How to incrementally predict for incremental data?
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Description
When new Kline data becomes available, I push it to the local dataset file.
However, how can I incrementally predict the new coming data instead of recreating the whole relevant qlib instances?
I know qlib has some caching mechanisms, such as D.calendar or D.feature.
Can I do just like this? I'm unsure if qlib requires any warmup bars.
ds = DatasetH(handler, segments={"test": (new_start_date, new_end_date)})
pred = self.model.predict(ds)
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Research direction
Start by reading the qlib DatasetH, D.calendar, D.feature, and model.predict entry points referenced in the issue. Determine how incremental Kline data, caching, and warmup bars are handled, then document the supported incremental-prediction workflow and its completion criteria.
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Assessment
- Tech stack
- python
- Domain
- data, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100