observation | Allocated additions | Unallocated reduction |
|---|---|---|
| Around price 100 | 120 contracts | 0 |
| Around price 102 | 180 contracts | 0 |
| Later OI decrease | 0 | 90 contracts |
| Total | 300 contracts | 90 contracts |
- 100 bucket additions
- 120 contracts
- 102 bucket additions
- 180 contracts
- Unallocated reduction
- -90 contracts
The prettiest profile may be the most hindsight-dependent
Changing the range start until a peak lines up with the eventual reversal selects the answer after the fact. Save the range, bucket width, venue set and allocation method at decision time. Then inspect how robust the level remains under nearby reasonable settings. A hypothesis that disappears after a small boundary change deserves less confidence.
Before acting
- Fix the time window, bucket width and OI unit.
- State how interval changes are assigned to prices.
- Show how reductions are handled and what remains unknown.
- Keep volume, estimated additions and surviving cost basis distinct.
- Save settings before testing the next price response.
Check your understanding
The profile shows 300 gross additions and 90 unallocated reductions. Can you conclude that 210 of the original new contracts remain open?
Show the explained answer
You can conclude that net OI grew by 210 over the window, assuming complete and consistent observations. You cannot identify which positions survived. Transfers, closures of older positions and offsetting openings/closings mean the original new cohort is not recoverable from those totals.