The new TradingCalendar method is called `minute_index_to_session_labels`.
It takes a DatetimeIndex of in-order market minutes and returns a
DatetimeIndex of the corresponding sessions.
The new method is approximately 100x faster than mapping
`minute_to_session_label` over a large DatetimeIndex.
In the data portal, remove methods that make a distinction between
future and equity asset type. Instead rely on the pricing reader
dispatching.
In support of incoming work which will upsample equity history arrays to
the larger future calendar.
Also, remove perf tracker tests which were using an equity
reader/writer, to be added back in later.
* First pass.
* Improvements and fixes
- Update usages of BcolzMinuteBarWriter
- Updates with rebuilt example data
- Expose calendar from BcolzMinuteBarMetadata instead of calendar_name
- Keep market_opens and market_closes in metadata for compatibility
* Store start_session and end_session in minute bcolz metadata
- start_session replaces first_trading_day
- Add end_session to limit to correct days
* For last_available_dt, get last close from calendar to maintain tz
* Bumps version and handles earlier versionson read
* Rebuilt example data on python 3
* Indicate metadata fields that are deprecated
Implement a `SessionBarReader` which uses a minute bar reader as a
backing source, resampling the minute bars into the box around the
corresponding session data.
Also, add future/CME test cases to resample suite.
Adds a new ``downsample`` method to all computable terms. Computable
terms (Filters, Factors, and Classifiers) can be downsampled to yearly,
quarterly, monthly, or weekly frequency.
The result of ``term.downsample`` is a new term of the same
family (Filter/Factor/Classifier) as ``term``. The downsampled term
computes by delegating to the original term; repeatedly calling its
``compute`` method with length-1 date ranges.
Downsampled terms take advantage of a new ``compute_extra_rows`` Term
method, which allows terms to dynamically request that additional extra
rows of themselves be computed based on the dates for which they're
being computed. This ensures, for example, that a monthly-downsampled
term always computes at the start of a month, even when a
naively-calculated pipeline window would end in the middle of the month.
Ana Ruelas