First allocation vs. first "prepend" of source data to an xy `ndarray`
format **must be mutex** in order to avoid a double prepend.
Previously when both blocks were executed we'd end up with
a `.xy_nd_start` that was decremented (at least) twice as much as it
should be on the first `.format_to_1d()` call which is obviously
incorrect (and causes problems for m4 downsampling as discussed below).
Further, since the underlying `ShmArray` buffer indexing is managed
(i.e. write-updated) completely independently from the incremental
formatter updates and internal xy indexing, we can't use
`ShmArray._first.value` and instead need to use the particular `.diff()`
output's prepend length value to decrement the `.xy_nd_start` on updates
after initial alloc.
Problems this resolves with m4:
- m4 uses a x-domain diff to calculate the number of "frames" to
downsample to, this is normally based on the ratio of pixel columns on
screen vs. the size of the input xy data.
- previously using an int-index (not epoch time) the max diff between
first and last index would be the size of the input buffer and thus
would never cause a large mem allocation issue (though it may have
been inefficient in terms of needed size).
- with an epoch time index this max diff could explode if you had some
near-now epoch time stamp **minus** an x-allocation value: generally
some value in `[0.5, -0.5]` which would result in a massive frames and
thus internal `np.ndarray()` allocation causing either a crash in
`numba` code or actual system mem over allocation.
Further, put in some more x value checks that trigger breakpoints if we
detect values that caused this issue - we'll remove em after this has
been tested enough.
Turns out we can't seem to avoid the artefacts when click-drag-scrolling
(results in weird repeated "smeared" curve segments) so just go back to
the original code.
Ensures that a "last datum" graphics object exists so that zooming can
read it using `.x_last()`. Also, disable the linked region stuff for now
since it's totally borked after flipping to the time indexing.
Since we don't really need it defined on the "chart widget" move it to
a viz method and rework it to hell:
- always discard the invalid view l > r case.
- use the graphic's UPPX to determine UI-to-scene coordinate scaling for
the L1-label collision detection, if there is no L1 just offset by
a few (index step scaled) datums; this allows us to drop the 2x
x-range calls as was hacked previous.
- handle no-data-in-view cases explicitly and error if we get any
ostensibly impossible cases.
- expect caller to trigger a graphics cycle if needed.
Further support this includes a rework a slew of other important
details:
- add `Viz.index_step`, an idempotent computed, index (presumably uniform)
step value which is needed for variable sample rate graphics displayed
on an epoch (second) time index.
- rework `Viz.datums_range()` to pass view x-endpoints as first and last
elements in return `tuple`; tighten up snap-to-data edge case logic
using `max()`/`min()` calls and better internal var naming.
- adjust all calls to `slice_from_time()` to not expect an "abs" slice.
- drop all `.yrange` resetting since we can just have the `Renderer` do
it when necessary.
If we presume that time indexing using a uniform step we can calculate
the exact index (using `//`) for the input time presuming the data
set has zero gaps. This gives a massive speedup over `numpy` fancy
indexing and (naive) `numba` iteration. Further in the case where time
gaps are detected, we can use `numpy.searchsorted()` to binary search
for the nearest expected index at lower latency.
Deatz,
- comment-disable the call to the naive `numba` scan impl.
- add a optional `step: int` input (calced if not provided).
- add todos for caching binary search results in the gap detection
cases.
- drop returning the "absolute buffer indexing" slice since the caller
can always just use the read-relative slice to acquire it.
When we use an epoch index and any sample rate > 1s we need to scale the
"number of bars" to that step in order to place the view correctly in
x-domain terms. For now we're calcing the step in-method but likely,
longer run, we'll pull this from elsewhere (like a ``Viz`` attr).
Gives approx a 3-4x speedup using plain old iterate-with-for-loop style
though still not really happy with this .5 to 1 ms latency..
Move the core `@njit` part to a `_slice_from_time()` with a pure python
func with orig name around it. Also, drop the output `mask` array since
we can generally just use the slices in the caller to accomplish the
same input array slicing, duh..
We need to subtract the first index in the array segment read, not the
first index value in the time-sliced output, to get the correct offset
into the non-absolute (`ShmArray.array` read) array..
Further we **do** need the `&` between the advance indexing conditions
and this adds profiling to see that it is indeed real slow (like 20ms
ish even when using `np.where()`).
Again, to make epoch indexing a flip-of-switch for testing look up the
`Viz.index_field: str` value when updating labels.
Also, drops the legacy tick-type set tracking which we no longer use
thanks to the new throttler subsys and it's framing msgs.
Planning to put the formatters into a new mod and aggregate all path
gen/op helpers into this module.
Further tweak include:
- moving `path_arrays_from_ohlc()` back to module level
- slice out the last xy datum for `OHLCBarsAsCurveFmtr` 1d formatting
- always copy the new x-value from the source to `.x_nd`
This was a major cause of error (particularly trying to get epoch
indexing working) and really isn't necessary; instead just have
`.diff()` always read from the underlying source array for current
index-step diffing and append/prepend slice construction.
Allows us to,
- drop `._last_read` state management and thus usage.
- better handle startup indexing by setting `.xy_nd_start/stop` to
`None` initially so that the first update can be done in one large
prepend.
- better understand and document the step curve "slice back to previous
level" logic which is now heavily commented B)
- drop all the `slice_to_head` stuff from and instead allow each
formatter to choose it's 1d segmenting.
In an effort to make it easy to override the indexing scheme.
Further, this repairs the `.datums_range()` special case to handle when
the view box is to-the-right-of the data set (i.e. l > datum_start).
As in make the call to `Flume.slice_from_time()` to try and convert any
time index values from the view range to array-indices; all untested
atm.
Also drop some old/unused/moved methods:
- `._set_xlimits()`
- `.bars_range()`
- `.curve_width_pxs()`
and fix some `flow` -> `viz` var naming.
Don't expect values (array + slice) to be returned and applied by
`.incr_update_xy_nd()` and instead presume this will implemented
internally in each (sub)formatter.
Attempt to simplify some incr-update routines, (particularly in the step
curve formatter, though most of it was reverted to just a simpler form
of the original implementation XD) including:
- dropping the need for the `slice_to_head: int` control.
- using the `xy_nd_start/stop` index counters over custom lookups.
Remove harcoded `'index'` field refs from all formatters in a first
attempt at moving towards epoch-time alignment (though don't actually
use it it yet).
Adjustments to the formatter interface:
- property for `.xy_nd` the x/y nd arrays.
- property for and `.xy_slice` the nd format array(s) start->stop index
slice.
Internal routine tweaks:
- drop `read_src_from_key` and always pass full source array on updates
and adjust handlers to expect to have to index the data field of
interest.
- set `.last_read` right after update calls instead of after 1d
conversion.
- drop `slice_to_head` array read slicing.
- add some debug points for testing 'time' indexing (though not used
here yet).
- add `.x_nd` array update logic for when the `.index_field` is not
'index' - i.e. when we begin to try and support epoch time.
- simplify some new y_nd updates to not require use of `np.broadcast()`
where possible.
Probably means it doesn't need to be a `Flume` method but it's
convenient to expect the caller to pass in the `np.ndarray` with
a `'time'` field instead of a `timeframe: str` arg; also, return the
slice mask instead of the sliced array as output (again allowing the
caller to do any slicing). Also, handle the slice-outside-time-range
case by just returning the entire index range with a `None` mask.
Adjust `Viz.view_data()` to instead do timeframe (for rt vs. hist shm
array) lookup and equiv array slicing with the returned mask.
Since these modules no longer contain Qt specific code we might
as well include them in the data sub-package.
Also, add `IncrementalFormatter.index_field` as single point to def the
indexing field that should be used for all x-domain graphics-data
rendering.
Was broken since the `_adhoc_futes_set` rework a while back. Removes the
cmdty symbols from that set into a new one and fixes the contract
case block to catch `Contract(secType='CMDTY')` case. Also makes
`Client.search_symbols()` return details `dict`s so that `piker search`
will work again..
Since higher level charting and fsp management need access to the
new `Flume` indexing apis this adjusts some func sigs to pass through
(and/or create) flume instances:
- `LinkedSplits.add_plot()` and dependents.
- `ChartPlotWidget.draw_curve()` and deps, and it now returns a `Flow`.
- `.ui._fsp.open_fsp_admin()` and `FspAdmin.open_fsp_ui()` related
methods => now we wrap the destination fsp shm in a flume on the admin
side and is returned from `.start_engine_method()`.
Drop a bunch of (unused) chart widget methods including some already
moved to flume methods: `.get_index()`, `.in_view()`,
`.last_bar_in_view()`, `.is_valid_index()`.
Move to expect and process new by-tick-event frames where the display
loop can now just iterate the most recent tick events by type instead of
the entire tick history sequence - thus we reduce iterations inside the
update loop.
Also, go back to use using the detected display's refresh rate (minus 6)
as the default feed requested throttle rate since we can now handle
much more bursty-ness in display updates thanks to the new framing
format B)
Factor out the chart widget creation since it's only executed once
during rendering of the first feed/flow whilst keeping plotitem overlay
creation inside the (flume oriented) init loop. Only create one vlm and
FSP chart/chain for now until we figure out if we want FSPs overlayed by
default or selected based on the "front" symbol in use. Add a default
color-palette set using shades of gray when plotting overlays. Presume
that the display loop's quote throttle rate should be uniformly
distributed over all input symbol-feeds for now. Restore feed pausing on
mouse interaction.
Initial support for real-time multi-symbol overlay charts using an
aggregate feed delivered by `Feed.open_multi_stream()`.
The setup steps for constructing the overlayed plot items is still very
very rough and will likely provide incentive for better refactoring high
level "charting APIs". For each fqsn passed into `display_symbol_data()`
we now synchronously,
- create a single call to `LinkedSplits.plot_ohlc_main() -> `ChartPlotWidget`
where we cache the chart in scope and for all other "sibling" fqsns
we,
- make a call to `ChartPlotWidget.overlay_plotitem()` -> `PlotItem`, hide its axes,
make another call with this plotitem input to
`ChartPlotWidget.draw_curve()`, set a sym-specific view box auto-yrange maxmin callback,
register the plotitem in a global `pis: dict[str, list[pgo.PlotItem, pgo.PlotItem]] = {}`
Once all plots have been created we then asynchronously for each symbol,
- maybe create a volume chart and register it in a similar task-global
table: `vlms: dict[str, ChartPlotWidget] = {}`
- start fsp displays for each symbol
Then common entrypoints are entered once for all symbols:
- a single `graphics_update_loop()` loop-task is started wherein
real-time graphics update components for each symbol are created,
* `L1Labels`
* y-axis last clearing price stickies
* `maxmin()` auto-ranger
* `DisplayState` (stored in a table `dss: dict[str, DisplayState] = {}`)
* an `increment_history_view()` task
and a single call to `Feed.open_multi_stream()` is used to create
a symbol-multiplexed quote stream which drives a single loop over all
symbols wherein for each quote the appropriate components are looked
up and passed to `graphics_update_cycle()`.
- a single call to `open_order_mode()` is made with the first symbol
provided as input, though eventually we want to support passing in the
entire list.
Further internal implementation details:
- special tweaks to the `pg.LinearRegionItem` setup wherein the region
is added with a zero opacity and *after* all plotitem overlays to
avoid and issue where overlays weren't being shown within the region
area in the history chart.
- all symbol-specific graphics oriented update calls are adjusted to
pass in the fqsn:
* `update_fsp_chart()`
* `ChartView._set_yrange()`
* ChartPlotWidget.update_graphics_from_flow()`
- avoid a double increment on sample step updates by not calling the
increment on any vlm chart since it seems the vlm-ohlc chart linking
already takes care of this now?
- use global counters for the last epoch time step to avoid incrementing
all views more then once per new time step given underlying shm array
buffers may be on different array-index values from one another.
Main "public" API change is to make `GodWidget.get/set_chart_symbol()`
accept and cache-on fqsn tuples to allow handling overlayed chart groups
and adjust method names to be plural to match.
Wrt `LinkedSplits`,
- create all chart widget axes with a `None` plotitem argument and set
the `.pi` field after axis creation (since apparently we have another
object reference causality dilemma..)
- set a monkeyed `PlotItem.chart_widget` for use in axes that still need
the widget reference.
- drop feed pause/resume for now since it's leaking feed tasks on the
`brokerd` side and we probably don't really need it any more, and if
we still do it should be done on the feed not the flume.
Wrt `ChartPlotItem`,
- drop `._add_sticky()` and use the `Axis` method instead and add some
overlay + axis sanity checks.
- refactor `.draw_ohlc()` to be a lighter wrapper around a call to
`.add_plot()`.
We have this method on our `ChartPlotWidget` but it makes more sense to
directly associate axis-labels with, well, the label's parent axis XD.
We add `._stickies: dict[str, YAxisLabel]` to replace
`ChartPlotWidget._ysticks` and pass in the `pg.PlotItem` to each axis
instance, stored as `Axis.pi` instead of handing around linked split
references (which are way out of scope for a single axis).
More work needs to be done to remove dependence on `.chart:
ChartPlotWidget` references in the date axis type as per comments.
Comments out the pixel-cache resetting since it doesn't seem we need it
any more to avoid draw oddities?
For `.fast_path` appends, this nearly got it working except the new path
segments are either not being connected correctly (step curve) or not
being drawn in full since the history path (plain line).
Leaving the attempted code commented in for a retry in the future; my
best guesses are that maybe,
- `.connectPath()` call is being done with incorrect segment length
and/or start point.
- the "appended" data: `appended = array[-append_len-1:slice_to_head]`
(done inside the formatter) isn't correct (i.e. endpoint handling
considering a path append) and needs special handling for different
curve types?
Ensure `.boundingRect()` calcs and `.draw_last_datum()` do geo-sizing
based on source data instead of presuming some `1.0` unit steps in some
spots; we need this to support an epoch index as is needed for overlays.
Further, clean out a bunch of old bounding rect calc code and add some
commented code for trying out `QRectF.united()` on the path + last datum
curve segment. Turns out that approach is slower as per eyeballing the
added profiler points.
After trying to hack epoch indexed time series and failing miserably,
decided to properly factor out all formatting routines into a common
subsystem API: ``IncrementalFormatter`` which provides the interface for
incrementally updating and tracking pre-path-graphics formatted data.
Previously this functionality was mangled into our `Renderer` (which
also does the work of `QPath` generation and update) but splitting it
out also preps for being able to do graphics-buffer downsampling and
caching on a remote host B)
The ``IncrementalFormatter`` (parent type) has the default behaviour of
tracking a single field-array on some source `ShmArray`, updating
a flattened `numpy.ndarray` in-mem allocation, and providing a default
1d conversion for pre-downsampling and path generation.
Changed out of `Renderer`,
- `.allocate_xy()`, `update_xy()` and `format_xy()` all are moved to
more explicitly named formatter methods.
- all `.x/y_data` nd array management and update
- "last view range" tracking
- `.last_read`, `.diff()`
- now calls `IncrementalFormatter.format_to_1d()` inside `.render()`
The new API gets,
- `.diff()`, `.last_read`
- all view range diff tracking through `.track_inview_range()`.
- better nd format array names: `.x/y_nd`, `xy_nd_start/stop`.
- `.format_to_1d()` which renders pre-path formatted arrays ready for
both m4 sampling and path gen.
- better explicit overloadable formatting method names:
* `.allocate_xy()` -> `.allocate_xy_nd()`
* `.update_xy()` -> `.incr_update_xy_nd()`
* `.format_xy()` -> `.format_xy_nd_to_1d()`
Finally this implements per-graphics-type formatters which define
each set up related formatting routines:
- `OHLCBarsFmtr`: std multi-line style bars
- `OHLCBarsAsCurveFmtr`: draws an interpolated line for ohlc sampled data
- `StepCurveFmtr`: handles vlm style curves
More or less a revamp (and possibly first draft for something similar in
`tractor` core) which ensures all actor trees attempt to discover the
`pikerd` registry actor.
Implementation improvements include:
- new `Registry` singleton which houses the `pikerd` discovery
socket-address `Registry.addr` + a `open_registry()` manager which
provides bootstrapped actor-local access.
- refine `open_piker_runtime()` to do the work of opening a root actor
and call the new `open_registry()` depending on whether a runtime has
yet been bootstrapped.
- rejig `[maybe_]open_pikerd()` in terms of the above.
Allows running simultaneous data feed services on the same (linux) host
by avoiding file-name collisions instead keying shm buffer sets by the
given `brokerd` instance. This allows, for example, either multiple dev
versions of the data layer to run side-by-side or for the test suite to
be seamlessly run alongside a production instance.
Previously we were relying on implicit actor termination in
`maybe_spawn_daemon()` but really on `pikerd` teardown we should be sure
to tear down not only all service tasks in each actor but also the actor
runtimes. This adjusts `Services.cancel_service()` to only cancel the
service task scope and wait on the `complete` event and reworks the
`open_context_in_task()` inner closure body to,
- always cancel the service actor at exit.
- not call `.cancel_service()` (potentially causing recursion issues on
cancellation).
- allocate a `complete: trio.Event` to signal full task + actor termination.
- pop the service task from the `.service_tasks` registry.
Further, add a `maybe_set_global_registry_sockaddr()` helper-cm to do
the work of checking whether a registry socket needs-to/has-been set
and use it for discovery calls to the `pikerd` service tree.
Seems that by default their history indexing rounds down/back to the
previous time step, so make sure we add a minute inside `Client.bars()`
when the `end_dt=None`, indicating "get the latest bar". Add
a breakpoint block that should trigger whenever the latest bar vs. the
latest epoch time is mismatched; we'll remove this after some testing
verifying the history bars issue is resolved.
Further this drops the legacy `backfill_bars()` endpoint which has been
deprecated and unused for a while.
Always use `open_sample_stream()` to register fast and slow quote feed
buffers and get a sampler stream which we use to trigger
`Sampler.broadcast_all()` calls on the service side after backfill
events.
Now spawned under the `pikerd` tree as a singleton-daemon-actor we offer
a slew of new routines in support of this micro-service:
- `maybe_open_samplerd()` and `spawn_samplerd()` which provide the
`._daemon.Services` integration to conduct service spawning.
- `open_sample_stream()` which is a client-side endpoint which does all
the work of (lazily) starting the `samplerd` service (if dne) and
registers shm buffers for update as well as connect a sample-index
stream for iterator by the caller.
- `register_with_sampler()` which is the `samplerd`-side service task
endpoint implementing all the shm buffer and index-stream registry
details as well as logic to ensure a lone service task runs
`Services.increment_ohlc_buffer()`; it increments at the shortest period
registered which, for now, is the default 1s duration.
Further impl notes:
- fixes to `Services.broadcast()` to ensure broken streams get discarded
gracefully.
- we use a `pikerd` side singleton mutex `trio.Lock()` to ensure
one-and-only-one `samplerd` is ever spawned per `pikerd` actor tree.
Drop the `_services` module level ref and adjust all client code to
match. Drop struct inheritance and convert all methods to class level.
Move `Brokerd.locks` -> `Services.locks` and add sampling mod to pikerd
enabled set.
We're moving toward a single actor managing sampler work and distributed
independently of `brokerd` services such that a user can run samplers on
different hosts then real-time data feed infra. Most of the
implementation details include aggregating `.data._sampling` routines
into a new `Sampler` singleton type.
Move the following methods to class methods:
- `.increment_ohlc_buffer()` to allow a single task to increment all
registered shm buffers.
- `.broadcast()` for IPC relay to all registered clients/shms.
Further add a new `maybe_open_global_sampler()` which allocates
a service nursery and assigns it to the `Sampler.service_nursery`; this
is prep for putting the step incrementer in a singleton service task
higher up the data-layer actor tree.
When we see multiple history frames that are duplicate to the request
set, bail re-trying after a number of tries (6 just cuz) and return
early from the tsdb backfill loop; presume that this many duplicates
means we've hit the beginning of history. Use a `collections.Counter`
for the duplicate counts. Make sure and warn log in such cases.
Wow, turns out tick framing was totally borked since we weren't framing
on "greater then throttle period long waits" XD
This moves all the framing logic into a common func and calls it in
every case:
- every (normal) "pre throttle period expires" quote receive
- each "no new quote before throttle period expires" (slow case)
- each "no clearing tick yet received" / only burst on clears case
Add some (untested) data slicing util methods for mapping time ranges to
source data indices:
- `.get_index()` which maps a single input epoch time to an equiv array
(int) index.
- add `slice_from_time()` which returns a view of the shm data from an
input epoch range presuming the underlying struct array contains
a `'time'` field with epoch stamps.
- `.view_data()` which slices out the "in view" data according to the
current state of the passed in `pg.PlotItem`'s view box.
This has been an outstanding idea for a while and changes the framing
format of tick events into a `dict[str, list[dict]]` wherein for each
tick "type" (eg. 'bid', 'ask', 'trade', 'asize'..etc) we create an FIFO
ordered `list` of events (data) and then pack this table into each
(throttled) send. This gives an additional implied downsample reduction
(in terms of iteration on the consumer side) from `N` tick-events to
a (max) `T` tick-types presuming the rx side only needs the latest tick
event.
Drop the `types: set` and adjust clearing event test to use the new
`ticks_by_type` map's keys.
Instead of uniformly distributing the msg send rate for a given
aggregate subscription, choose to be more bursty around clearing ticks
so as to avoid saturating the consumer with L1 book updates and vs.
delivering real trade data as-fast-as-possible.
Presuming the consumer is in the "UI land of slow" (eg. modern display
frame rates) such an approach serves more useful for seeing "material
changes" in the market as-bursty-as-possible (i.e. more short lived fast
changes in last clearing price vs. many slower changes in the bid-ask
spread queues). Such an approach also lends better to multi-feed
overlays which in aggregate tend to scale linearly with the number of
feeds/overlays; centralization of bursty arrival rates allows for
a higher overall throttle rate if used cleverly with framing.
Seems that by default their history indexing rounds down/back to the
previous time step, so make sure we add a minute inside `Client.bars()`
when the `end_dt=None`, indicating "get the latest bar". Add
a breakpoint block that should trigger whenever the latest bar vs. the
latest epoch time is mismatched; we'll remove this after some testing
verifying the history bars issue is resolved.
Further this drops the legacy `backfill_bars()` endpoint which has been
deprecated and unused for a while.
Likely pertains to helping with stuff in issues #345 and #373 and just
generally is handy to have when processing ledgers / clearing event
tables.
Adds the following helper methods:
- `iter_by_dt()` to iter-sort an arbitrary `Transaction`-like table of
clear entries.
- `Position.iter_clears()` as a convenience wrapper for the above.
Trying to send a message in the `NoBsWs.fixture()` exit when the ws is
not currently disconnected causes a double `._stack.close()` call which
will corrupt `trio`'s coro stack. Instead only do the unsub if we detect
the ws is still up.
Also drops the legacy `backfill_bars()` module endpoint.
Fixes#437
Seems that by default their history indexing rounds down/back to the
previous time step, so make sure we add a minute inside `Client.bars()`
when the `end_dt=None`, indicating "get the latest bar". Add
a breakpoint block that should trigger whenever the latest bar vs. the
latest epoch time is mismatched; we'll remove this after some testing
verifying the history bars issue is resolved.
Further this drops the legacy `backfill_bars()` endpoint which has been
deprecated and unused for a while.
Instead of requiring any `-b` try to import all built-in broker backend
python modules by default and only load those detected from the input symbol
list's fqsn values. In other words the `piker chart` cmd can be run sin
`-b` now and that flag is only required if you only want to load
a subset of the built-ins or are trying to load a specific
not-yet-builtin backend.
Allows using `set` ops for subscription management and guarantees no
duplicates per `brokerd` actor. New API is simpler for dynamic
pause/resume changes per `Feed`:
- `_FeedsBus.add_subs()`, `.get_subs()`, `.remove_subs()` all accept multi-sub
`set` inputs.
- `Feed.pause()` / `.resume()` encapsulates management of *only* sending
a msg on each unique underlying IPC msg stream.
Use new api in sampler task.
Previously we would only detect overruns and drop subscriptions on
non-throttled feed subs, however you can get the same issue with
a wrapping throttler task:
- the intermediate mem chan can be blocked either by the throttler task
being too slow, in which case we still want to warn about it
- the stream's IPC channel actually breaks and we still want to drop
the connection and subscription so it doesn't be come a source of
stale backpressure.
Set each quote-stream by matching the provider for each `Flume` and thus
results in some flumes mapping to the same (multiplexed) stream.
Monkey-patch the equivalent `tractor.MsgStream._ctx: tractor.Context` on
each broadcast-receiver subscription to allow use by feed bus methods as
well as other internals which need to reference IPC channel/portal info.
Start a `_FeedsBus` subscription management API:
- add `.get_subs()` which returns the list of tuples registered for the
given key (normally the fqsn).
- add `.remove_sub()` which allows removing by key and tuple value and
provides encapsulation for sampler task(s) which deal with dropped
connections/subscribers.
Adds provider-list-filtered (quote) stream multiplexing support allowing
for merged real-time `tractor.MsgStream`s using an `@acm` interface.
Behind the scenes we are just doing a classic multi-task push to common
mem chan approach.
Details to make it work on `Feed`:
- add `Feed.mods: dict[str, Moduletype]` and
`Feed.portals[ModuleType, tractor.Portal]` which are both populated
during init in `open_feed()`
- drop `Feed.portal` and `Feed.name`
Also fix a final lingering tsdb history loading loop termination bug.
A slight facepalm but, the main issue was a simple indexing logic error:
we need to slice with `tsdb_history[-shm._first.value:]` to push most
recent history not oldest.. This allows cleanup of tsdb backfill loop as
well.
Further, greatly simply `diff_history()` time slicing by using the
classic `numpy` conditional slice on the epoch field.
This had a bug prior where the end of a frame (a partial) wasn't being
sliced correctly and we'd get odd gaps showing up in the backfilled from
`brokerd` vs. tsdb end index. Repair this by doing timeframe aware index
diffing in `diff_history()` which seems to resolve it. Also, use the
frame-result's `end_dt: datetime` for the loop exit condition.
Sync per-symbol sampler loop start to subscription registers such that
the loop can't start until the consumer's stream subscription is added;
the task-sync uses a `trio.Event`. This patch also drops a ton of
commented cruft.
Further adjustments needed to get parity with prior functionality:
- pass init msg 'symbol_info' field to the `Symbol.broker_info: dict`.
- ensure the `_FeedsBus._subscriptions` table uses the broker specific
(without brokername suffix) as keys for lookup so that the sampler
loop doesn't have to append in the brokername as a suffix.
- ensure the `open_feed_bus()` flumes-table-msg returned sent by
`tractor.Context.started()` uses the `.to_msg()` form of all flume
structs.
- ensure `maybe_open_feed()` uses `tractor.MsgStream.subscribe()` on all
`Flume.stream`s on cache hits using the
`tractor.trionics.gather_contexts()` helper.
Orient shm-flow-arrays around the new idea of a `Flume` which provides
access, mgmt and basic measure of real-time data flow sets (see water
flow management semantics).
- We discard the previous idea of a "init message" which contained all
the shm attachment info and instead send a startup message full of
`Flume.to_msg()`s which are symmetrically loaded on the caller actor
side.
- Create data-flows "entries" for every passed in fqsn such that the consumer gets back
streams and shm for each, now all wrapped in `Flume` types. For now we
allocate `brokermod.stream_quotes()` tasks 1-to-1 for each fqsn
(instead of expecting each backend to do multi-plexing, though we
might want that eventually) as well a `_FeedsBus._subscriber` entry
for each. The pause/resume management loop is adjusted to match.
Previously `Feed`s were allocated 1-to-1 with each fqsn.
- Make `Feed` a `Struct` subtype instead of a `@dataclass` and move all
flow specific attrs to the new `Flume`:
- move `.index_stream()`, `.get_ds_info()` to `Flume`.
- drop `.receive()`: each fqsn entry will now require knowledge of
separate streams by feed users.
- add multi-fqsn tables: `.flumes`, `.streams` which point to the
appropriate per-symbol entries.
- Async load all `Flume`s from all contexts and all quote streams using
`tractor.trionics.gather_contexts()` on the client `open_feed()` side.
- Update feeds test to include streaming 2 symbols on the same (binance)
backend.
This is to prep for multi-symbol feeds and charts so we accept
a sequence of fqsns to the top level entrypoints as well as the
`.data.feed.open_feed()` API (though we're not actually supporting true
multiplexed feeds nor shm lookups per fqsn yet).
Allows starting UI apps and passing the `pikerd` registry socket-addr
args via `--host` or `--port` such that a separate actor tree can be
started by selecting an unused port. This is handy when hacking new
features but while also wishing to run a more stable version of the code
for trading on the same host.
Drop all attempts at rewiring `ViewBox` signals, monkey-patching
relayee handlers, and generally modifying event source public
attributes. Instead take a much simpler approach where the event source
graphics object simply has it's handler dynamically overridden by
a broadcaster function which relays to all consumers using a Python
loop.
The benefits of this much simplified approach include:
- avoiding the tedious and often complex (re)connection of signals between
the source plot and the overlayed consumers.
- requiring zero modification of the public interface of any of the
publisher or consumer `ViewBox`s, no decoration, extra signal
definitions (eg. previous `mouseDragEventRelay` or the like).
- only a single dynamic method override on the event source graphics object
(`ViewBox`) which does the broadcasting work and requires no
modification to handler implementations.
Detailed `.ui._overlay` changes:
- drop `mk_relay_signal()`, `enable_relays()` which removes signal/slot
hacking methodology.
- drop unused `ComposedGridLayout.grid` and `.reverse`, change some
method names: `.insert()` -> `.insert_plotitem()`, `append()` ->
`.append_plotitem()`.
- in `PlotOverlay`, again drop all signal/slot rewiring in
`.add_plotitem()` and instead add our new closure based python-loop in
`broadcast()` routine which is used to override the event-source
object's handler.
- comment out all the auxiliary/want-to-have event source selection
methods for now.
Mainly this involves instantiating our overriden `PlotItem` in a few
places and tweaking type annots. A further detail is that inside
the fsp sub-chart creation code we hide some axes for overlays in the
flows subchart; these were previously somehow hidden implicitly?
Fork out our patch set submitted to upstream in multiple PRs (since they
aren't moving and/or aren't a priority to core) which can be seen in
full from the following diff:
https://github.com/pyqtgraph/pyqtgraph/compare/master...pikers:pyqtgraph:graphics_pin
Move these type extensions into the internal `.ui._pg_overrides` module.
The changes are related to both `pyqtgraph.PlotItem` and `.AxisItem` and
were driven for our need for multi-view overlays (overlaid charts with
optionally synced axis and interaction controls) as documented in the PR
to upstream: https://github.com/pyqtgraph/pyqtgraph/pull/2162
More specifically,
- wrt to `AxisItem` we added lru caching of tick values as per:
https://github.com/pyqtgraph/pyqtgraph/pull/2160.
- wrt to `PlotItem` we adjusted some of the axis management code, namely
adding a standalone `.removeAxis()` and modifying the `.setAxisItems()` logic
to use it in: https://github.com/pyqtgraph/pyqtgraph/pull/2162
as well as some tweaks to `.updateGrid()` to loop through all possible
axes when grid setting.
Details of the original patch to upstream are in:
https://github.com/pyqtgraph/pyqtgraph/pull/2281
Instead of trying to land this we've opted to just copy out that version
of `.debug.Profiler` into our own internals (luckily the class is
entirely self-contained) until such a time when we choose to find
a better dependency as per https://github.com/pikers/piker/issues/337
To make it easier to manually read/decipher long ledger files this adds
`dict` sorting based on record-type-specific (api vs. flex report)
datetime processing prior to ledger file write.
- break up parsers into separate routines for flex and api record
processing.
- add `parse_flex_dt()` for special handling of the weird semicolon
stamps in flex reports.
There never was any underlying db bug, it was a hardcoded timeframe in
the column series write key.. Now we always assert a matching timeframe
in results.
Not only improves startup latency but also avoids a bug where the rt
buffer was being tsdb-history prepended *before* the backfilling of
recent data from the backend was complete resulting in our of order
frames in shm.
Factor the multi-sample-rate region UI connecting into a new helper
`link_views_with_region()` which reads in the shm buffer offsets from
the `Feed` and appropriately connects the fast and slow chart handlers
for the linear region graphics. Add detailed comments writeup for the
inter-sampling transform algebra.
If a history manager raises a `DataUnavailable` just assume the sample
rate isn't supported and that no shm prepends will be done. Further seed
the shm array in such cases as before from the 1m history's last datum.
Also, fix tsdb -> shm back-loading, cancelling tsdb queries when either
no array-data is returned or a frame is delivered which has a start time
no lesser then the least last retrieved. Use strict timeframes for every
`Storage` API call.
Turns out querying for a high freq timeframe (like 1sec) will still
return a lower freq timeframe (like 1Min) SMH, and no idea if it's the
server or the client's fault, so we have to explicitly check the sample
step size and discard lower freq series-results. Do this inside
`Storage.read_ohlcv()` and return an empty `dict` when the wrong time
step is detected from the query result.
Further enforcements,
- both `.load()` and `read_ohlcv()` now require an explicit `timeframe:
int` input to guarantee the time step of the output array.
- drop all calls `.load()` with non-timeframe specific input.
Our default sample periods are 60s (1m) for the history chart and 1s for
the fast chart. This patch adds concurrent loading of both (or more)
different sample period data sets using the existing loading code but
with new support for looping through a passed "timeframe" table which
points to each shm instance.
More detailed adjustments include:
- breaking the "basic" and tsdb loading into 2 new funcs:
`basic_backfill()` and `tsdb_backfill()` the latter of which is run
when the tsdb daemon is discovered.
- adjust the fast shm buffer to offset with one day's worth of 1s so
that only up to a day is backfilled as history in the fast chart.
- adjust bus task starting in `manage_history()` to deliver back the
offset indices for both fast and slow shms and set them on the
`Feed` object as `.izero_hist/rt: int` values:
- allows the chart-UI linked view region handlers to use the offsets
in the view-linking-transform math to index-align the history and
fast chart.
Allows keeping mutex state around data reset requests which (if more
then one are sent) can cause a throttling condition where ib's servers
will get slower and slower to conduct a reconnect. With this you can
have multiple ongoing contract requests without hitting that issue and
we can go back to having a nice 3s timeout on the history queries before
activating the hack.
When a network outage or data feed connection is reset often the
`ib_insync` task will hang until some kind of (internal?) timeout takes
place or, in some (worst) cases it never re-establishes (the event
stream) and thus the backend needs to restart or the live feed will
never resume..
In order to avoid this issue once and for all this patch implements an
additional (extremely simple) task that is started with the real-time
feed and simply waits for any market data reset events; when detected
restarts the `open_aio_quote_stream()` call in a loop using
a surrounding cancel scope.
Been meaning to implement this for ages and it's finally working!
Allows for easier restarts of certain `trio` side tasks without killing
the `asyncio`-side clients; support via flag.
Also fix a bug in `Client.bars()`: we need to return the duration on the
empty bars case..
This allows the history manager to know the decrement size for
`end_dt: datetime` on the next query if a no-data / gap case was
encountered; subtract this in `get_bars()` in such cases. Define the
expected `pendulum.Duration`s in the `.api._samplings` table.
Also add a bit of query latency profiling that we may use later to more
dynamically determine timeout driven data feed resets. Factor the `162`
error cases into a common exception handler block.
When we get a timeout or a `NoData` condition still return a tuple of
empty sequences instead of `None` from `Client.bars()`. Move the
sampling period-duration table to module level.
It doesn't seem to be any slower on our least throttled backend
(binance) and it removes a bunch of hard to get correct frame
re-ordering logic that i'm not sure really ever fully worked XD
Commented some issues we still need to resolve as well.
Manual tinker-testing demonstrated that triggering data resets
completely independent of the frame request gets more throughput and
further, that repeated requests (for the same frame after cancelling on
the `trio`-side) can yield duplicate frame responses. Re-work the
dual-task structure to instead have one task wait indefinitely on the
frame response (and thus not trigger duplicate frames) and the 2nd data
reset task poll for the first task to complete in a poll loop which
terminates when the frame arrives via an event.
Dirty deatz:
- make `get_bars()` take an optional timeout (which will eventually be
dynamically passed from the history mgmt machinery) and move request
logic inside a new `query()` closure meant to be spawned in a task
which sets an event on frame arrival, add data reset poll loop in the
main/parent task, deliver result on nursery completion.
- handle frame request cancelled event case without crash.
- on no-frame result (due to real history gap) hack in a 1 day decrement
case which we need to eventually allow the caller to control likely
based on measured frame rx latency.
- make `wait_on_data_reset()` a predicate without output indicating
reset success as well as `trio.Nursery.start()` compat so that it can
be started in a new task with the started values yielded being
a cancel scope and completion event.
- drop the legacy `backfill_bars()`, not longer used.
Adjust all history query machinery to pass a `timeframe: int` in seconds
and set default of 60 (aka 1m) such that history views from here forward
will be 1m sampled OHLCV. Further when the tsdb is detected as up load
a full 10 years of data if possible on the 1m - backends will eventually
get a config section (`brokers.toml`) that allow user's to tune this.
The `Store.load()`, `.read_ohlcv()` and `.write_ohlcv()` and
`.delete_ts()` now can take a `timeframe: Optional[float]` param which
is used to look up the appropriate sampling period table-key from
`marketstore`.
Allow data feed sub-system to specify the timeframe (aka OHLC sample
period) to the `open_history_client()` delivered history fetching API.
Factor the data keycombo hack into a new routine to be used also from
the history backfiller code when request latency increases; there is
a first draft at trying to use the feed reset to speed up 1m frame
throttling by timing out on the history frame response, but it needs
a lot of fine tuning.
This is a simpler (and oddly more `trio`-nic and/or SC) way to handle
the cancelled-before-acked race for order dialogs. Will allow keeping
the `.req` field as solely an `Order` msg.
When the client is faster then a `brokerd` at submitting and cancelling
an order we run into the case where we need to specify that the EMS
cancels the order-flow as soon as the brokerd's ack arrives. Previously
we were stashing a `BrokerdCancel` msg as the `Status.req` msg (to be
both tested for as a "already cancelled" and sent immediately on ack arrival to
the broker), but for such
cases we can't use that msg to find the fqsn (since only the client side
msgs have it defined) which is required by the new
`Router.client_broadcast()`.
So, Since `Status.req` is supposed to be a client-side flow msg anyway,
and we need the fqsn for client broadcasting, we change this `.req`
value to the client's submitted `Cancel` msg (thus rectifying the
missing `Router.client_broadcast()` fqsn input issue) and build the
`BrokerdCancel` request from that `Cancel` inline in the relay loop
from the `.req: Cancel` status msg lookup.
Further we allow `Cancel` msgs to define an `.account` and adjust the
order mode loop to expect `Cancel` source requests in cancelled status
updates.
Except for paper accounts (in which case we need a trades dialog and
paper engine per symbol to enable simulated clearing) we can rely on the
instrument feed (symbol name) to be the caching key. Utilize
`tractor.trionics.maybe_open_context()` and the new key-as-callable
support in the paper case to ensure we have separate paper clearing
loops per symbol.
Requires https://github.com/goodboy/tractor/pull/329
With the refactor of the dark loop into a daemon task already-open order
relaying from a `brokerd` was broken since no subscribed clients were
registered prior to the relay loop sending status msgs for such existing
live orders. Repair that by adding one more synchronization phase to the
`Router.open_trade_relays()` task: deliver a `client_ready: trio.Event`
which is set by the client task once the client stream has been
established and don't start the `brokerd` order dialog relay loop until
this event is ready.
Further implementation deats:
- factor the `brokerd` relay caching back into it's own `@acm` method:
`maybe_open_brokerd_dialog()` since we do want (but only this) stream
singleton-cached per broker backend.
- spawn all relay tasks on every entry for the moment until we figure
out what we're caching against (any client pre-existing right, which
would mean there's an entry in the `.subscribers` table?)
- rename `_DarkBook` -> `DarkBook` and `DarkBook.orders` -> `.triggers`
This enables "headless" dark order matching and clearing where an `emsd`
daemon subactor can be left running with active dark (or other
algorithmic) orders which will still trigger despite to attached-controlling
ems-client.
Impl details:
- rename/add `Router.maybe_open_trade_relays()` which now does all work
of starting up ems-side long living clearing and relay tasks and the
associated data feed; make is a `Nursery.start()`-able task instead of
an `@acm`.
- drop `open_brokerd_trades_dialog()` and move/factor contents into the
above method.
- add support for a `router.client_broadcast('all', msg)` to wholesale
fan out a msg to all clients.
Establishes a more formalized subscription based fan out pattern to ems
clients who subscribe for order flow for a particular symbol (the fqsn
is the default subscription key for now).
Make `Router.client_broadcast()` take a `sub_key: str` value which
determines the set of clients to forward a message to and drop all such
manually defined broadcast loops from task (func) code. Also add
`.get_subs()` which (hackily) allows getting the set of clients for
a given sub key where any stream that is detected as "closed" is
discarded in the output. Further we simplify to `Router.dialogs:
defaultdict[str, set[tractor.MsgStream]]` and `.subscriptions` as maps
to sets of streams for much easier broadcast management/logic using set
operations inside `.client_broadcast()`.
This patch was originally to fix a bug where new clients who
re-connected to an `emsd` that was running a paper engine were not
getting updates from new fills and/or cancels. It turns out the solution
is more general: now, any client that creates a order dialog will be
subscribing to receive updates on the order flow set mapped for that
symbol/instrument as long as the client has registered for that
particular fqsn with the EMS. This means re-connecting clients as well
as "monitoring" clients can see the same orders, alerts, fills and
clears.
Impl details:
- change all var names spelled as `dialogues` -> `dialogs` to be
murican.
- make `Router.dialogs: dict[str, defaultdict[str, list]]` so that each
dialog id (oid) maps to a set of potential subscribing ems clients.
- add `Router.fqsn2dialogs: dict[str, list[str]]` a map of fqsn entries to
sets of oids.
- adjust all core task code to make appropriate lookups into these 2 new
tables instead of being handed specific client streams as input.
- start the `translate_and_relay_brokerd_events` task as a daemon task
that lives with the particular `TradesRelay` such that dialogs cleared
while no client is connected are still processed.
- rename `TradesRelay.brokerd_dialogue` -> `.brokerd_stream`
- broadcast all status msgs to all subscribed clients in the relay loop.
- always de-reg each client stream from the `Router.dialogs` table on close.
Not sure what exactly happened but it seemed clears weren't working in
some cases without this, also there's no point in spinning the simulated
clearing loop if we're handling a non-clearing tick type.
We haven't been using it for a while and the supposed (remembered)
latency issue on interaction doesn't seem existing after applying the
cache mode. This allows dropping some internal state-logic and generally
simplifying the show-on-hover checks.
Further add `.show_markers()` and `.hide_markers()` as explicit methods
that can be called externally by UI business logic.
Bit of a face palm but obviously `LevelLine.delete()` also removes any
`._marker` from the view which makes it disappear permanently when
moving from non-zero to zero to non-zero positions.. We don't really
need to delete the line since it can be re-used so just remove that
code.
Further this patch removes marker style setting logic from within the
`pp_line()` factory and instead expects the caller to set the correct
"direction" (for long / short) afterward.
- Every time a symbol is switched on chart we need to wait until the
search bar sidepane has been added beside the slow chart before
determining the offset for the pp line's arrow/labels; trigger this in
`GodWidget.load_symbol()` -> required monkeypatching on a
`.mode: OrderMode` to the `.rt_linked` for now..
- Drop the search pane widget removal from the current linked chart,
seems faster?
- On the slow chart override the `LevelMarker.scene_x()` callback to
adjust for the case where no L1 labels are shown beside the y-axis.
Also adds a `GodWidget.resize_all()` helper method which resizes all
sub-widgets and charts to their default ratios and/or parent-widget
dependent defaults using the detected available space on screen. This is
a "default layout" config method that eventually we'll probably want
allow users to customize.
In other words instead of some static view size previously determined by
the accompanying (slow) chart's height, (recursively) calculate the
number of displayed rows and compute the minimal height needed. This
still caps the view at the height of the chart such that the view will
switch to scroll bar mode when too many results are shown and can't all
be fit in the vertical space.
Deats:
- add a ``CompleterView.iter_df_rows()`` which recursively iterates all
rows in depth-first order making it simple to compute the absolute
number of result rows in view and thus the minimal number of pixels to
show all results.
- always pass the height in the `.on_resize()` handler to ensure
triggering the height logic when new results are generated in the
search loop.
Scales the "view" instance that holds search results to the size of the
accompanying "slow chart" for which the search pane is a "sidepane".
A lot of mucking about was required due to resizing of the view
seemingly feeding back into window resizing and further implementing the
sizing logic such that the parent `QSplitter` can be resized as the
user's whim as well.
Details,
- add a `CompleterView._init: bool` which is set once (and only once)
after startup where the first display of the current symbol/feed is
shown allowing and a single *width* padding applied once at startup
to ensure we don't have an awkward line to the right of the longest
result.
- in `.resize_to_results()` only apply a minimum height to the view
using `.setMinimumHeight()` with a down-scaled (`0.91` for now) height
value from input.
- re-implement `CompleterView.show_matches()` to accept and optional
width, heigh tuple and when not supplied pull the slow chart's
dimensions and pass as input to the resize method.
- Make `SearchWidget` x dim sizing policy "fixed".
- register the `SearchWidget` for resize events with god.
- add `.show_only_cache_entries()` for easy results clearing.
- add `.space_dims()` to retrieve slow linked-charts dimensions.
- implement `SearchWidget.on_resize()` which is the caller of all the
previously mentioned resizing routines.
- do resizing and cache entry showing on search loop startup and be sure
to clear to cache when the user selects a symbol-feed with Enter.
It ended up being what'd you expect, races on the accessing shm buffer
data by the UI during the whole "mega-async-startup-everything" phase XD
So we add the following list of ad-hoc startup steps:
- do `.default_view()` on the slow chart after the fast chart is mostly
fully spawned with the intention being to capture the state where the
historical buffer is mostly loaded before sizing the view to the
graphical form of the data.
- resize slow chart sidepanes from the fast chart just before sleeping
forever (and after order mode has booted).
Turns out god widget resizes aren't triggered implicitly by window
resizes, so instead, hook into the window by moving what was our useless
method to that class. Further we explicitly define and declare that our
window has a `.godwidget: GodWidget` and set it up in the bootstrap
phase - in `run_qutractor()` during `trio` guest mode configuration.
Further deatz:
- retype the runtime/bootstrap routines to take a qwidget "type" not an
instance, and drop the whole implicit `.main_widget` stuff.
- delegate into the `GodWidget.on_win_resize()` for any window resize
which then triggers all the custom resize callbacks we already had in
place.
- privatize `ChartnPane.sidepane` so that it can't be mutated willy
nilly without calling `.set_sidepane()`.
- always adjust splitter sizes inside `LinkeSplits.add_plot()`.
More or less moves all the UI related position "nav" logic and graphics
item management into a new `._position.Nav` composite type + api for
high level mgmt of position graphics indicators across multiple charts
(fast and slow).
The slow (history) chart requires it's own y-range checker logic which
needs to be run in 2 cases:
- the last datum is in view and goes outside the previous mx/mn in view
- the chart is incremented a step
Since we need this duplicate logic this patch also factors the incremental
graphics update info "reading" into a new `DisplayState.incr_info()`
method that can be configured to a chart and input state and returns all
relevant "graphics update measure" in a tuple (for now).
Use this method throughout the rest of the display loop for both fast
and slow chart checks and in the `increment_history_view()` slow chart
task.
Use the new `Feed.get_ds_info()` method in a poll loop to definitively
get the inter-chart sampling info and avoid races with shm buffer
backfilling.
Also, factor the history increment closure-task into
`graphics_update_loop()` which will make it clearer how to factor
all the "should we update" logic into some `DisplayState` API.
If you spawn a brokerd set and no `ib` data feed was started (via our
`.data.feed.Feed` api) then there will be no active client loaded and
thus wont' be connected. So in these cases just return nothing, and
I guess we'll figure out real connection failures later?
Add an update call to the display loop to consistently update the last
datum in the history view chart. Compute the inter-chart sampling ratio
and use it to sync the linear region.
Add a first draft of a working `pyqtgraph.LinearRegionItem` link between
a history view chart (+ data set) and the normal real-time "HFT" chart
set.
Add the history view (aka more downsampled data view) chart set to the
rt/hft set's splitter as it's "first widget". Hook up linear region
callbacks to enable syncing between charts including compenstating for
the downsampling rate ration (in this case hardcoded 60 since 1s to 1M,
but we'll actually compute it going forward obvs).
More to come dawgys..
Adds an additional `GodWidget.hist_linked: LinkedSplits` alongside the
renamed `.rt_linked` to enable 2 sets of linked charts with different
sampled data sets/flows. The history set is added without "all the
fixins" for now (i.e. no order mode sidepane or search integration) such
that it is merely a top level chart which shows a much longer term
history and can be added to the UI via embedding the entire history
linked-splits instance into the real-time linked set's splitter.
Further impl deats:
- adjust the `GodWidget._chart_cache: dict[str, tuple]]` to store both
linked split chart sets per symbol so that symbol switching will
continue to work with the added history chart (set).
- rework `.load_symbol()` to operate on both the real-time (HFT) chart
set and the history set.
- rework `LinkedSplits.set_split_sizes()` to compensate for the history
chart and do more detailed height calcs arithmetic to make it appear
by default as a minor sub-chart.
- adjust `LinkedSplits.add_plot()` and `ChartPlotWidget` internals to allow
adding a plot without a sidepane and/or container `ChartnPane`
composite widget by checking for a `sidepane == False` input.
- make `.default_view()` accept a manual y-axis offset kwarg.
- adjust search mode to provide history linked splits to
`.set_chart_symbol()` call.
As part of supporting a "history view" chart which shows downsampled
datums alongside our 1s (or higher) sampled OHLC we need a separate
buffer to store a the slower history from broker backends. This begins
that design by allocating 2 buffers:
- `rt_shm: ShmArray` which maps to a `/dev/shm/` file with `_rt` suffix
- `hist_shm: ShmArray` which maps to a file with `_hist` suffix
Deliver both of these shms back from both `manage_history()` and load
them as `Feed.rt_shm`/`.hist_shm` on the client side.
Impl deats:
- init the rt buffer with the first datum from loaded history and
assign all OHLC values to that row's 'close' and the vlm to 0.
- pass the hist buffer to the backfiller task
- only spawn **one** global sampler array-row increment task per
`brokerd` and pass in the 1s delay which we presume is our lowest
OHLC sample rate for now.
- drop `open_sample_step_stream()` and just move its body contents into
`Feed.index_stream()`
Instead of worrying about the increment period per shm subscription,
just use the value passed as input and presume the caller knows that
only one task is necessary and that the wakeup (sampling) period should
be the shortest that is needed.
It's very unlikely we don't want at least a 1s sampling (both in terms
of task switching cost and general usage) which will eventually ship as
the default "real-time" feed "timeframe". Further, this "fast" increment
sampling task can handle all lower sampling periods (eg. 1m, 5m, 1H)
based on the current implementation just the same.
Also, add a global default sample period as `_defaul_delay_s` for use in
other internal modules.
Clearly, the linter didn't help us here.. but, just pass the
`brokerd` time for now in the `.broker_time` field; we can't get it from
the fill-case incremental updates in the `openOrders` sub. Add some
notes about this and how we might approach for backends with this
limitation.
This fixes a regression added after moving the msg parsing to later in
the order mode startup sequence. The `Allocator` needs to be configured
*to* the initial pos otherwise default settings will show in the UI..
Move the startup config logic from inside `mk_allocator()` to
`PositionTracker.update_from_pp()` and add a flag to allow setting the
`.startup_pp` from the current live one as is needed during initial
load.
In the short case (-ve size) we had a bug where the last sub-slots worth
of exit size would never be limited to zero once the allocator limit pos
size was hit (i.e. you could keep going more -ve on the pos,
exponentially per slot over the limit). It's a simple fix, just
a `max()` around the `l_sub_pp` var used in the next-step-size calc.
Resolves#392
Turns out we were putting too many brokername suffixes in the symbol
field and thus the order mode msg parser wasn't matching the current
asset to said msgs correctly and pps weren't being shown...
This repairs that plus simplifies the order mode initial pos msg loading
to just delegate into `process_trade_msg()` just as is done for
real-time msg updates.
If a setting fails to apply try to log an error msg and revert to the
previous setting by not applying the UI read-update until after the new
`SettingsPane.apply_setting()` call. This prevents crashes when the user
tries to give bad inputs on editable allocator fields.
Previously we only simulated paper engine fills when the data feed
provide L1 queue-levels matched an execution. This patch add further
support for clear-level matches when there are real live clears on the
data feed that are faster/not synced with the L1 (aka usually during
periods of HFT).
The solution was to simply iterate the interleaved paper book entries on
both sides for said tick types and instead yield side-specific predicate
per entry.
Not entirely sure why this all of a sudden became a problem but it seems
price changes on order edits were sometimes resulting in key errors when
modifying paper book entries quickly. This changes the implementation to
not care about matching the last price when keying/popping old orders
and use `bidict`s to more easily pop cleared orders in the paper loop.
When the paper engine is used it seems we can definitely hit races where
order ack msgs arrive close enough to status messages that `trio`
schedules the status processing before the acks. In such cases we want
to be tolerant and not crash but instead warn that we got an
unknown/out-of-order msg.
Quite a simple fix, we just assign the account-specific
`PositionTracker` to the level line's `._on_level_change()` handler
instead of whatever the current `OrderMode.current_pp` is set to.
Further this adds proper pane switching support such that when a user
modifies an order line from an account which is not the currently
selected one, the settings pane is changed to reflect the
account and thus corresponding position info for that account and
instrument B)
We were overwriting the existing loaded orders list in the per client
loop (lul) so move the def above all that.
Comment out the "try-to-cancel-inactive-orders-via-task-after-timeout"
stuff pertaining to https://github.com/erdewit/ib_insync/issues/363 for
now since we don't have a mechanism in place to cancel the re-cancel
task once the order is cancelled - plus who knows if this is even the
best way to do it..
Fills seems to be dual emitted from both the `status` and `fill` events
in `ib_insync` internals and more or less contain the same data nested
inside their `Trade` type. We started handling the 'fill' case to deal
with a race issue in commissions/cost report tracking but we don't
really want to leak that same race to incremental fills vs.
order-"closed" tracking.. So go back to only emitting the fill msgs
on statuses and a "closed" on `.remaining == 0`.
`ib` is super good not being reliable with order event sequence order
and duplication of fill info. This adds some guards to try and avoid
popping the last status status too early if we end up receiving
a `'closed'` before the expected `'fill`' event(s). Further delete the
`status_msg` ref on each iteration to avoid stale reference lookups in
the relay task/loop.
This includes darks, lives and alerts with all connecting clients
being broadcast all existing order-flow dialog states. Obviously
for now darks and alerts only live as long as the `emsd` actor lifetime
(though we will store these in local state eventually) and "live" orders
have lifetimes managed by their respective backend broker.
The details of this change-set is extensive, so here we go..
Messaging schema:
- change the messaging `Status` status-key set to:
`resp: Literal['pending', 'open', 'dark_open', 'triggered',
'closed', 'fill', 'canceled', 'error']`
which better reflects the semantics of order lifetimes and was
partially inspired by the status keys `kraken` provides for their
order-entry API. The prior key set was based on `ib`'s horrible
semantics which sound like they're right out of the 80s..
Also, we reflect this same set in the `BrokerdStatus` msg and likely
we'll just get rid of the separate brokerd-dialog side type
eventually.
- use `Literal` type annots for statuses where applicable and as they
are supported by `msgspec`.
- add additional optional `Status` fields:
-`req: Order` to allow each status msg to optionally ref its
commanding order-request msg allowing at least a request-response
style implicit tracing in all response msgs.
-`src: str` tag string to show the source of the msg.
-`reqid: str | int` such that the ems can relay the `brokerd`
request id both to the client side and have one spot to look
up prior status msgs and
- draft a (unused/commented) `Dialog` type which can be eventually used
at all EMS endpoints to track msg-flow states
EMS engine adjustments/rework:
- use the new status key set throughout and expect `BrokerdStatus` msgs
to use the same new schema as `Status`.
- add a `_DarkBook._active: dict[str, Status]` table which is now used for
all per-leg-dialog associations and order flow state tracking
allowing for the both the brokerd-relay and client-request handler loops
to read/write the same msg-table and provides for delivering
the overall EMS-active-orders state to newly/re-connecting clients
with minimal processing; this table replaces what the `._ems_entries`
table from prior.
- add `Router.client_broadcast()` to send a msg to all currently
connected peers.
- a variety of msg handler block logic tweaks including more `case:`
blocks to be both flatter and improve explicitness:
- for the relay loop move all `Status` msg update and sending to
within each block instead of a fallthrough case plus hard-to-follow
state logic.
- add a specific case for unhandled backend status keys and just log
them.
- pop alerts from `._active` immediately once triggered.
- where possible mutate status msgs fields over instantiating new
ones.
- insert and expect `Order` instances in the dark clearing loop and
adjust `case:` blocks accordingly.
- tag `dark_open` and `triggered` statuses as sourced from the ems.
- drop all the `ChainMap` stuff for now; we're going to make our own
`Dialog` type for this purpose..
Order mode rework:
- always parse the `Status` msg and use match syntax cases with object
patterns, hackily assign the `.req` in many blocks to work around not
yet having proper on-the-wire decoding yet.
- make `.load_unknown_dialog_from_msg()` expect a `Status` with boxed
`.req: Order` as input.
- change `OrderDialog` -> `Dialog` in prep for a general purpose type
of the same name.
`ib` backend order loading support:
- do "closed" status detection inside the msg-relay loop instead
of expecting the ems to do this..
- add an attempt to cancel inactive orders by scheduling cancel
submissions continually (no idea if this works).
- add a status map to go from the 80s keys to our new set.
- deliver `Status` msgs with an embedded `Order` for existing live order
loading and make sure to try an get the source exchange info (instead
of SMART).
Paper engine ported to match:
- use new status keys in `BrokerdStatus` msgs
- use `match:` syntax in request handler loop
Ideally every client that connects to the ems can know its state
(immediately) meaning relay all the order dialogs that are currently
active. This adds full (hacky WIP) support to receive those dialog
(msgs) from the `open_ems()` startup values via the `.started()` msg
from `_emsd_main()`.
Further this adds support to the order mode chart-UI to display existing
(live) orders on the chart during startup. Details include,
- add a `OrderMode.load_unknown_dialog_from_msg()` for processing and
displaying a ``BrokerdStatus`` (for now) msg from the EMS that was not
previously created by the current ems client and registering and
displaying it on the chart.
- break out the ems msg processing into a new
`order_mode.process_trade_msg()` func so that it can be called on the
startup dialog-msg set as well as eventually used a more general low
level auto-strat API (eg. when we get to displaying auto-strat and
group trading automatically on an observing chart UI.
- hackyness around msg-processing for the dialogs delivery since we're
technically delivering `BrokerdStatus` msgs when the client-side
processing technically expects `Status` msgs.. we'll rectify this
soon!
In order to avoid missed existing order message emissions on startup we
need to be sure the client side stream is registered with the router
first. So break out the starting of the
`translate_and_relay_brokerd_events()` task until inside the client
stream block and start the task using the dark clearing loop nursery.
Also, ensure `oid` (and thus for `ib` the equivalent re-used `reqid`)
are cast to `str` before registering the dark book. Deliver the dark
book entries as part of the `_emsd_main()` context `.started()` values.
This seems to have been broken in refactoring from commit 279c899de5
which was never tested against multiple accounts/clients.
The fix is 2 part:
- position tables are now correctly loaded ahead of time and used by
account for each connected client in processing of ledgers and
existing positions.
- a task for each API client is started (as implemented prior) so that
we actually get status updates for every client used for submissions.
Further we add a bit of code using `bisect.insort()` to normalize
ledgers to a datetime sorted list records (though pretty sure the `dict`
transform ruins it?) in an effort to avoid issues with ledger
transaction processing with previously minimized `Position.clears`
tables, which should (but might not?) avoid incorporating clear events
prior to the last "net-zero" positioning state.
This firstly changes `.audit_sizing()` => `.ensure_state()` and makes it
return `None` as well as only error when split ratio denoted (via
config) positions do not size as expected.
Further refinements,
- add an `.expired()` predicate method
- always return a size of zero from `.calc_size()` on expired assets
- load each `pps.toml` entry's clear tabe into `Transaction`s and use
`.add_clear()` during from config init.
In order to avoid issues with reloading ledger and API trades after an
existing `pps.toml` exists we have to make sure we not only avoid
duplicate entries but also avoid re-adding entries that would have been
removed during a prior call to the `Position.minimize_clears()` filter.
The easiest way to do this is to sort on timestamps and avoid adding any
record that pre-existed the last net-zero position ledger event that
`.minimize_clears()` discarded. In order to implement this it means
parsing config file clears table's timestamps into datetime objects for
inequality checks and we add a `Position.first_clear_dt` attr for
storing this value when managing pps in object form but never store it
in the config (since it should be obviously from the sorted clear event
table).
The (partial) fills from this sub are most indicative of clears (also
says support) whereas the msgs in the `ownTrades` sub are only emitted
after the entire order request has completed - there is no size-vlm
remaining.
Further enhancements:
- this also includes proper subscription-syncing inside `subscribe()` with
a small pre-msg-loop which waits on ack-msgs for each sub and raises any
errors. This approach should probably be implemented for the data feed
streams as well.
- configure the `ownTrades` sub to not bother sending historical data on
startup.
- make the `openOrders` sub include rate limit counters.
- handle the rare case where the ems is trying to cancel an order which
was just edited and hasn't yet had it's new `txid` registered.
Since we figured out how to pass through ems dialog ids to the
`openOrders` sub we don't really need to do much with status updates
other then error handling. This drops `process_status()` and moves the
error handling logic into a status handler sub-block; we now just
info-log status updates for troubleshooting purposes.
Why we need so many fields to accomplish passing through a dialog key to
orders is beyond me but this is how they do it with edits..
Allows not having to handle `editOrderStatus` msgs to update the dialog
key table and instead just do it in the `openOrders` sub by checking the
canceled msg for a 'cancel_reason' of 'Order replaced', in which case we
just pop the txid and wait for the new order the kraken backend engine
will submit automatically, which will now have the correct 'userref'
value we passed in via the `newuserref`, and then we add that new `txid`
to our table.
Turns out you can pass both thus making mapping an ems `oid` to
a brokerd-side `reqid` much more simple. This allows us to avoid keeping
as much local dialog state but with still the following caveats:
- ok `editOrder` msgs must update the reqid<->txid map
- only pop `reqids2txids` entries inside the `cancelOrderStatus` handler
If we don't have a pos table built out already (in mem) we can't figure
out the likely dst asset (since there's no pair entry to guide us) that
we should use to search for withdrawal transactions; so move it later.
Further this ports to the new api changes in `piker.pp`` that will land
with #365.
This ended up driving the rework of the `piker.pp` apis to use context
manager + table style which resulted in a much easier to follow
state/update system B). Also added is a flag to do a manual simulation
of a "fill triggered rt pp msg" which requires the user to delete the
last ledgered trade entry from config files and then allowing that trade
to emit through the `openOrders` sub and update client shortly after
order mode boot; this is how the rt updates were verified to work
without doing even more live orders 😂.
Patch details:
- open both `open_trade_ledger()` and `open_pps()` inside the trade
dialog startup and conduct a "pp state sync" logic phase where we now
pull the account balances and incrementally load pp data (in order,
from `pps.toml`, ledger, api) until we can generate the asset balance
by reverse incrementing through trade history eventually erroring out
if we can't reproduce the balance value.
- rework the `trade2pps()` to take in the `PpTable` and generate new
ems msgs from table updates.
- return the new `dict[str, Transaction]` expected from
`norm_trade_records()`
- only update pp config and ledger on dialog exit.
Since our ems doesn't actually do blocking style client-side submission
updates, thus resulting in the client being able to update an existing
order's state before knowing its current state, we can run into race
conditions where for some backends an order is updated using the wrong
order id. For kraken we manually implement detecting this race (lol, for
now anyway) such that when a new client side edit comes in before the
new `txid` is known, we simply expect the handler loop to cancel the
order. Further this adds cancellation on arbitrary status errors, like
rate limits.
Also this adds 2 leg (ems <-> brokerd <-> kraken) msg tracing using
a `collections.ChainMap` which is likely going to end up being the POC
for a more general data structure recommended for backends that need to
trace msg flow for translation with the ems.
Turns out the `openOrders` and `ownTrades` subs always return a `reqid`
value (the one brokerd sends to the kraken api in order requests) is
always set to zero, which seems to be a bug? So this includes patches to
work around that as well reliance on the `openOrders` sub to do most
`BrokerdStatus` updates since `XOrderStatus` events don't seem to have
much data in them at all (they almost look like pure ack events so maybe
they aren't affirmative of final state changes anyway..).
Other fixes:
- respond with a `BrokerdOrderAck` immediately after `requid` generation
not after order submission to ensure the ems has a valid `requid`
*before* kraken api events are relayed through.
- add a `reqids2txids: bidict[int, str]` which maps brokerd genned
`requid`s to kraken-side `txid`s since (as mentioned above) the
clearing and state endpoints don't relay back this value (it's always
0...)
- add log messages for each sub so that (at least for now) we can see
exact msg contents coming from kraken.
- drop `.remaining` calcs for now since we need to keep record of the
order states manually in order to retreive the original submission
vlm..
- fix the `openOrders` case for fills, in this case the message includes
no `status` field and thus we must catch it in a block *after* the
normal state handler to avoid masking.
- drop response msg generation from the cancel status case since we
can do it again from the `openOrders` handler and sending a double
status causes issues on the client side.
- add a shite ton of notes around all this missing `requid` stuff.
More or less just to avoid orders the user wasn't aware of from
persisting until we get "open order relaying" through the ems working.
Some further fixes which required a new `reqids2txids` map which keeps
track of which `kraken` "txid" is mapped to our `reqid: int`; mainly
this was needed for cancel requests which require knowing the underlying
`txid`s (since apparently kraken doesn't keep track of the "reqid" we
pass it). Pass the ws instance into `handle_order_updates()` to enable
the cancelling orders on startup. Don't key error on unknown `reqid`
values (for eg. when receiving historical trade events on startup).
Handle cancel requests first in the ems side loop.
Since we seem to always be able to get back the `reqid`/`userref` value
we send to kraken ws endpoints, we can use this as our brokerd side
order id and avoid all race cases with getting the true `txid` value
that `kraken` assigns (and which changes when you do "edits"
:eyeroll:). This simplifies status updates by allowing our relay loop
just to pass back our generated `.reqid` verbatim and allows responding
with a `BrokerdOrderAck` immediately in the request handler task which
should guarantee there are no further race conditions with the relay
loop and mapping `txid`s from kraken.. and figuring out wtf to do when
they change, etc.
Addressing same issue as in #350 where we need to compute position
updates using the *first read* from the ledger **before** we update it
to make sure `Position.lifo_update()` gets called and **not skipped**
because new trades were read as clears entries but haven't actually been
included in update calcs yet.. aka we call `Position.lifo_update()`.
Main change here is to convert `update_ledger()` into a context mngr so
that the ledger write is committed after pps updates using
`pp.update_pps_conf()`..
This is basically a hotfix to #346 as well.
Turns out the EMS can support this as originally expected: you can
update a `brokerd`-side `.reqid` through a `BrokerdAck` msg and the ems
which update its cross-dialog (leg) tracking correctly! The issue was
a bug in the `editOrderStatus` msg handling and appropriate tracking
of the correct `.oid` (ems uid) on the kraken side. This unfortunately
required adding a `emsflow: dict[str, list[BrokerdOrder]]` msg flow
tracing table which means the broker daemon is tracking all the msg flow
with the ems, though I'm wondering now if this is just good practise
anyway and maybe we should offer a small primitive type from our msging
utils to aid with this? I've used such constructs in event handling
systems prior.
There's a lot more factoring that can be done after these changes as
well but the quick detailed summary is,
- rework the `handle_order_requests()` loop to use `match:` syntax and
update the new `emsflow` table on every new request from the ems.
- fix the `editOrderStatus` case pattern to not include an error msg and
thus actually be triggered to respond to the ems with a `BrokerdAck`
containing the new `.reqid`, the new kraken side `txid`.
- skip any `openOrders` msgs which are detected as being kraken's
internal order "edits" by matching on the `cancel_reason` field.
- update the `emsflow` table in all ws-stream msg handling blocks
with responses sent to the ems.
Relates to #290
Move to using the websocket API for all order control ops and dropping
the sync rest api approach which resulted in a bunch of buggy races.
Further this gets us must faster (batch) order cancellation for free
and a simpler ems request handler loop. We now heavily leverage the new
py3.10 `match:` syntax for all kraken-side API msg parsing and
processing and handle both the `openOrders` and `ownTrades` subscription
streams.
We also block "order editing" (by immediate cancellation) for now since
the EMS isn't entirely yet equipped to handle brokerd side `.reqid`
changes (which is how kraken implements so called order "updates" or
"edits") for a given order-request dialog and we may want to even
consider just implementing "updates" ourselves via independent cancel
and submit requests? Definitely something to ponder. Alternatively we
can "masquerade" such updates behind the count-style `.oid` remapping we
had to implement anyway (kraken's limitation) and maybe everything will
just work?
Further details in this patch:
- create 2 tables for tracking the EMS's `.oid` (uui4) value to `int`s
that kraken expects (for `reqid`s): `ids` and `reqmsgs` which enable
local lookup of ems uids to piker-backend-client-side request ids and
received order messages.
- add `openOrders` sub support which more or less directly relays to
equivalent `BrokerdStatus` updates and calc the `.filled` and
`.remaining` values based on cleared vlm updates.
- add handler blocks for `[add/edit/cancel]OrderStatus` events including
error msg cases.
- don't do any order request response processing in
`handle_order_requests()` since responses are always received via one
(or both?) of the new ws subs: `ownTrades` and `openOrders` and thus
such msgs are now handled in the response relay loop.
Relates to #290Resolves#310, #296
This drops the use of `pp.update_pps_conf()` (and friends) and instead
moves to using the context style `open_trade_ledger()` and `open_pps()`
managers for faster pp msg gen due to delayed file writing (which was
the main source update latency).
In order to make this work with potentially multiple accounts this also
uses an exit stack which loads each ledger / `pps.toml` into an account
id mapped `dict`; a POC for likely how we should implement some higher
level position manager api.
The original implementation of `.calc_be_price()` wasn't correct since
the real so called "price per unit" (ppu), is actually defined by
a recurrence relation (which is why the original state-updated
`.lifo_update()` approach worked well) and requires the previous ppu to
be weighted by the new accumulated position size when considering a new
clear event. The ppu is the price that above or below which the trader
takes a win or loss on transacting one unit of the trading asset and
thus it is the true "break even price" that determines making or losing
money per fill. This patches fixes the implementation to use trailing
windows of the accumulated size and ppu to compute the next ppu value
for any new clear event as well as handle rare cases where the
"direction" changes polarity (eg. long to short in a single order). The
new method is `Position.calc_ppu()` and further details of the relation
can be seen in the doc strings.
This patch also includes a wack-ton of clean ups and removals in an
effort to refine position management api for easier use in new backends:
- drop `updaate_pps_conf()`, `load_pps_from_toml()` and rename
`load_trands_from_ledger()` -> `load_pps_from_ledger()`.
- extend `PpTable` to have a `.to_toml()` method which returns the
active set of positions ready to be serialized to the `pps.toml` file
which is collects from calling,
- `PpTable.dump_active()` which now returns double dicts of the
open/closed pp object maps.
- make `Position.minimize_clears()` now iterate the clears table in
chronological order (instead of reverse) and only drop fills prior
to any zero-size state (the old reversed way can result incorrect
history-size-retracement in cases where a position is lessened but
not completely exited).
- drop `Position.add_clear()` and instead just manually add entries
inside `.update_from_trans()` and also add a `accum_size` and `ppu`
field to ever entry thus creating a position "history" sequence of
the ppu and accum size for every position and prepares for being
and to show "position lifetimes" in the UI.
- move fqsn getting into `Position.to_pretoml()`.
Use the new `.calc_[be_price/size]()` methods when serializing to and
from the `pps.toml` format and add an audit method which will warn about
mismatched values and assign the clears table calculated values pre-write.
Drop the `.lifo_update()` method and instead allow both
`.size`/`.be_price` properties to exist (for non-ledger related uses of
`Position`) alongside the new calc methods and only get fussy about
*what* the properties are set to in the case of ledger audits.
Also changes `Position.update()` -> `.add_clear()`.
Since we're going to need them anyway for desired features, add
2 new `Position` methods:
- `.calc_be_price()` which computes the breakeven cost basis price
from the entries in the clears table.
- `.calc_size()` which just sums the clear sizes.
Add a `cost_scalar: float` control to the `.update_from_trans()` method
to allow manual adjustment of the cost weighting for the case where
a "non-symmetrical" model is wanted.
Go back to always trying to write the backing ledger files on exit, even
when there's an error (obvs without the `return` in the `finally:` block
f$#% up).
Can't believe i missed this but any `return` inside a `finally` will
suppress the error from the `try:` part... XD
Thought i was losing my mind when the ledger was mutated and then
an error just after wasn't getting raised.. lul.
Never again...
In order to avoid double transaction adds/updates and too-early-discard
of zero sized pps (like when trades are loaded from a backend broker but
were already added to a ledger or `pps.toml` prior) we now **don't** pop
such `Position` entries from the `.pps` table in order to keep each
position's clears table always in place. This avoids the edge case where
an entry was removed too early (due to zero size) but then duplicate
trade entries that were in that entrie's clears show up from the backend
and are entered into a new entry resulting in an incorrect size in a new
entry..We still only push non-net-zero entries to the `pps.toml`.
More fixes:
- return the updated set of `Positions` from `.lifo_update()`.
- return the full table set from `update_pps()`.
- use `PpTable.update_from_trans()` more throughout.
- always write the `pps.toml` on `open_pps()` exit.
- only return table from `load_pps_from_toml()`.
In an effort to begin allowing backends to have more granular control
over position updates, particular in the case where they need to be
reloaded from a trades ledger, this adds a new table API which can
be loaded using `open_pps()`.
- offer an `.update_trans()` method which takes in a `dict` of
`Transactions` and updates the current table of `Positions` from it.
- add a `.dump_active()` which renders the active pp entries dict in
a format ready for toml serialization and all closed positions since
the last update (we might want to not drop these?)
All other module-function apis currently in use should remain working as
before for the moment.
Change `.find_contract()` -> `.find_contracts()` to allow multi-search
for so called "ambiguous" contracts (like for `Future`s) such that the
method now returns a `list` of tracts and populates the contract cache
with all specific tracts retrieved. Let it take in an (unvalidated)
contract that will be fqsn-style-tokenized such that it can be called
from `.search_symbols()` (though we're not quite yet XD).
More stuff,
- add `Client.parse_patt2fqsn()` which is an fqsn to token unpacker
built from the original logic in the old `.find_contract()`.
- handle fiat/forex pairs with the `'CASH'` sectype.
- add a flag to allow unqualified contracts to fail with a warning msg.
- populate the client's contract cache with all expiries of
an ambiguous derivative.
- allow `.con_deats()` to warn msg instead of raise on def-not-found.
- add commented `assert 0` which was triggering a debugger deadlock in
`tractor` which we still haven't been able to create a unit test for.
Minimize calling `.data._shmarray.attach_shm_array()` as much as is
possible to avoid the crash from #332. This is the suggested hack from
issue #359.
Resolves https://github.com/pikers/piker/issues/359
Not sure why I put this off for so long but the check is in now such
that if the market isn't open or no rt quote comes in from the first
query, we just pull from the last shm history 'close' value.
Includes another fix to avoid raising when a double remove on the client
side stream from the registry sometimes happens.
Not sure this didn't get caught in usage, but basically real-time
updates got broken by a rework of `update_ledger_from_api_trades()`.
The issue is that the ledger was being updated **before** calling
`piker.pp.update_pps_conf()` which resulted in the `Position.size`
not being updated correctly since the [latest added] clears passed
in via the `trade_records` arg were already found in the `.clears` table
and thus were causing the loop to skip the `Position.lifo_update()`
call..
The solution here is to not update the ledger **until after** we call
`update_pps_conf()` - it's more read/writes but it's correct and we
figure out a less io heavy way to do the file writing later.
Further this includes a fix to avoid double emitting a pp update caused
by non-thorough logic that waits for a commission report to arrive
during a fill event; previously we were emitting the same message twice
due to the lack of a check for an existing comms report in the case
where the report arrives *after* the fill.
Moves to using the new `piker.pp` apis to both store real-time trade
events in a ledger file as well emit position update msgs (which were
not in this backend at all prior) when new orders clear (aka fill).
In terms of outstanding issues,
- solves the pp update part of the bugs reported in #310
- starts a msg case block in prep for #293
Details of rework:
- move the `subscribe()` ws fixture to module level and `partial()` in
the client token instead of passing it to the instance; in prep for
removal of the `.token` attr from the `NoBsWs` wrapper.
- drop `make_auth_sub()` since it was too thin and we can just
do it all succinctly in `subscribe()`
- filter trade update msgs to those not yet stored int the toml ledger
- much better kraken api msg unpacking using new `match:` synax B)
Resolves#311
No real-time update support (yet) but this is the first draft at writing
trades ledgers and `pps.toml` entries for the kraken backend.
Deatz:
- drop `pack_positions()`, no longer used.
- use `piker.pp` apis to both write a trades ledger file and update the
`pps.toml` inside the `trades_dialogue()` endpoint startup.
- drop the weird paper engine swap over if auth can't be done, we should
be doing something with messaging in the ems over this..
- more web API error response raising.
- pass the `pp.Transaction` set loaded from ledger into
`process_trade_msgs()` do avoid duplicate sends of already collected
trades msgs.
- add `norm_trade_records()` public endpoing (used by `piker.pp` api)
and `update_ledger()` helper.
- rejig `process_trade_msgs()` to drop the weird `try:` assertion block
and skip already-recorded-in-ledger trade msgs as well as yield *each*
trade instead of sub-sequences.
This was just implemented totally wrong but somehow worked XD
The idea was to include all trades that contribute to ongoing position
size since the last time the position was "net zero", i.e. no position
in the asset. Adjust arithmetic to *subtract* from the current size
until a zero size condition is met and then keep all those clears as
part of the "current state" clears table.
Additionally this fixes another bug where the positions freshly loaded
from a ledger *were not* being merged with the current `pps.toml` state.
Gah, was a remaining bug where if you tried to update the pps state with
both new trades and from the ledger you'd do a double add of
transactions that were cleared during a `update_pps()` loop. Instead now
keep all clears in tact until ready to serialize to the `pps.toml` file
in which cases we call a new method `Position.minimize_clears()` which
does the work of only keep clears since the last net-zero size.
Re-implement `update_pps_conf()` update logic as a single pass loop
which does expiry and size checking for closed pps all in one pass thus
allowing us to drop `dump_active()` which was kinda redundant anyway..
Before we weren't emitting pp msgs when a position went back to "net
zero" (aka the size is zero) nor when a new one was opened (wasn't
previously loaded from the `pps.toml`). This reworks a bunch of the
incremental update logic as well as ports to the changes in the
`piker.pp` module:
- rename a few of the normalizing helpers to be more explicit.
- drop calling `pp.get_pps()` in the trades dialog task and instead
create msgs iteratively, per account, by iterating through collected
position and API trade records and calling instead
`pp.update_pps_conf()`.
- always from-ledger-update both positions reported from ib's pp sys and
session api trades detected on ems-trade-dialog startup.
- `update_ledger_from_api_trades()` now does **just** that: only updates
the trades ledger and returns the transaction set.
- `update_and_audit_msgs()` now only the input list of msgs and properly
generates new msgs for newly created positions that weren't previously
loaded from the `pps.toml`.
- use `tomli` package for reading since it's the fastest pure python
reader available apparently.
- add new fields to each pp's clears table: price, size, dt
- make `load_pps_from_toml()`'s `reload_records` a dict that can be
passed in by the caller and is verbatim used to re-read a ledger and
filter to the specified symbol set to build out fresh pp objects.
- add a `update_from_ledger: bool` flag to `load_pps_from_toml()`
to allow forcing a full backend ledger read.
- if a set of trades records is passed into `update_pps_conf()` parse
out the meta data required to cause a ledger reload as per 2 bullets
above.
- return active and closed pps in separate by-account maps from
`update_pps_conf()`.
- drop the `key_by` kwarg.
This makes it possible to refresh a single fqsn-position in one's
`pps.toml` by simply deleting the file entry, in which case, if there is
new trade records passed to `load_pps_from_toml()` via the new
`reload_records` kwarg, then the backend ledger entries matching that
symbol will be filtered and used to recompute a fresh position.
This turns out to be super handy when you have crashes that prevent
a `pps.toml` entry from being updated correctly but where the ledger
does have all the data necessary to calculate a fresh correct entry.
Since some positions obviously expire and thus shouldn't continually
exist inside a `pps.toml` add naive support for tracking and discarding
expired contracts:
- add `Transaction.expiry: Optional[pendulum.datetime]`.
- add `Position.expiry: Optional[pendulum.datetime]` which can be parsed
from a transaction ledger.
- only write pps with a non-none expiry to the `pps.toml`
- change `Position.avg_price` -> `.be_price` (be is "breakeven")
since it's a much less ambiguous name.
- change `load_pps_from_legder()` to *not* call `dump_active()` since
for the only use case it ends up getting called later anyway.
We can probably make this better (and with less file sys accesses) later
such that we keep a consistent pps state in mem and only write async
maybe from another side-task?
What a nightmare this was.. main holdup was that cost (commissions)
reports are fired independent from "fills" so you can't really emit
a proper full position update until they both arrive.
Deatz:
- move `push_tradesies()` and relay loop in `deliver_trade_events()` to
the new py3.10 `match:` syntax B)
- subscribe for, and handle `CommissionReport` events from `ib_insync`
and repack as a `cost` event type.
- handle cons with no primary/listing exchange (like futes) in
`update_ledger_from_api_trades()` by falling back to the plain
'exchange' field.
- drop reverse fqsn lookup from ib positions map; just use contract
lookup for api trade logs since we're already connected..
- make validation in `update_and_audit()` optional via flag.
- pass in the accounts def, ib pp msg table and the proxies table to the
trade event relay task-loop.
- add `emit_pp_update()` too encapsulate a full api trade entry
incremental update which calls into the `piker.pp` apis to,
- update the ledger
- update the pps.toml
- generate a new `BrokerdPosition` msg to send to the ems
- adjust trades relay loop to only emit pp updates when a cost report
arrives for the fill/execution by maintaining a small table per exec
id.
I don't want to rant too much any more since it's pretty clear `ib` has
either zero concern for its (api) user's or a severely terrible data
management team and/or general inter-team coordination system, but this
patch more or less hacks the flex report records to be similar enough to
API "execution" / "fill" records such that they can be similarly
normalized and stored as well as processed for position calculations..
Dirty deats,
- use the `IB.fills()` method for pulling current session trade events
since it's both recommended in the docs and does seem to capture
more extensive meta-data.
- add a `update_ledger_from_api()` helper which does all the insane work
of making sure api trade entries are usable both within piker's global
fqsn system but also compatible with incremental updates of positions
computed from trade ledgers derived from ib's "flex reports".
- add "auditting" of `ib`'s reported positioning API messages by
comparison with piker's new "traders first" breakeven price style and
complain via logging on mismatches.
- handle buy vs. sell arithmetic (via a +ve or -ve multiplier) to make
"size" arithmetic work for API trade entries..
- draft out options contract transaction parsing but skip in pps
generation for now.
- always use the "execution id" as ledger keys both in flex and api
trade processing.
- for whatever weird reason `ib_insync` doesn't include the so called
"primary exchange" in contracts reported in fill events, so do manual
contract lookups in such cases such that pps entries can be placed
in the right fqsn section...
Still ToDo:
- incremental update on trade clears / position updates
- pps audit from ledger depending on user config?
This makes a few major changes but mostly is centered around including
transaction (aka trade-clear) costs in the avg breakeven price
calculation.
TL;DR:
- rename `TradeRecord` -> `Transaction`.
- make `Position.fills` a `dict[str, float]` which holds each clear's
cost value.
- change `Transaction.symkey` -> `.bsuid` for "backend symbol unique id".
- drop `brokername: str` arg to `update_pps()`
- rename `._split_active()` -> `dump_active()` and use input keys
verbatim in output map.
- in `update_pps_conf()` always incrementally update from trade records
even when no `pps.toml` exists yet since it may be both the case that
the ledger needs loading **and** the caller is handing new records not
yet in the ledger.
Begins the position tracking incremental update API which supports both
constructing a `pps.toml` both from trade ledgers as well diff-oriented
incremental update from an existing config assumed to be previously
generated from some prior ledger.
New set of routines includes:
- `_split_active()` a helper to split a position table into the active
and closed positions (aka pps of size 0) for determining entry updates
in the `pps.toml`.
- `update_pps_conf()` to maybe load a `pps.toml` and update it from
an input trades ledger including necessary (de)serialization to and
from `Position` object form(s).
- `load_pps_from_ledger()` a ledger parser-loader which constructs
a table of pps strictly from the broker-account ledger data without
any consideration for any existing pps file.
Each "entry" in `pps.toml` also contains a `fills: list` attr (name may
change) which references the set of trade records which make up its
state since the last net-zero position in the instrument.
Add a `TradeRecord` struct which holds the minimal field set to build
out position entries. Add `.update_pps()` to convert a set of records
into LIFO position entries, optionally allowing for an update to some
existing pp input set. Add `load_pps_from_ledger()` which does a full
ledger extraction to pp objects, ready for writing a `pps.toml`.
Since "flex reports" are only available for the current session's trades
the day after, this adds support for also collecting trade execution
records for the current session and writing them to the equivalent
ledger file.
Summary:
- add `trades_to_records()` to handle parsing both flex and API event
objects into a common record form.
- add `norm_trade_records()` to handle converting ledger entries into
`TradeRecord` types from the new `piker.pps` mod (coming in next
commit).
Start a generic "position related" util mod and bring in the `Position`
type from the allocator , convert it to a `msgspec.Struct` and add
a `.lifo_update()` method. Implement a WIP pp parser from a trades
ledger and use the new lifo method to gather position entries.
Add `ChartPlotWidget._on_screen: bool` which allows detecting for the
first state where there is y-range-able flow data loaded and able to be
drawn. Check for this flag to be set in `.maxmin()` such that until the
historical data is loaded `.default_view()` will be called to ensure
that a blank view is never shown: race with the UI starting versus the
data layer loading flow graphics can have this outcome.
This should hopefully make teardown more reliable and includes better
logic to fail over to a hard kill path after a 3 second timeout waiting
for the instance to complete using the `docker-py` wait API. Also
generalize the supervisor teardown loop by allowing the container config
endpoint to return 2 msgs to expect:
- a startup message that can be read from the container's internal
process logging that indicates it is fully up and ready.
- a teardown msg that can be polled for that indicates the container has
gracefully terminated after a cancellation request which is passed to
our container wrappers `.cancel()` method.
Make the marketstore config endpoint return the 2 messages we previously
had hard coded and use this new api.
This was introduced in #302 but after thorough testing was clear to be
not working XD. Adjust the display loop to update the last graphics
segment on both the OHLC and vlm charts (as well as all deriving fsp
flows) whenever the uppx >= 1 and there is no current path append
taking place (since more datums are needed to span an x-pixel in view).
Summary of tweaks:
- move vlm chart update code to be at the end of the cycle routine and
have that block include the tests for a "interpolated last datum in
view" line.
- make `do_append: bool` compare with a floor of the uppx value (i.e.
appends should happen when we're just fractionally over a pixel of
x units).
- never update the "volume" chart.
Allows for optionally updating a "downsampled" graphics type which is
currently necessary in the `BarItems` -> `FlattenedOHLC` curve switching
case; we don't want to be needlessly redrawing the `Flow.graphics`
object (which will be an OHLC curve) when in flattened curve mode.
Further add a `only_last_uppx: bool` flag to `.draw_last()` to allow
forcing a "last uppx's worth of data max/min" style interpolating line
as needed.
The single-file module was getting way out of hand size-wise with the
new flex report parsing stuff so this starts the process of breaking
things up into smaller modules oriented around trade, data, and ledger
related endpoints.
Add support for backends to declare sub-modules to enable in
a `__enable_modules__: list[str]` module var which is parsed by the
daemon spawning code passed to `tractor`'s `enable_modules: list[str]`
input.
When using m4, we downsample to the max and min of each
pixel-column's-worth of data thus preserving range / dispersion details
whilst not drawing more graphics then can be displayed by the available
amount of horizontal pixels.
Take and apply this exact same concept to the "last datum" graphics
elements for any `Flow` that is reported as being in a downsampled
state:
- take the xy output from the `Curve.draw_last_datum()`,
- slice out all data that fits in the last pixel's worth of x-range
by using the uppx,
- compute the highest and lowest value from that data,
- draw a singe line segment which spans this yrange thus creating
a simple vertical set of pixels which are "filled in" and show the
entire y-range for the most recent data "contained with that pixel".
Instead of using a bunch of internal logic to modify low level paint-able
elements create a `Curve` lineage that allows for graphics "style"
customization via a small set of public methods:
- `Curve.declare_paintables()` to allow setup of state/elements to be
drawn in later methods.
- `.sub_paint()` to allow painting additional elements along with the
defaults.
- `.sub_br()` to customize the `.boundingRect()` dimensions.
- `.draw_last_datum()` which is expected to produce the paintable
elements which will show the last datum in view.
Introduce the new sub-types and load as necessary in
`ChartPlotWidget.draw_curve()`:
- `FlattenedOHLC`
- `StepCurve`
Reimplement all `.draw_last()` routines as a `Curve` method
and call it the same way from `Flow.update_graphics()`
The basic logic is now this:
- when zooming out, uppx (units per pixel in x) can be >= 1
- if the uppx is `n` then the next pixel in view becomes occupied by
a new datum-x-coordinate-value when the diff between the last
datum step (since the last such update) is greater then the
current uppx -> `datums_diff >= n`
- if we're less then some constant uppx we just always update (because
it's not costly enough and we're not downsampling.
More or less this just avoids unnecessary real-time updates to flow
graphics until they would actually be noticeable via the next pixel
column on screen.
This was a bit of a nightmare to figure out but, it seems that the
coordinate caching system will really be a dick (like the nickname for
richard for you serious types) about leaving stale graphics if we don't
reset the cache on downsample full-redraw updates...Sooo, instead we do
this manual reset to avoid such artifacts and consequently (for now)
return a `reset: bool` flag in the return tuple from `Renderer.render()`
to indicate as such.
Some further shite:
- move the step mode `.draw_last()` equivalent graphics updates down
with the rest..
- drop some superfluous `should_redraw` logic from
`Renderer.render()` and compound it in the full path redraw block.
Adds a new pre-graphics data-format callback incremental update api to
our `Renderer`. `Renderer` instance can now overload these custom routines:
- `.update_xy()` a routine which accepts the latest [pre/a]pended data
sliced out from shm and returns it in a format suitable to store in
the optional `.[x/y]_data` arrays.
- `.allocate_xy()` which initially does the work of pre-allocating the
`.[x/y]_data` arrays based on the source shm sizing such that new
data can be filled in (to memory).
- `._xy_[first/last]: int` attrs to track index diffs between src shm
and the xy format data updates.
Implement the step curve data format with 3 super simple routines:
- `.allocate_xy()` -> `._pathops.to_step_format()`
- `.update_xy()` -> `._flows.update_step_xy()`
- `.format_xy()` -> `._flows.step_to_xy()`
Further, adjust `._pathops.gen_ohlc_qpath()` to adhere to the new
call signature.
We're doing this in `Flow.update_graphics()` atm and probably are going
to in general want custom graphics objects for all the diff curve / path
types. The new flows work seems to fix the bounding rect width calcs to
not require the ad-hoc extra `+ 1` in the step mode case; before it was
always a bit hacky anyway. This also tries to add a more correct
bounding rect adjustment for the `._last_line` segment.
Finally this gets us much closer to a generic incremental update system
for graphics wherein the input array diffing, pre-graphical format data
processing, downsampler activation and incremental update and storage of
any of these data flow stages can be managed in one modular sub-system
:surfer_boi:.
Dirty deatz:
- reorg and move all path logic into `Renderer.render()` and have it
take in pretty much the same flags as the old
`FastAppendCurve.update_from_array()` and instead storing all update
state vars (even copies of the downsampler related ones) on the
renderer instance:
- new state vars: `._last_uppx, ._in_ds, ._vr, ._avr`
- `.render()` input bools: `new_sample_rate, should_redraw,
should_ds, showing_src_data`
- add a hack-around for passing in incremental update data (for now)
via a `input_data: tuple` of numpy arrays
- a default `uppx: float = 1`
- add new render interface attrs:
- `.format_xy()` which takes in the source data array and produces out
x, y arrays (and maybe a `connect` array) that can be passed to
`.draw_path()` (the default for this is just to slice out the index
and `array_key: str` columns from the input struct array),
- `.draw_path()` which takes in the x, y, connect arrays and generates
a `QPainterPath`
- `.fast_path`, for "appendable" updates like there was on the fast
append curve
- move redraw (aka `.clear()` calls) into `.draw_path()` and trigger
via `redraw: bool` flag.
- our graphics objects no longer set their own `.path` state, it's done
by the `Flow.update_graphics()` method using output from
`Renderer.render()` (and it's state if necessary)
A bit hacky to get all graphics types working but this is hopefully the
first step toward moving all the generic update logic into `Renderer`
types which can be themselves managed more compactly and cached per
uppx-m4 level.
Which is basically just "deleting" rows from a column series.
You can only use the trim command from the `.cmd` cli and only with a so
called `LocalClient` currently; it's also sketchy af and caused
a machine to hang due to mem usage..
Ideally we can patch in this functionality for use by the rpc api
and have it not hang like this XD
Pertains to https://github.com/alpacahq/marketstore/issues/264
Yet another path ops routine which converts a 1d array into a data
format suitable for rendering a "step curve" graphics path (aka a "bar
graph" but implemented as a continuous line).
Also, factor the `BarItems` rendering logic (which determines whether to
render the literal bars lines or a downsampled curve) into a routine
`render_baritems()` until we figure out the right abstraction layer for
it.
Starts a module for grouping together all our `QPainterpath` related
generation and data format operations for creation of fast curve
graphics. To start, drops `FastAppendCurve.downsample()` and moves
it to a new `._pathops.xy_downsample()`.
Mostly just dropping old commented code for "step mode" format
generation. Always slice the tail part of the input data and move to the
new `ms_threshold` in the `pg` profiler'
Relates to the bug discovered in #310, this should avoid out-of-order
msgs which do not have a `.reqid` set to be error logged to console.
Further, add `pformat()` to kraken logging of ems msging.
Since downsampling with the more correct version of m4 (uppx driven
windows sizing) is super fast now we don't need to avoid downsampling
on low uppx values. Further all graphics objects now support in-view
slicing so make sure to use it on interaction updates. Pass in the view
profiler to update method calls for more detailed measuring.
Even moar,
- Add a manual call to `.maybe_downsample_graphics()` inside the mouse
wheel event handler since it seems that sometimes trailing events get
lost from the `.sigRangeChangedManually` signal which can result in
"non-downsampled-enough" graphics on chart given the scroll amount;
this manual call seems to entirely fix this?
- drop "max zoom" guard since internals now support (near) infinite
scroll out to graphics becoming a single pixel column line XD
- add back in commented xrange signal connect code for easy testing to
verify against range updates not happening without it
This took longer then i care to admit XD but it definitely adds a huge
speedup and with only a few outstanding correctness bugs:
- panning from left to right causes strange trailing artifacts in the
flows fsp (vlm) sub-plot but only when some data is off-screen on the
left but doesn't appear to be an issue if we keep the `._set_yrange()`
handler hooked up to the `.sigXRangeChanged` signal (but we aren't
going to because this makes panning way slower). i've got a feeling
this is a bug todo with the device coordinate cache stuff and we may
need to report to Qt core?
- factoring out the step curve logic from
`FastAppendCurve.update_from_array()` (un)fortunately required some
logic branch uncoupling but also meant we needed special input controls
to avoid things like redraws and curve appends for special cases,
this will hopefully all be better rectified in code when the core of
this method is moved into a renderer type/implementation.
- the `tina_vwap` fsp curve now somehow causes hangs when doing erratic
scrolling on downsampled graphics data. i have no idea why or how but
disabling it makes the issue go away (ui will literally just freeze
and gobble CPU on a `.paint()` call until you ctrl-c the hell out of
it). my guess is that something in the logic for standard line curves
and appends on large data sets is the issue?
Code related changes/hacks:
- drop use of `step_path_arrays_from_1d()`, it was always a bit hacky
(being based on `pyqtgraph` internals) and was generally hard to
understand since it returns 1d data instead of the more expected (N,2)
array of "step levels"; instead this is now implemented (uglily) in
the `Flow.update_graphics()` block for step curves (which will
obviously get cleaned up and factored elsewhere).
- add a bunch of new flags to the update method on the fast append
curve: `draw_last: bool`, `slice_to_head: int`, `do_append: bool`,
`should_redraw: bool` which are all controls to aid with previously
mentioned issues specific to getting step curve updates working
correctly.
- add a ton of commented tinkering related code (that we may end up
using) to both the flow and append curve methods that was written as
part of the effort to get this all working.
- implement all step curve updating inline in `Flow.update_graphics()`
including prepend and append logic for pre-graphics incremental step
data maintenance and in-view slicing as well as "last step" graphics
updating.
Obviously clean up commits coming stat B)
Since we have in-view style rendering working for all curve types
(finally) we can avoid the guard for low uppx levels and without losing
interaction speed. Further don't delay the profiler so that the nested
method calls correctly report upward - which wasn't working likely due
to some kinda GC collection related issue.
More or less this improves update latency like mad. Only draw data in
view and avoid full path regen as much as possible within a given
(down)sampling setting. We now support append path updates with in-view
data and the *SPECIAL CAVEAT* is that we avoid redrawing the whole curve
**only when** we calc an `append_length <= 1` **even if the view range
changed**. XXX: this should change in the future probably such that the
caller graphics update code can pass a flag which says whether or not to
do a full redraw based on it knowing where it's an interaction based
view-range change or a flow update change which doesn't require a full
path re-render.
After much effort (and exhaustion) but failure to get a view into our
`numpy` OHLC struct-array, this instead allocates an in-thread-memory
array which is updated with flattened data every flow update cycle.
I need to report what I think is a bug to `numpy` core about the whole
view thing not working but, more or less this gets the same behaviour
and minimizes work to flatten the sampled data for line-graphics drawing
thus improving refresh latency when drawing large downsampled curves.
Update the OHLC ds curve with view aware data sliced out from the
pre-allocated and incrementally updated data (we had to add a last index
var `._iflat` to track appends - this should be moved into a renderer
eventually?).
This begins the removal of data processing / analysis methods from the
chart widget and instead moving them to our new `Flow` API (in the new
module introduce here) and delegating the old chart methods to the
respective internal flow. Most importantly is no longer storing the
"last read" of an array from shm in an internal chart table (was
`._arrays`) and instead the `ShmArray` instance is passed as input and
stored in the `Flow` instance. This greatly simplifies lookup logic such
that the display loop now doesn't have to worry about reading shm, it
can be done by internal graphics logic as desired. Generally speaking,
all previous `._arrays`/`._graphics` lookups are now delegated to the
entries in the chart's `._flows` table.
The new `Flow` methods are generally better factored and provide more
detailed output regarding data-stream <-> graphics inter-relations for
the future purpose of allowing much more efficient update calls in the
display loop as well as supporting low latency interaction UX.
The concept here is that we're introducing an intermediary layer that
ties together graphics and real-time data flows such that widget code is
oriented around plot layout and the flow apis are oriented around
real-time low latency updates and providing an efficient high level
metric layer for the UX.
The summary api transition is something like:
- `update_graphics_from_array()` -> `.update_graphics_from_flow()`
- `.bars_range()` -> `Flow.datums_range()`
- `.bars_range()` -> `Flow.datums_range()`
Given that naming the port map is mostly pointless, since accounts can
be detected once the client connects, just expect a `brokers.toml` to
define a simple sequence of port numbers. Toss in a warning for using
the old map/`dict` style.
Now that we have working client auth thanks to:
https://github.com/barneygale/asyncvnc/pull/4 and related issue,
we can use a pw for the vnc server, though we should eventually
auto-generate a random one from a docker super obviously.
Add logic to the data reset hack loop to do a connection reset after
2 failed/timeout attempts at the regular data reset. We need to also add
this logic around reconnectionn events that are due to the host
network connection: aka roaming that's faster then timing logic
builtin to the gateway.
`ib-gw` seems particularly fragile to connections from clients with the
same id (can result in weird connect hangs and even crashes) and
`ib_insync` doesn't handle intermittent tcp disconnects that
well..(especially on dockerized IBC setups). This adds a bunch of
changes to our client caching and scan loop as well a proper
task-locking-to-cache-proxies so that,
- `asyncio`-side clients aren't double-loaded/connected even when
explicitly trying to reconnect repeatedly with a given client to work
around the unreliability of the `asyncio.Transport` design in
`ib_insync`.
- we can use `tractor.trionics.maybe_open_context()` to lock the `trio`
side from loading more then one `Client` on the `asyncio` side and
instead on cache hits only making a new `MethodProxy` around the
reused `asyncio`-side client (since each `trio` task needs its own
inter-task msg channel).
- a `finally:` block teardown on all clients loaded in the scan loop
avoids stale connections.
- the connect params are now exposed as named args to
`load_aio_clients()` can be easily controlled from caller code.
Oh, and we properly hooked up the internal `ib_insync` logging to our
own internal schema - makes it a lot easier to debug wtf is going on XD
In order to expose more `asyncio` powered `Client` methods to endpoint
task-code this adds a more extensive and layered set of `MethodProxy`
loading routines, in dependency order these are:
- `load_clients_for_trio()` a `tractor.to_asyncio.open_channel_from()`
entry-point factory for loading all scanned clients on the `asyncio` side
and delivering them over the inter-task channel to a `trio`-side task.
- `get_preferred_data_client()` a simple client instance loading routine
which reads from the users `brokers.toml -> `prefer_data_account:
list[str]` which must list account names, in priority order, that are
acceptable to be used as the main "data connection client" such that
only one of the detected clients is used for data (whereas the rest
are used only for order entry).
- `open_client_proxies()` which delivers the detected `Client` set
wrapped each in a `MethodProxy`.
- `open_data_client()` which directly delivers the preferred data client
as a proxy for `trio` tasks.
- update `open_client_method_proxy()` and `open_client_proxy` to require
an input `Client` instance.
Further impl details:
- add `MethodProxy._aio_ns` to ref the original `asyncio` side proxied instance
- add `Client.trades()` to pull executions from the last day/session
- load proxies inside `trades_dialogue` and use the new `.trades()`
method to try and pull a fill ledger for eventual correct pp price
calcs (pertains to #307)..
We return a copy (since since a view doesn't seem to work..) of the
(field filtered) shm array contents which is the same index-length as
the source data.
Further, fence off the resource tracker disable-hack into a helper
routine.
It seems once in a while a frame can get missed or dropped (at least
with binance?) so in those cases, when the request erlangs is already at
max, we just manually request the missing frame and presume things will
work out XD
Further, discard out of order frames that are "from the future" that
somehow end up in the async queue once in a while? Not sure why this
happens but it seems thus far just discarding them is nbd.
Bleh/🤦, the ``end_dt`` in scope is not the "earliest" frame's
`end_dt` in the async response queue.. Parse the queue's latest epoch
and use **that** to compare to the last last pushed datetime index..
Add more detailed logging to help debug any (un)expected datetime index
gaps.
When the tsdb has a last datum that is in the past less then a "frame's
worth" of sample steps we need to slice out only the data from the
latest frame that doesn't overlap; this fixes that slice logic..
Previously i dunno wth it was doing..
When the market isn't open the feed layer won't create a subscriber
entry in the sampler broadcast loop and so if a manual call to
``broadcast()`` is made (like when trying to update a chart from
a history prepend) we need to handle that case and just broadcast
a random `-1` for now..BD
Expect each backend to deliver a `config: dict[str, Any]` which provides
concurrency controls to `trimeter`'s batch task scheduler such that
backends can define their own concurrency limits.
The dirty deats in this patch include handling history "gaps" where
a query returns a history-frame-result which spans more then the typical
frame size (in seconds). In such cases we reset the target frame index
(datetime index sequence implemented with a `pendulum.Period`) using
a generator protocol `.send()` such that the sequence can be dynamically
re-indexed starting at the new (possibly) pre-gap datetime. The new gap
logic also allows us to detect out of order frames easier and thus wait
for the next-in-order to arrive before making more requests.
Use the new `open_history_client()` endpoint/API and expect backends to
provide a history "getter" routine that can be called to load historical
data into shm even when **not** using a tsdb. Add logic for filling in
data from the tsdb once the backend has provided data up to the last
recorded in the db. Add logic for avoiding overruns of the shm buffer
with more-then-necessary queries of tsdb data.
It turns out (i guess not so shockingly?) that `marketstore` doesn't
always teardown "gracefully" under SIGINT (seems to hang if there are
open client connections which are also in the midst of teardown?) so
this instead first tries the SIGINT and then fails over to a SIGKILL
(destroy loop) which seems to be much more reliable to ensure shutdown
without any downside - in terms of a "hard kill".
Originally i was thinking the issue was root perms related (which get
relegated solely to the `marketstored` daemon actor after spawn) but
actually it was indeed the signalling / application layer causing the
hold-up/latency on teardown. There's a bunch of lingering (now
commented) code which tried to solve this non-problem as well as a bunch
logging/prints to help decipher the root of the issue - this will all
get cleaned out shortly.
If `marketstore` is detected try to only load most recent missing data
from the data provider (broker) and the rest from the tsdb and push it
all to shm for display in the UI. If the provider/broker doesn't have
the history client endpoint, just use the old one for now so we can
start to incrementally add support. Don't start the ohlc step
incrementer task until the backend signals that the feed is live.
Add some basic `numpy` epoch slice logic to generate append and prepend
arrays to write to the db.
Mooar cool things,
- add a `Storage.delete_ts()` method to wipe a column series from the db
easily.
- don't attempt to read in any OHLC series by default on client load
- add some `pyqtgraph` profiling and drop manual latency measures
- if no db series for the fqsn exists write the entire shm array
Also, Start tinkering with `tractor.trionics.ipython_embed()`
In effort to get back to a usable REPL around the mkts client
this adds usage of the new `tractor` integration api as well as logic
for skipping backfilling if existing tsdb arrays are found.
Starts a wrapper around the `marketstore` client to do basic ohlcv query
and retrieval and prototypes out write methods for ohlc and tick.
Try to connect to `marketstore` automatically (which will fail if not
started currently) but we will eventually first do a service query.
Further:
- get `pikerd` working with and without `--tsdb` flag.
- support spawning `brokerd` with no real-time quotes.
- bring back in "fqsn" support that was originally not
in this history before commits factoring.
Not sure how I missed mapping the 5995 grpc port 🤦; done now.
Also adds graceful teardown using SIGINT with included container
logging relayed to the piker console B).
Found this caused breakage on `kraken` orders which triggered the
"insufficient funds" error response. Makes sense since they won't
generate an order id if the order can't ever be submitted.
The most important changes include:
- iterating the new `Flow` type and updating graphics
- adding detailed profiling
- increasing the min uppx before graphics updates are throttled
- including the L1 spread in y-range calcs so that you never have the
bid/ask go "out of view"..
- pass around `Flow`s instead of shms
- drop all the old prototyped downsampling code
If manually managing an overlay you'll likely call `.overlay_plotitem()`
and then a plotting method so we need to accept a plot item input so
that the chart's pi doesn't get assigned incorrectly in the `Flow` entry
(though it is by default if no input is provided).
More,
- add a `Flow.graphics` field and set it to the `pg.GraphicsObject`.
- make `Flow.maxmin()` return `None` in the "can't calculate" cases.
- set shm refs on `Flow` entries.
- don't run a graphics cycle on 'update' msgs from the engine
if the containing chart is hidden.
- drop `volume` from flows map and disable auto-yranging
once $vlm comes up.
Allows for removing resize callbacks for a flow/overlay that you wish to
remove from view (eg. unit volume after dollar volume is up) and thus
less general interaction callback overhead for any plot you don't wish
to show or resize.
Further,
- drop the `autoscale_linked_plots` block for now since with
multi-view-box overlays each register their own vb resize slots
- pull the graphics object from the chart's `Flow` map inside
`.maybe_downsample_graphics()`
This new type wraps a shm data flow and will eventually include things
like incremental path-graphics updates and serialization + bg downsampling
techniques. The main immediate motivation was to get a cached y-range max/min
calc going since profiling revealed the `numpy` equivalents were
actually quite slow as the data set grows large. Likely we can use all
this to drive a streaming mx/mn routine that's always launched as part
of each on-host flow.
This is our official foray into use of `msgspec.Struct` B) and I have to
say, pretty impressed; we'll likely completely ditch `pydantic` from
here on out.
We don't need update graphics on every x-range change since that's what
the display loop does. Instead, only on manual changes do we make manual
calls into `.update_graphics_from_array()` and be sure to iterate all
linked subplots and all their embedded graphics.
The pg profiler seems to have trouble with early `return`s in function
calls (likely muckery with the GC/`.__delete__()`) so let's just try
to avoid it for now until we either fix it (probably by implementing as
a ctx mngr) or use diff one.
Ugh, turns out the wacky `ChartView.maxmin` callback stuff we did (for
determining y-range sizings) currently requires that the volume array
has a "bars in view" result.. so let's make that keep working without
rendering the graphics for the curve (since we're disabling them once
$vlm comes up).
As with the `BarItems` graphics, this makes it possible to pass in a "in
view" range of array data that can be *only* rendered improving
performance for large(r) data sets. All the other normal behaviour is
kept (i.e a persistent, (pre/ap)pendable path can still be maintained)
if a ``view_range`` is not provided.
Further updates,
- drop the `.should_ds_or_redraw()` and `.maybe_downsample()` predicates
instead moving all that logic inside `.update_from_array()`.
- disable the "cache flipping", which doesn't seem to be needed to avoid
artifacts any more?
- handle all redraw/dowsampling logic in `.update_from_array()`.
- even more profiling.
- drop path `.reserve()` stuff until we better figure out how it's
supposed to work.
Tyler Goodlet
Guillermo Rodriguez
goodboy
Esmeralda Gallardo
algorandpa
algorandpa
Guillermo Rodriguez
dinkus