Since we definitely want to markup gaps that are both data-errors and
just plain old venue closures (time gaps), generalize the `gaps:
pl.DataFrame` loop in a func and call it from the `ldshm` cmd for now.
Some other tweaks to `store ldshm`:
- add `np.median()` failover when detecting (ohlc) ts sample rate.
- use new `tsp.dedupe()` signature.
- differentiate between sample-period size gaps and venue closure gaps
by calling `tsp.detect_time_gaps()` with diff size thresholds.
- add todo for backfilling null-segs when detected.
Apparently `.storage.nativedb.mk_ohlcv_shm_keyed_filepath()` was always
kinda broken if you passed in a `period: float` with an actual non-`int`
to the format string? Fixed it to strictly cast to `int()` before
str-ifying so that you don't get weird `60.0s.parquet` in there..
Further this rejigs the `sotre ldshm` gap correction-annotation loop to,
- use `StorageClient.write_ohlcv()` instead of hackily re-implementing
it.. now that problem from above is fixed!
- use a `needs_correction: bool` var to determine if gap markup and
de-duplictated data should be pushed to the shm buffer,
- go back to using `AnnotCtl.add_rect()` for all detected gaps such that
they all persist (and thus are shown together) until the client
disconnects.
Obvi took a little `.ui` component fixing (as per prior commits) but
this is now a working PoC for gap detection and markup from a remote
(data) non-`chart` actor!
Iface and impl deats from `.ui._remote_ctl`:
- add new `open_annot_ctl()` mngr which attaches to all locally
discoverable chart actors, gathers annot-ctl streams per fqme set, and
delivers a new `AnnotCtl` client which allows adding annotation
rectangles via a `.add_rect()` method.
- also template out some other soon-to-get methods for removing and
modifying pre-exiting annotations on some `ChartView` 💥
- ensure the `chart` CLI subcmd starts the (`qtloops`) guest-mode init
with the `.ui._remote_ctl` module enabled.
- actually use this stuff in the `piker store ldshm` CLI to submit
markup rects around any detected null/time gaps in the tsdb data!
Still lots to do:
- probably colorization of gaps depending on if they're venue
closures (aka real mkt gaps) vs. "missing data" from the backend (aka
timeseries consistency gaps).
- run gap detection and markup as part of the std `.tsp` sub-sys
runtime such that gap annots are a std "built-in" feature of
charting.
- support for epoch time stamp AND abs-shm-index rect x-values
(depending on chart operational state).
Thinking about just moving all of that module (after a content breakup)
to a new `.piker.tsp` which will mostly depend on the `.data` and
`.storage` sub-pkgs; the idea is to move biz-logic for tsdb IO/mgmt and
orchestration with real-time (shm) buffers and the graphics layer into
a common spot for both manual analysis/research work and better
separation of low level data structure primitives from their higher
level usage.
Add a better `data.history` mod doc string in prep for this move
as well as clean out a bunch of legacy commented cruft from the
`trimeter` and `marketstore` days.
TO CHERRY #486 (if we can)
Yet again these are (going to be) generally useful in the data proc
layer as well as going forward with (possibly) moving the history and
shm rt-processing layer to apache (arrow or other) shared-ds
equivalents.
Includes a rename of `.data._timeseries` -> `.data.tsp` for "time series
processing", making it a public sub-mod; it contains a highly useful set
of data-frame and `numpy.ndarray` ops routines in various subsystems Bo
Think i finally figured out the weird issue without out-of-order OHLC
history getting jammed in the wrong place:
- gap is detected in parquet/offline ts (likely due to a zero dt or
other gap),
- query for history in the gap is made BUT that frame is then inserted
in the shm buffer **at the end** (likely using array int-entry
indexing) which inserts it at the wrong location,
- later this out-of-order frame is written to the storage layer
(parquet) and then is repeated on further reboots with the original
gap causing further queries for the same frame on every history
backfill.
A set of tools useful for detecting these issues and annotating them
nicely on chart part of this patch's intent:
- `dedupe()` will detect any dt gaps, deduplicate datetime rows and
return the de-duplicated df along with gaps table.
- use this in both `piker store anal` such that we potentially
resolve and backfill the gaps correctly if some rows were removed.
- possibly also use this to detect the backfilling error in logic at
the time of backfilling the frame instead of after the fact (which
would require re-writing the shm array from something like `store
ldshm` and would be a manual post-hoc solution, not a fix to the
original issue..
A common usage error is to run `piker anal mnq.cme.ib` where the CLI
passed fqme is not actually fully-qualified (in this case missing an
expiry token) and we get an underlying `FileNotFoundError` from the
`StorageClient.read_ohlcv()` call. In such key misses, scan the existing
`StorageClient._index` for possible matches and report in a `raise from`
the new error.
CHERRY into #486
Changed from the old `store clone` to instead simply load any shm buffer
matching a user provided `FQME: str` pattern; writing to parquet file is
only done if an explicit option flag is passed by user.
Implement new `iter_dfs_from_shms()` generator which allows interatively
loading both 1m and 1s buffers delivering the `Path`, `ShmArray` and
`polars.DataFrame` instances per matching file B)
Also add a todo for a `NativeStorageClient.clear_range()` method.
For OHLCV time series we normally presume a uniform sampling period
(1s or 60s by default) and it's handy to have tools to ensure a series
is gapless or contains expected gaps based on (legacy) market hours.
For this we leverage `polars`:
- add `.nativedb.with_dts()` a datetime-from-epoch-time-column frame
"column-expander" which inserts datetime-casted, epoch-diff and
dt-diff columns.
- add `.nativedb.detect_time_gaps()` which filters to any larger then
expected sampling period rows.
- wrap the above (for now) in a `piker store anal` (analysis) cmd which
atm always enters a breakpoint for tinkering.
Supporting storage client additions:
- add a `detect_period()` helper for extracting expected OHLC time step.
- add new `NativedbStorageClient` methods and attrs to provide for the above:
- `.mk_path()` to **only** deliver a parquet-file path for use in
other methods.
- `._dfs` to house cached `pl.DataFrame`s loaded from `.parquet` files.
- `.as_df()` which loads cached frames or loads them from disk and
then caches (for next use).
- `_write_ohlcv()` a private-sync version of the public equivalent
meth since we don't currently have any actual async file IO
underneath; add a flag for whether to return as a `numpy.ndarray`.
- drop buncha cruft from `store ls` cmd and make it work for
multi-backend fqme listing.
- including adding an `.address` to the mkts client which shows the
grpc socketaddr details.
- change defauls to new `'nativedb'.
- drop 'marketstore' from built-in backend list (for now)
After much frustration with a particular tsdb (cough) this instead
implements a new native-file (and apache tech based) backend which
stores time series in parquet files (for now) using the `polars` apis
(since we plan to use that lib as well for processing).
Note this code is currently **very** rough and in draft mode.
Details:
- add conversion routines for going from `polars.DataFrame` to
`numpy.ndarray` and back.
- lay out a simple file-name as series key symbology:
`fqme.<datadescriptions>.parquet`, though probably it will evolve.
- implement the entire `StorageClient` interface as it stands.
- adjust `storage.cli` cmds to instead expect to use this new backend,
which means it's a complete mess XD
Main benefits/motivation:
- wayy faster load times with no "datums to load limit" required.
- smaller space footprint and we haven't even touched compression
settings yet!
- wayyy more compatible with other systems which can lever the apache
ecosystem.
- gives us finer grained control over the filesystem usage so we can
choose to swap out stuff like the replication system or networking
access.
Turns out just (over)writing `.parquet` files with >= 1M datums is like
less then a second, and we can likely speed up appends using
`fastparquet` (usage coming soon).
Includes:
- a new `clone` CLI subcmd to test this all out by ad-hoc copy of
(literally hardcoded to a daemon-actor specific shm allocation X) an
existing `/dev/shm/<ShmArray>` and push to `.parquet` file.
- code to convert from our `ShmArray.array: np.ndarray` ->
`polars.DataFrame` (thanks SO).
- timing checks around the file IO and np -> polars conversion.
- a `read` subcmd which i was using to test the sync `pymarketstore`
client against our async one to see if the issues from
https://github.com/pikers/piker/issues/443 were resolved, but nope!
Turns out you can mix and match `click` with `typer` so this moves what
was the `.data.cli` stuff into `storage.cli` and uses the integration
api to make it all work B)
New subcmd: `piker store`
- add `piker store ls` which lists all fqme keyed time-series from backend.
- add `store delete` to remove any such key->time-series.
- now uses a nursery for multi-timeframe concurrency B)
Mask out all the old `marketstore` specific subcmds for now (streaming,
ingest, storesh, etc..) in anticipation of moving them into
a subpkg-module and make sure to import the sub-cmd module in our top
level cli package.
Other `.storage` api tweaks:
- drop the reraising with custom error (for now).
- rename `Storage` -> `StorageClient` (or should it be API?).
Tyler Goodlet