In order to support instruments with lifetimes (aka derivatives) we need
generally need special symbol annotations which detail such meta data
(such as `MNQ.GLOBEX.20220717` for daq futes). Further there is really
no reason for the public api for this feed layer to care about getting
a special "brokername" field since generally the data is coming directly
from UIs (eg. search selection) so we might as well accept a fqsn (fully
qualified symbol name) which includes the broker name; for now a suffix
like `'.ib'`. We may change this schema (soon) but this at least gets us
to a point where we expect the full name including broker/provider.
An additional detail: for certain "generic" symbol names (like for
futes) we will pull a so called "front contract" and map this to
a specific fqsn underneath, so there is a double (cached) entry for that
entry such that other consumers can use it the same way if desired.
Some other machinery changes:
- expect the `stream_quotes()` endpoint to deliver it's `.started()` msg
almost immediately since we now need it deliver any fqsn asap (yes
this means the ep should no longer wait on a "live" first quote and
instead deliver what quote data it can right away.
- expect the quotes ohlc sampler task to add in the broker name before
broadcast to remote (actor) consumers since the backend isn't (yet)
expected to do that add in itself.
- obviously we start using all the new fqsn related `Symbol` apis
Move the core ws message handling into `stream_messages()` and call that
from 2 new stream processors: `process_data_feed_msgs()` and
`process_order_msgs()`. Add comments for hints on how to implement the
order msg parsing as well as `pprint` received msgs to console for now.
Since moving to a "god loop" for graphics, we don't really need to have
a dedicated task for updating graphics on new sample increments. The
only UX difference will be that curves won't be updated until an actual new
rt-quote-event triggers the graphics loop -> so we'll have the chart
"jump" to a new position and new curve segments generated only when new
data arrives. This is imo fine since it's just less "idle" updates
where the chart would sit printing the same (last) value every step.
Instead only update the view increment if a new index is detected by
reading shm.
If we ever want this dedicated task update again this commit can be
easily reverted B)
Break up real-time quote feed and history loading into 2 separate tasks
and deliver a client side `data.Feed` as soon as history is loaded
(instead of waiting for a rt quote - the previous logic). If
a symbol doesn't have history then likely the feed shouldn't be loaded
(since presumably client code will need at least "some" datums history
to do anything) and waiting on a real-time quote is dumb, since it'll
hang if the market isn't open XD. If a symbol doesn't have history we
can always write a zero/null array when we run into that case. This also
greatly speeds up feed loading when both history and quotes are available.
TL;DR summary:
- add a `_Feedsbus.start_task()` one-cancel-scope-per-task method for
assisting with (re-)starting and stopping long running persistent
feeds (basically a "one cancels one" style nursery API).
- add a `manage_history()` task which does all history loading (and
eventually real-time writing) which has an independent signal and
start it in a separate task.
- drop the "sample rate per symbol" stuff since client code doesn't really
care when it can just inspect shm indexing/time-steps itself.
- run throttle tasks in the bus nursery thus avoiding cancelling the
underlying sampler task on feed client disconnects.
- don't store a repeated ref the bus nursery's cancel scope..
To avoid the "trigger finger" issue (darks execing before they should
due to a stale last price state, normally when generating a trigger
predicate..) always iterate the loop and update the last known book
price even when no execs/triggered orders are registered.
You can get a weird "last line segment" artifact if *only* that segment
is drawn and the cache is enabled, so just disable unless in step mode
at startup and re-flash as normal when new path data is appended. Add
a `.disable_cache()` method for the multi-use in the update method. Use
line style on the `._last_line: QLineF` segment as well.
Enables retrieving all "named axes" on a particular "side" of the
overlayed plot items. This is useful for calculating how much space
needs to be allocated for the axes before the view box area starts.
Though it's not per-tick accurate, accumulate the number of "trades"
(i.e. the "clearing rate" - maybe this is a better name?) per bar
inside the `dolla_vlm` fsp and average and report wmas of this in the
`flow_rates` fsp.
Define the flows table as a class var (thus making it a "global" and/or
actor-local state) which can be accessed by any in process task. Add
`Fsp.get_shm()` to allow accessing output streams by source-token + fsp
routine reference and thus providing inter-fsp low level access to
real-time flows.
In order for fsp routines to be able to look up other "flows" in the
cascade, we need a small registry-table which gives access to a map of
a source stream + an fsp -> an output stream. Eventually we'll also
likely want a dependency (injection) mechanism so that any fsp demanded
can either be dynamically allocated or at the least waited upon before
a consumer tries to access it.
Instead of referencing the remote processing funcs by a `str` name start
embracing the new `@fsp`/`Fsp` API such that wrapped processing
functions are first class APIs.
Summary of the changeset:
- move and load the fsp built-in set in the new `.fsp._api` module
- handle processors ("fsps") which want to yield multiple keyed-values
(interleaved in time) by expecting both history that is keyed and
assigned to the appropriate struct-array field, *and* real-time
`yield`ed value in tuples of the form `tuple[str, float]` such that
any one (async) processing function can deliver multiple outputs from
the same base calculation.
- drop `maybe_mk_fsp_shm()` from UI module
- expect and manage `Fsp` instances (`@fsp` decorated funcs) throughout
the UI code, particularly the `FspAdmin` layer.
Since more curves costs more processing and since the vlm and $vlm
curves are normally very close to the same (graphically) we hide the
unit volume curve once the dollar volume is up (after the fsp daemon-task is
spawned) and just expect the user to understand the diff in axes units.
Also, use the new `title=` api to `.overlay_plotitem()`.
Use our internal `Label` with much better dpi based sizing of text and
placement below the y-axis ticks area for more minimalism and less
clutter.
Play around with `lru_cache` on axis label bounding rects and for now
just hack sizing by subtracting half the text height (not sure why) from
the width to avoid over-extension / overlap with any adjacent axis.
Allow passing in a formatter function for processing tick values on an
axis. This makes it easy to for example, `piker.calc.humanize()` dollar
volume on a subchart.
Factor `set_min_tick()` into the `PriceAxis` since it's not used on any
x-axis data thus far.
Adds `FspAdmin.open_fsp_chart()` which allows adding a real time graphics
display of an fsp's output with different options for where (which chart
or make a new one) to place it.
Further,
- change some method naming, namely the other fsp engine task methods to
`.open_chain()` and `.start_engine_task()`.
- make `run_fsp_ui()` a lone task function for now with the default
config parsing and chart setup logic (and it still includes a buncha
commented out stuff for doing graphics update which is now done in the
main loop to avoid task switching overhead).
- move all vlm related fsp config entries into the `open_vlm_displays()`
task for dedicated setup with the fsp admin api such as special
auto-yrange handling and graph overlays.
- `start_fsp_displays()` is now just a small loop through config entries
with synced startup status messages.
For wtv cucked reason all the viewbox/scene coordinate calcs do **not**
include a left axis in the geo (likely because it's a hacked in widget
+ layout thing managed by `PlotItem`). Detect if there's a left axis and
if so use it in the label placement scene coords calc. ToDo: probably
make this a non-move calc and only recompute any time the axis changes.
Other:
- rate limit mouse events down to the 60 (ish) Hz for now
- change one last lingering `'ohlc'` array lookup
- fix `.mouseMoved()` "event" type annot
This is a huge commit which moves a bunch of code around in order to
simplify some of our UI modules as well as support our first official
mult-axis chart: overlaid volume and "dollar volume". A good deal of
this change set is to make startup fast such that volume data which is
often shipped alongside OHLC history is loaded and shown asap and FSPs
are loaded in an actor cluster with their graphics overlayed
concurrently as each responsible worker generates plottable output.
For everything to work this commit requires use of a draft `pyqtgraph`
PR: https://github.com/pyqtgraph/pyqtgraph/pull/2162
Change summary:
- move remaining FSP actor cluster helpers into `.ui._fsp` mod as well
as fsp specific UI managers (`maybe_open_vlm_display()`,
`start_fsp_displays()`).
- add an `FspAdmin` API for starting fsp chains on the cluster
concurrently allowing for future work toward reload/unloading.
- bring FSP config dict into `start_fsp_displays()` and `.started()`-deliver
both the fsp admin and any volume chart back up to the calling display
loop code.
ToDo:
- repair `ChartView` click-drag interactions
- auto-range on $ vlm needs to use `ChartPlotWidget._set_yrange()`
- a lot better styling for the $_vlm overlay XD
As part of factoring `._set_yrange()` into the lower level view box,
move the y-range calculations into a new method. These calcs should
eventually be completely separate (as they are for the real-time version
in the graphics display update loop) and likely part of some kind of
graphics-related lower level management API. Draft such an API as an
`ArrayScene` (commented for now) as a sketch toward factoring array
tracking **out of** the chart widget. Drop the `'ohlc'` array name and
instead always use whatever `.name` was assigned to the chart widget
to lookup its "main" / source data array for now.
Enable auto-yranging on overlayed plotitems by enabling on its viewbox
and, for now, assign an ad-hoc `._maxmin()` since the widget version
from this commit has no easy way to know which internal array to use. If
an FSP (`dolla_vlm` in this case) is overlayed on an existing chart
without also having a full widget (which it doesn't in this case since
we're using an overlayed `PlotItem` instead of a full `ChartPlotWidget`)
we need some way to define the `.maxmin()` for the overlayed
data/graphics. This likely means the `.maxmin()` will eventually get
factored into wtv lowlevel `ArrayScene` API mentioned above.
Calculations for auto-yaxis ranging are both signalled and drawn by our
`ViewBox` so we might as well factor this handler down from the chart
widget into the view type. This makes it much easier (and clearer) that
`PlotItem` and other lower level overlayed `GraphicsObject`s can utilize
*size-to-data* style view modes easily without widget-level coupling.
Further changes,
- support a `._maxmin()` internal callable (temporarily) for allowing
a viewed graphics object to define it's own y-range max/min calc.
- add `._static_range` var (though usage hasn't been moved from the
chart plot widget yet
- drop y-axis click-drag zoom instead reverting back to default viewbox
behaviour with wheel-zoom and click-drag-pan on the axis.
This brings in the WIP components developed as part of
https://github.com/pyqtgraph/pyqtgraph/pull/2162.
Most of the history can be understood from that issue and effort but the
TL;DR is,
- add an event handler wrapper system which can be used to
wrap `ViewBox` methods such that multiple views can be overlayed and
a single event stream broadcast from one "main" view to others which
are overlaid with it.
- add in 2 relay `Signal` attrs to our `ViewBox` subtype (`Chartview`)
to accomplish per event `MouseEvent.emit()` style broadcasting to
multiple (sub-)views.
- Add a `PlotItemOverlay` api which does all the work of overlaying the
actual chart graphics and arranging multiple-axes without collision as
well as tying together all the event/signalling so that only a single
"focussed" view relays to all overlays.
Each `pyqtgraph.PlotItem` uses a `QGraphicsGridLayout` to place its view
box, axes and titles in the traditional graph format. With multiple
overlayed charts we need those axes to not collide with one another and
further allow for an "order" specified by the user. We accomplish this
by adding `QGraphicsLinearLayout`s for each axis "side": `{'left',
'right', 'top', 'bottom'}` such that plot axes can be inserted and moved
easily without having to constantly re-stack/order a grid layout (which
does not have a linked-list style API).
The new type is called `ComposedGridLayout` for now and offers a basic
list-like API with `.insert()`, `.append()`, and eventually a dict-style
`.pop()`. We probably want to also eventually offer a `.focus()` to
allow user switching of *which* main graphics object (aka chart) is "in
use".
This syncs with a dev branch in our `pyqtgraph` fork:
https://github.com/pyqtgraph/pyqtgraph/pull/2162
The main idea is to get mult-yaxis display fully functional with
multiple view boxes running in a "relay mode" where some focussed view
relays signals to overlaid views which may have independent axes. This
preps us for both displaying independent codomain-set FSP output as well
as so called "aggregate" feeds of multiple fins underlyings on the same
chart (eg. options and futures over top of ETFs and underlying stocks).
The eventual desired UX is to support fast switching of instruments for
order mode trading without requiring entirely separate charts as well as
simple real-time anal of associated instruments.
The first effort here is to display vlm and $_vlm alongside each other
as a built-in FSP subchart.
We can instead use the god widget's nursery to schedule all the feed
pause/resume requests and be even more concurrent during a view (of
symbols) switch.
Use `tractor.trionics.gather_contexts()` to start up the fsp and volume
chart-displays (for an additional conc speedup). Drop `dolla_vlm` again for
now until we figure out how we can display it *and* vlm on the same
sub-chart? It would be nice to avoid having to spawn an fsp process
before showing the volume curve.
Call the resize method only after all FSP subcharts have rendered
such that the main OHLC chart's final width is read.
Further tweaks:
- drop rsi by default
- drop the stream drain stuff
- fix failed-to-read shm logging
This fixes a weird re-render bug/slowdown/artifact that was introduced
with the order mode sidepane work. Prior to the sidepane addition, chart
switching was immediate with zero noticeable widget rendering steps.
The slow down was caused by 2 things:
- not yielding back to the Qt loop asap after re-showing/focussing
a linked split chart that was already in memory.
- pausing/resuming feeds only after a Qt loop render cycle has
completed.
This now restores the near zero latency UX.
There was a lingering issue where the fsp daemon would sync its shm
array with the source data and we'd set the start/end indices to the
same value. Under some races a reader would then read an empty `.array`
which it wasn't expecting. This fixes that as well as tidies up the
`ShmArray.push()` logic and adds a temporary check in `.array` for zero
length if the array hasn't been written yet.
We can now start removing read array length checks in consumer code
and hopefully no more races will show up.
Revert to old shm "last" meaning last row
It can now be declared inside an fsp config dict under the name
`dolla_vlm`. We still need to offer an engine control that zeros
the newest sample value instead of copying from the previous.
This also litters the engine code with `pyqtgraph` profiling to see if
we can improve startup times - likely it'll mean pre-allocating a small
fsp daemon cluster at startup.
Use a fixed worker count and don't respawn for every chart, instead
opting for a round-robin to tasks in a cluster and (for now) hoping for
the best in terms of trio scheduling, though we should obviously route
via symbol-locality next. This is currently a boon for chart spawning
startup times since actor creation is done AOT.
Additionally,
- use `zero_on_step` for dollar volume
- drop rsi on startup (again)
- add dollar volume (via fsp) along side unit volume
- litter more profiling to fsp chart startup sequence
- pre-define tick type classes for update loop
We are already packing framed ticks in extended lists from
the `.data._sampling.uniform_rate_send()` task so the natural solution
to avoid needless graphics cycles for HFT-ish feeds (like binance) is
to unpack those frames and for most cases only update graphics with the
"latest" data per loop iteration. Unpacking in this way also lessens
nested-iterations per tick type.
Btw, this also effectively solves all remaining issues of fast tick
feeds over-triggering the graphics loop renders as long as the original
quote stream is throttled appropriately, usually to the local display
rate.
Relates to #183, #192
Dirty deats:
- drop all per-tick rate checks, they were always somewhat pointless
when iterating a frame of ticks per render cycle XD.
- unpack tick frame into ticks per frame type, and last of each type;
the lasts are used to update each part of the UI/graphics by class.
- only skip the label update if we can't retrieve the last from from a
graphics source array; it seems `chart.update_curve_from_array()`
already does a `len` check internally.
- add some draft commented code for tick type classes and a possible
wire framed tick data structure.
- move `chart_maxmin()` range computer to module level, bind a chart to
it with a `partial.`
- only check rate limits in main quote loop thus reporting actual
overages
- add in commented logic for only updating the "last" cleared price from
the most recent framed value if we want to eventually (right now seems
like this is only relevant to ib and it's dark trades: `utrade`).
- rename `_clear_throttle_rate` -> `_quote_throttle_rate`, drop
`_book_throttle_rate`.
This is in prep toward doing fsp graphics updates from the main quotes
update loop (where OHLC and volume are done). Updating fsp output from
that task should, for the majority of cases, be fine presuming the
processing is derived from the quote stream as a source. Further,
calling an update function on each fsp subplot/overlay is of course
faster then a full task switch - which is how it currently works with
a separate stream for every fsp output. This also will let us delay
adding full `Feed` support around fsp streams for the moment while still
getting quote throttling dictated by the quote stream.
Going forward, We can still support a separate task/fsp stream for
updates as needed (ex. some kind of fast external data source that isn't
synced with price data) but it should be enabled as needed required by
the user.
The major change is moving the fsp "daemon" (more like wanna-be fspd)
endpoint to use the newer `tractor.Portal.open_context()` and
bi-directional streaming api.
There's a few other things in here too:
- make a helper for allocating single colume fsp shm arrays
- rename some some fsp related functions to be more explicit on their
purposes
Since our startup is very concurrent there is often races where widgets
have not fully spawned before python (re-)sizing code has a chance to
run sizing logic and thus incorrect dimensions are read. Instead ensure
the Qt render loop gets to run in between such checks.
Also add a `open_sidepane()` mngr for creating a minimal form widget for
FSP subchart sidepanes which can be configured from an input `dict`.
This should in theory result in increased burstiness since we remove
the plain `trio.sleep()` and instead always wait on the receive channel
as much as possible until the `trio.move_on_after()` (+ time diffing
calcs) times out and signals the next throttled send cycle. This also is
slightly easier to grok code-wise instead of the `try, except` and
another tight while loop until a `trio.WouldBlock`. The only simpler
way i can think to do it is with 2 tasks: 1 to collect ticks and the
other to read and send at the throttle rate.
Comment out the log msg for now to avoid latency and add much more
detailed comments. Add an overrun log msg to the main sample loop.
A `QRectF` is easier to make and draw (i think?) so use that and fill it
on volume events for decent sleek real-time look. Adjust the step array
generator to allow for an endpoints flag. Comment and/or clean out all
the old path filling calls that gave us perf issues..
Turns out the performance of updating and refilling step curves > 1k ish
points is super slow :sadkek:. Disabling the fill basically returns
normal performance, so it seems maybe we'll stick with unfilled volume
"bars" for now. The other tricky bit is getting the path to extend and
fill which is particularly slow if you use the `QPainterPath.united()`
(what `+` set op does) operation which seems to require an entire redraw
of the curve each paint iteration. Removing the pixel buffer cache makes
things that much worse too..
One technique i tried was only setting a `._fill` flag when so many
datums are in view (< 1k as determined by the chart widget), and this
helps, but under high load (trade rates) you still see more lag then
without the fill which makes me say screw it and let's stick with
unfilled bars for now. Trying go to get performant filled curves will be
an exercise for an aspiring graphics eng :P
In latest `pyqtgraph` it seems there's a discrepancy
since `function.arrayToQPath()` was reworked and now
we need to *not* connect the last point for each bar.
The prior PR for fixing fsp array misalignment also added
`tractor.Context` usage which wasn't reflected in the graphics update
loop (newer code added it but the prior PR was factored from path
dependent history) and thus was broken. Further in newer work we don't
have fsp actors actually stream value updates since the display loop can
already pull from the source feed and update graphics at a preferred
throttle rate. Re-enabled the fsp stream sending here by default until
that newer only-throttle-pull-from-source code is landed in the display
loop.
This should finally be correct fsp src-to-dst array syncing now..
There's a few edge cases but mostly we need to be sure we sync both
back-filled history diffs and avoid current step lag/leads. Use
a polling routine and the more stringent task re-spawn system to get
this right.
There was a lingering issue where the fsp daemon would sync its shm
array with the source data and we'd set the start/end indices to the
same value. Under some races a reader would then read an empty `.array`
which it wasn't expecting. This fixes that as well as tidies up the
`ShmArray.push()` logic and adds a temporary check in `.array` for zero
length if the array hasn't been written yet.
We can now start removing read array length checks in consumer code
and hopefully no more races will show up.
Litter the engine code with `pyqtgraph` profiling to see if we can
improve startup times - likely it'll mean pre-allocating a small fsp
daemon cluster at startup.
Split up the rather large `.ui._chart` module into its constituents:
- a `.ui._app` for the highlevel widget composition, qtractor entry
point and startup logic
- `.ui._display` for all the real-time graphics update tasks which
consume the `.ui._chart` widget apis
Must have run into some confusion with data structures in `brokerd` vs.
`emsd`. This fixes the ems `relay.positions` state tracking to be
composed maps, vs. messages from `brokerd` should just be a sequence.
This reverts commit 6fa8958acf.
We actually do need it since the selection widget of course won't tell
you its "key" that we assign and further we'd have to use a (value, key)
style invocation which isn't super pythonic.
The paper engine returns `"paper"` instead of `None` in the pp msgs so
expect that. Don't bother with fills tracking for now (since we'll need
either the account in the msg or a lookup table locally for oids to
accounts). Change the order line update handler to a local module function,
there was no reason for it to be a pane method.
Make a pp tracker per account and load on order mode boot.
Only show details on the pp tracker for the selected account.
Make the settings pane assign a `.current_pp` state on the order mode
instance (for the charted symbol) on account selection switches and no
longer keep a ref to a single pp tracker and allocator in the pane.
`SettingsPane.update_status_ui()` now expects an explicit tracker
reference as input. Still need to figure out the pnl update task logic
despite the intermittent account changes.
This adds full support for a single `brokerd` managing multiple API
endpoint clients in tandem. Get the client scan loop correct and load
accounts from all discovered clients as specified in a user's
`broker.toml`. We now just always re-scan for all clients and if there's
a cache hit just skip a creation/connection logic.
Route orders with an account name to the correct client in the
`handle_order_requests()` endpoint and spawn an event relay task per
client for transmitting trade events back to `emsd`.
Make the `handle_order_requests()` tasks now lookup the appropriate API
client for a given account (or error if it can't be found) and use it
for submission. Account names are loaded from the
`brokers.toml::accounts.ib` section both UI side and in the `brokerd`.
Change `_aio_get_client()` to a `load_aio_client()` which now tries to
scan and load api clients for all connections defined in the config as
well as deliver the client cache and account lookup tables.
Each backend broker may support multiple (types) of accounts; this patch
lets clients send order requests that pass through an `account` field in
certain `emsd` <-> `brokerd` transactions. This allows each provider to read
in and conduct logic based on what account value is passed via requests
to the `trades_dialogue()` endpoint as well as tie together positioning
updates with relevant account keys for display in UIs.
This also adds relay support for a `Status` msg with a `'broker_errored'`
status which for now will trigger the same logic as cancelled orders on
the client side and thus will remove order lines submitted on a chart.
Get rid of `PositionTracker.init_status_ui()` and instead make
a helper func `mk_allocator()` which takes in the alloc and adjusts
default settings on the allocator alone (which is expected to be
passed in). Expect a `Position` instance to be passed into the tracker
which will be looked up for UI updates. Move *update-from-position-msg*
ops into a `Position.update_from_msg()` method.
We weren't updating the LHS size labels on creation and we now use the
lot size digits to do so. Change `PositionTracker.update()` to
`.update_from_pp_msg()`.
Acts as a fix for lodpi and better sizing logic for the pp status bar.
Drop all the redundant passing of the form to its child layouts during
instantiating (since they're all added as layouts to the tree). Comment
out the feed status label for now since it's not hooked up to the
backend and we'll get it going in a new PR.
Down the road we probably want to do all the pp pane component-widget
sizing *after* the `pyqtgraph` chart is up; it's going to take some
reworking of the charting api tho.
We were re-implementing a few things order lines already support.
All we really needed was to not add a pp size label if one is provided.
Use `.hide_label()` in the mouse hover handler.
When exiting a pp toward net-zero, we may sometimes run into the issue
of having a "fractional slot" worth of units in allocator limit terms.
This is further nuanced by live orders which are submitted above the
current clearing price which get allocated a size (based on that staged
but non-cleared price) according to their limit size unit which can be
calculated to be less then the size that would have been allocated at
the actual clearing price. In the short term cope with this discrepancy
by simply using a "slot and a half" as the decision point of whether to
exit a slot's worth or the remaining pp's worth of units. In other words
if you can exit 1.5x a slot's worth or less, exit the remaining pp,
otherwise exit a slot's worth. This is a stop gap until we have a better
solution to limiting staged orders to (some range around) the currently
computed clear-able price.
We need a subtask to compute the current pp PnL in real-time but really
only if a pp exists - a spawnable subtask would be ideal for this. Stage
a tick streaming task using a stream bcaster; no actual pnl calc yet.
Since we're going to need subtasks anyway might as well stick the order
mode UI processing loop in a task as well and then just give the whole
thing a ctx mngr api. This'll probably be handy for when we have
auto-strats that need to dynamically use the mode's api as well.
Oh, and move the time -> index mapper to a chart method for now.
Use this method to go through writing all allocator parameters and then
reading all changes back into the order mode pane including updating the
limit and step labels by the fill bar.
Machinery changes:
- add `.limit()` and `.step_sizes()` methods to the allocator to
provide the appropriate data depending on the pp limit size unit (eg.
currency vs. units)
- humanize the label display text such that you have nice suffixes and
a fixed precision
- tweak the fill bar labels to be simpler since the values are now
humanized
- expect `.on_ui_settings_change()` to be called for every slots hotkey
tweak
Turned out to be pretty simple, on every pp update just recompute
the proportion of slots used based on the limit size units.
Don't assign the allocator callback method for alert lines since
there's no size to generate. Move from-existing-pp calculations
into the order pane itself.
Handling the edge cases in this was "fun", namely:
- entering with less then a slot's worth of units to purchase
before hitting the pp limit or, less then a slots worth when exiting
toward a net-zero position.
- round pp msg updates using the symbol tick and lot size digits to
avoid super small (1e-30 lel) positions lingering in the ems (happens
moreso with the paper engine).
- don't expect the next size method to be called for alert level changes
- pass label text and field widget key separately
- fix fill status bar slot sizing logic (once and for all) and
create a new type that allows generating / resizing the bar's
size / values with a `.set_slots()` method
- pull account names from allocator attr
- set `.fill_bar` as the fill status bar on the form for now
- make `GodWidget.load_symbol()` async
- track loaded feeds with a private `._feeds` dict
- add methods to pause/resume all feeds when chart is (un)focussed
- add some commented test code for 2nd feed consumer task and rsi2 fsp
- load async signal handler for view clicking
- generate lines from staged `Order` msgs
- apply level update callback to each order that dynamically
updates the order size from the allocator calcs
- pass order msg instances to the ems client for submission
- update order size on line moves
- add `Order` msg and `Symbol` refs to each dialog
In an effort to simplify line creation and management from an order
mode here's a slew of changes:
- use our new ``LevelMarker`` for order lines and fully drop usage
of the original marker implementation stuff from `pg.InfiniteLine`
- add a left side label which shows the instrument's "units" value
- the most fundamental unit for the "size" of the order
- allow passing in an optional `marker_size: str` so that `action: str`
doesn't necessarily have to be passed (eg. when copying from an
existing line)
- change a couple of internal line config options to be public attrs
which can now be configured dynamically in real-time (since they're
all `bool` anyway):
* `hl_on_hover` -> `highlight_on_hover`
* `_always_show_labels` -> `always_show_labels`
- `LevelLine.set_level()` now only sets the position if it was **not**
called from the position changed signal (which would be redundant)
Move all the ``pydantic`` finagling to an `_orm.py` and
just keep an `Allocator` as the backing model for our pp controls
in the position module. This all needs to be tied together in some sane
with with facility for multiple symbols/streams per chart for when we
get to charting-trading aggregate feeds.
It was becoming too much with all the labels and markers and lines..
Might as well package it all together instead of cramming it in the
order mode loop, chief.
The techincal summary,
- move `_lines.position_line()` -> `PositionInfo.position_line()`.
- slap a `.pp` on the order mode instance which *is* a `PositionInfo`
- drop the position info info label for now (let's see what users want
eventually but for now let's keep it super minimal).
- add a `LevelMarker` type to replace the old `LevelLine` internal
marker system (includes ability to change the style and level on the
fly).
- change `_annotate.mk_marker()` -> `mk_maker_path()` and expect caller
to wrap in a `QGraphicsPathItem` if needed.
Generate and maintain position messages in the paper engine for each
`pikerd` session. We no longer tear down the engine on each client
disconnect. Ensure -ve size on sells to make the math work.
This gives us fast search over a known set of symbols you can't search
for with the api such as futures and commodities contracts.
Toss in a new client method to lookup contract details
`Client.con_deats()` and avoid calling it for now from `.search_stock()`
for speed; it seems originally we were doing the 2nd lookup due to weird
suffixes in the `.primaryExchange` which we can just discard.
In order to ensure the lifetime of the feed can in fact be kept open
until the last consumer task has completed we need to maintain
a lifetime which is hierarchically greater then all consumer tasks.
This solution is somewhat hacky but seems to work well: we just use the
`tractor` actor's "service nursery" (the one normally used to invoke rpc
tasks) to launch the task which will start and keep open the target
cached async context manager. To make this more "proper" we may want to
offer a "root nursery" in all piker actors that is exposed through some
singleton api or even introduce a public api for it into `tractor`
directly.
Think this was fixed by passing through `**kwargs` in
`maybe_open_feed()`, the shielding for fsp respawns wasn't being
properly passed through..
This reverts commit 2f1455d423.
Maybe i've finally learned my lesson that exit stacks and per task ctx
manager caching is just not trionic.. Use the approach we've taken for
the daemon service manager as well: create a process global nursery for
each unique ctx manager we wish to cache and simply tear it down when
the number of consumers goes to zero.
This seems to resolve all prior issues and gets us error-free cached
feeds!
Try out he new broadcast channels from `tractor` for data feeds
we already have cached. Any time there's a cache hit we load the
cached feed and just slap a broadcast receiver on it for the local
consumer task.
Add a new type/api to manage "contents labels" (labels that sit in
a view and display info about viewed data) since it's mostly used by
the linked charts cursor. Make `LinkedSplits.cursor` the new and only
instance var for the cursor such that charts can look it up from that
common class. Drop the `ChartPlotWidget._ohlc` array, just add
a `'ohlc'` entry to `._arrays`.
Orders in order mode should be chart oriented since there's a mode per
chart. If you want all orders just ask the ems or query all the charts
in a loop.
This fixes cancel-all-orders such that when 'cc' is tapped only the
orders on the *current* chart are cancelled, lel.
Generalize the methods for cancelling groups of orders (all or those
under cursor) and add new group status support such that statuses for
each cancel or order submission is displayed in the status bar. In the
"cancel-all-orders" case, use the new group status stuff.
Allows for submitting a top level "group status" associated with
a "group key" which eventually resolves once all sub-statuses associated
with that group key (and thus top level status) complete and are also
removed. Also add support for a "final message" for each status such
that once the status clear callback is called a final msg is placed on
the status bar that is then removed when the next status is set.
It's all a questionable bunch of closures/callbacks but it worx.
Instead of callbacks for key presses/releases convert our `ChartView`'s
kb input handling to async code using our event relaying-over-mem-chan
system. This is a first step toward a more async driven modal control
UX. Changed a bunch of "chart" component naming as part of this as well,
namely: `ChartSpace` -> `GodWidget` and `LinkedSplitCharts` ->
`LinkedSplits`. Engage the view boxe's async handler code as part of new
symbol data loading in `display_symbol_data()`. More re-orging to come!
Add an `open_handler()` ctx manager for wholesale handling event sets
with a passed in async func. Better document and implement the event
filtering core including adding support for key "auto repeat" filtering;
it turns out the events delivered when `trio` does its guest-most tick
are not the same (Qt has somehow consumed them or something) so we have
to do certain things (like getting the `.type()`, `.isAutoRepeat()`,
etc.) before shipping over the mem chan. The alt might be to copy the
event objects first but haven't tried it yet. For now just offer
auto-repeat filtering through a flag.
If a client attaches to a quotes data feed and requests a throttle rate,
be sure to unsub that side-band memchan + task when it detaches and
especially so on any transport connection error.
Also, use an explicit `tractor.Context.cancel()` on the client feed
block exit since we removed the implicit cancel option from the
`tractor` api.
There is no reason to have more then `brokerd` trades dialogue stream
open per `emsd`. Here we minimize to managing that lone stream and
multiplexing msgs from each client such that multiple clients can be
connected to the ems, conducting trading without requiring multiple
ems-client connections to the backend broker and without the broker
being aware there are even multiple flows going on.
This patch also sets up for being able to have ems clients which
register to receive and track trade flows from other piker clients thus
enabling so called "multi-player" trading where orders for both paper
and live trades can be shared between multiple participants in the form
of a pre-broker, local clearing service and trade signals dark book.
This solves a bunch of issues to do with `brokerd` order status msgs
getting relayed for each order to **every** correspondingly connected
EMS client. Previously we weren't keeping track of which emsd orders
were associated with which clients so you had backend msgs getting
broadcast to all clients which not only resulted in duplicate (and
sometimes erroneous, due to state tracking) actions taking place in the
UI's order mode, but it's also just duplicate traffic (usually to the
same actor) over multiple logical streams. Instead, only keep up **one**
(cached) stream with the `trades_dialogue()` endpoint such that **all**
emsd orders route over that single connection to the particular
`brokerd` actor.
An async exit stack around the new `@tractor.context` is problematic
since a pushed context can't bubble errors unless the exit stack has
been closed. But in that case why do you need the exit stack if you're
going to push it and wait it right away; it seems more correct to use
a nursery and spawn a task in `pikerd` that waits on the both the
target context completion first (thus being able to bubble up any errors
from the remote, and top level service task) and the sub-actor portal.
(Sub)service Daemons are spawned with `.start_actor()` and thus will
block forever until cancelled so, add a way to cancel them explicitly
which we'll need eventually for restarts and dynamic feed management.
The big lesson here is that async exit stacks are not conducive to
spawning and monitoring service tasks, and especially so if
a `@tractor.context` is used since if the `.open_context()` call isn't
exited (only possible by the stack being closed), then there will be no
way for `trio` to cancel the task that pushed that context (since it
can't run a checkpoint while yielded inside the stack) without also
cancelling all other contexts pushed on that stack. Presuming one
`pikerd` task is used to do the original pushing (which it was) then
any error would have to kill all service daemon tasks which obviously
won't work.
I see this mostly as the painz of tinkering out an SC service manager
with `tractor` / `trio` for the first time, so try to go easy on the
process ;P
Adding binance's "hft" ws feeds has resulted in a lot of context
switching in our Qt charts, so much so it's chewin CPU and definitely
worth it to throttle to the detected display rate as per discussion in
issue #192.
This is a first very very naive attempt at throttling L1 tick feeds on
the `brokerd` end (producer side) using a constant and uniform delivery
rate by way of a `trio` task + mem chan. The new func is
`data._sampling.uniform_rate_send()`. Basically if a client request
a feed and provides a throttle rate we just spawn a task and queue up
ticks until approximately the next display rate's worth period of time
has passed before forwarding. It's definitely nothing fancy but does
provide fodder and a start point for an up and coming queueing eng to
start digging into both #107 and #109 ;)
Avoids some cyclical and confusing import time stuff that we needed to get
DPI aware fonts configured from the active display. Move the main window
singleton into its own module and add a `main_window()` getter for it.
Make `current_screen()` a ``MainWindow` method to avoid so many module
variables.
This moves the entire clearing system to use typed messages using
`pydantic.BaseModel` such that the streamed request-response order
submission protocols can be explicitly viewed in terms of message
schema, flow, and sequencing. Using the explicit message formats we can
now dig into simplifying and normalizing across broker provider apis to
get the best uniformity and simplicity.
The order submission sequence is now fully async: an order request is
expected to be explicitly acked with a new message and if cancellation
is requested by the client before the ack arrives, the cancel message is
stashed and then later sent immediately on receipt of the order
submission's ack from the backend broker. Backend brokers are now
controlled using a 2-way request-response streaming dialogue which is
fully api agnostic of the clearing system's core processing; This
leverages the new bi-directional streaming apis from `tractor`. The
clearing core (emsd) was also simplified by moving the paper engine to
it's own sub-actor and making it api-symmetric with expected `brokerd`
endpoints.
A couple of the ems status messages were changed/added:
'dark_executed' -> 'dark_triggered'
added 'alert_triggered'
More cleaning of old code to come!
This makes the paper engine look IPC-wise exactly like any
broker-provider backend module and uses the new ``trades_dialogue()``
2-way streaming endpoint for commanding order requests.
This serves as a first step toward truly distributed forward testing
since the paper engine can now be run out-of tree from `pikerd` if
needed thus demonstrating how real-time clearing signals can be shared
between fully distinct services.
This avoids somewhat convoluted "hackery" making 2 one-way streams
between the order client and the EMS and instead uses the new
bi-directional streaming and context API from `tractor`. Add a router
type to the EMS that gets setup by the initial service tree and which
we'll eventually use to work toward multi-provider executions and
order-trigger monitoring. Move to py3.9 style where possible throughout.
Makes it so we can move toward separate provider results fills in an
async way, on demand.
Also,
- add depth 1 iteration helper method
- add section finder helper method
- fix last selection loading to be mostly consistent
This allows for more deterministically managing long running sub-daemon
services under `pikerd` using the new context api from `tractor`.
The contexts are allocated in an async exit stack and torn down at root
daemon termination. Spawn brokerds using this method by changing the
persistence entry point to be a `@tractor.context`.
Some providers do well with a "longer" debounce period (like ib) since
searching them too frequently causes latency and stalls. By supporting
both a min and max debounce period on keyboard input we can only send
patterns to the slower engines when that period is triggered via
`trio.move_on_after()` and continue to relay to faster engines when the
measured period permits. Allow search routines to register their "min
period" such that they can choose to ignore patterns that arrive before
their heuristically known ideal wait.
Obviously this only supports stocks to start, it looks like we might
actually have to hard code some of the futures/forex/cmdtys that don't
have a search.. so lame. Special throttling is added here since the api
will grog out at anything more then 1Hz.
Additionally, decouple the bar loading request error handling from the
shm pushing loop so that we can always recover from a historical bars
throttle-error even if it's on the first try for a new symbol.
This allows for more deterministically managing long running sub-daemon
services under `pikerd` using the new context api from `tractor`.
The contexts are allocated in an async exit stack and torn down at root
daemon termination. Spawn brokerds using this method by changing the
persistence entry point to be a `@tractor.context`.
This gets the binance provider meeting the data feed schema requirements
of both the OHLC sampling/charting machinery as well as proper
formatting of historical OHLC history.
Notably,
- spec a minimal ohlc dtype based on the kline endpoint
- use a dataclass to parse out OHLC bar datums and pack into np.ndarray/shm
- add the ``aggTrade`` endpoint to get last clearing (traded) prices,
validate with ``pydantic`` and then normalize these into our tick-quote
format for delivery over the feed stream api.
- a notable requirement is that the "first" quote from the feed must
contain a 'last` field so the clearing system can start up correctly.
This required a fsp task spawning logic rework which ended up being
cleaner just spawning tasks in a loop sequentially instead of trying
a 2-phase spawn-then-initialize approach.
This also includes changes from the symbol search branch hacked in.
Mostly it includes isolating the main chart startup-sequence to a
function that can be run in a new task every time a new symbol is
requested by the selector/searcher. The actual search functionality
obviously isn't in here yet but minor changes are included as part of
pulling out the `tractor` stream api patch from the symbol search dev
branch.
Avoid bothering with a trio event and expect the caller to do manual shm
registering with the write loop. Provide OHLC sample period indexing
through a re-branded pub-sub func ``iter_ohlc_periods()``.
Move all feed/stream agnostic logic and shared mem writing into a new
set of routines inside the ``data`` sub-package. This lets us move
toward a more standard API for broker and data backends to provide
cache-able persistent streams to client apps.
The data layer now takes care of
- starting a single background brokerd task to start a stream for as
symbol if none yet exists and register that stream for later lookups
- the existing broker backend actor is now always re-used if possible
if it can be found in a service tree
- synchronization with the brokerd stream's startup sequence is now
oriented around fast startup concurrency such that client code gets
a handle to historical data and quote schema as fast as possible
- historical data loading is delegated to the backend more formally by
starting a ``backfill_bars()`` task
- write shared mem in the brokerd task and only destruct it once requested
either from the parent actor or further clients
- fully de-duplicate stream data by using a dynamic pub-sub strategy
where new clients register for copies of the same quote set per symbol
This new API is entirely working with the IB backend; others will need
to be ported. That's to come shortly.
Add a `Services` nurseries container singleton for spawning sub-daemons
inside the long running `pikerd` tree. Bring in `brokerd` spawning util
funcs to start getting eyes on what can be factored into a service api.
The direct open is needed for running `pikerd` cmd and
the ems spawn point is the first step toward detaching UI based order
entry from the engine itself.
- break (custom) graphics item style marker drawing into separate func
but keep using it since it still seems oddly faster then the
QGraphicsPathItem thing..
- unfactor hover handler; it was uncessary
- make both the graphics path item and custom graphics items approaches
both work inside ``.paint()``
Add support for drawing ``QPathGraphicsItem`` markers but don't use them
since they seem to be shitting up when combined with the infinite line
(bounding rect?): weird artifacts and whatnot. The only way to avoid
said glitches seems to be to update inside the infinite line's
`.paint()` but that slows stuff down.. Instead stick with the manual
paint job use the same pin point: left of the L1 spread graphics - where
the lines now also extend to.
Further stuff:
- Pin the y-label to a line's value on hover.
- Disable x-dimension line moving
- Rework the labelling to be more minimal
Add a line which shows the current average price position with and arrow
marker denoting the direction (long or short). Required some further
rewriting of the infinite line from pyqtgraph including:
- adjusting marker (arrow) placement to be offset from axis + l1 labels
- fixing the hover event to not require the `.movable` attribute to be
set
It's a super naive implementation with no slippage model or network
latency besides some slight delays. Clearing only happens on bid/ask
sweep ticks at the moment - simple last volume based clearing coming
up next.
This turned into a larger endeavour then intended but now we're using our
own label system on level lines to be able to display things nicely
**pinned wherever we want in the UI**. Keep the old ``LevelLabel`` for
now for the L1 graphics but we'll likely replace this as well since i'm
pretty sure the new label type (which wraps `QGraphicsTextItem`) is more
performant anyway.
For labels that want it add nice arrow paths that point just over the
respective axis. Couple label text offset from the axis line based on
parent 'tickTextOffset' setting. Drop `YSticky` it was not enough
meat to bother with.
The min tick size is the smallest step an instrument can move in value
(think the number of decimals places of precision the value can have).
We start leveraging this in a few places:
- make our internal "symbol" type expose it as part of it's api
so that it can be passed around by UI components
- in y-axis view box scaling, use it to keep the bid/ask spread (L1 UI)
always on screen even in the case where the spread has moved further
out of view then the last clearing price
- allows the EMS to determine dark order live order submission offsets
Async spawn a deats getter task whenever we load a symbol data feed.
Pass these symbol details in the first message delivered by the feed at
open. Move stream loop into a new func.
Basically a stop limit mode where the dirty execution-condition deats
are entirely held client side away from the broker. For now, there's
a static order size setting and a 0.5% limit setting relative to the
trigger price. Swap to using 'd' for dump and 'f' for fill - they're
easier for use with ctrl (which is used now to submit orders directly to
broker - ala "live (order) mode"). Still more kinks to work out with too
fast cancelled orders and alerts but we're getting there.
Our first major UI "mode" (yes kinda like the modes in emacs) that has
handles to a client side order book api, line and arrow editors, and
interacts with a spawned `emsd` (the EMS daemon actor).
Buncha cleaning and fixes in here for various thingers as well.
Since the "crosshair" is growing more and more UX implementation details
it probably makes sense to call it what it is; a python level mouse
abstraction. Add 2 internal sets: `_hovered` for allowing mouse hovered
objects to register themselves to other cursor aware components, and
`_trackers` for allowing scene items to "track" cursor movements via
a `on_tracked_source()` callback.
Support tracking the mouse cursor using a new `on_tracked_sources()`
callback method. Make hovered highlight a bit thicker and highlight when
click-dragged. Add a delete method for removing from the scene along
with label.
Leverages `QGraphicsItem.cacheMode` to speed up interactivity via
less `.paint()` calls (on mouse interaction) and redraws of the
underlying path when there are no transformations (other then a shift).
In order to keep the "flat bar on new time period" UX, a couple special
methods have to be triggered to get a redraw of the pixel buffer when
appending new data.
Use `QPainterPath.controlPointRect()` over `.boundingRect()` since
supposedly it's a lot faster. Drop all use of `QPicture` (since it seems
to conflict with the pixel buffer stuff?) and it doesn't give any
measurable speedup when drawing the "last bar" lines.
Oh, and add some profiling for now.
This is a bit hacky (what with array indexing semantics being relative
to the primary index's "start" value but it works. We'll likely want
to somehow wrap this index finagling into an API soon.
Failed at using either.
Quirks in numba's typing require specifying readonly arrays by
composing types manually.
The graphics item path thing, while it does take less time to write on
bar appends, seems to be slower in general in calculating the
``.boundingRect()`` value. Likely we'll just add manual max/min tracking
on array updates like ``pg.PlotCurveItem`` to squeeze some final juices
on this.
Pertains further to #109.
Instead of redrawing the entire `QPainterPath` every time there's
a historical bars update just use `.addPath()` to slap in latest
history. It seems to work and is fast. This also seems like it will be
a great strategy for filling in earlier data, woot!
This gives a massive speedup when viewing large bar sets (think a day's
worth of 5s bars) by using the `pg.functions.arrayToQPath()` "magic"
binary array writing that is also used in `PlotCurveItem`. We're using
this same (lower level) function directly to draw bars as part of one
large path and it seems to be painting 15k (ish) bars with around 3ms
`.paint()` latency. The only thing still a bit slow is the path array
generation despite doing it with `numba`. Likely, either having multiple
paths or, only regenerating the missing backing array elements should
speed this up further to avoid slight delays when incrementing the bar
step.
This is of course a first draft and more cleanups are coming.
This makes it so you don't have to ctrl-c kill apps.
Add in the experimental openGL support even though I'm pretty sure it's
not being used much for curve plotting (but could be wrong).
Break the chart update code for fsps into a new task (add a nursery) in
new `spawn_fsps` (was `chart_from_fsps`) that async requests actor
spawning and initial historical data (all CPU bound work). For multiple
fsp subcharts this allows processing initial output in parallel
(multi-core). We might want to wrap this in a "feed" like api
eventually. Basically the fsp startup sequence is now:
- start all requested fsp actors in an async loop and wait for
historical data to arrive
- loop through them all again to start update tasks which do chart
graphics rendering
Add separate x-axis objects for each new subchart (required by
pyqtgraph); still need to fix hiding unnecessary ones.
Add a `ChartPlotWidget._arrays: dict` for holding overlay data distinct
from ohlc. Drop the sizing yrange to label heights for now since it's
pretty much all gone to hell since adding L1 labels. Fix y-stickies to
look up correct overly arrays.
Requires decent modification of the built-in ``ViewBox``.
We do away with the zoom functionality for now and instead just add
a label full of some simple stats on the bounded data.
I think this gets us to the same output as TWS both on booktrader and
the quote details pane. In theory there might be logic needed to
decreases an L1 queue size on trades but can't seem to get it without
getting -ves displayed occasionally - thus leaving it for now.
Also, fix the max-min streaming logic to actually do its job, lel.
Start a simple API for L1 bid/ask labels.
Make `LevelLabel` draw a line above/below it's text (instead of the
rect fill we had before) since it looks much simpler/slicker.
Generalize the label text orientation through bounding rect
geometry positioning.
Until we get a better datum "cursor" figured out just draw the flat bar
despite the extra overhead. The reason to do this in 2 separate calls is
detailed in the comment but basic gist is that there's a race between
writer and reader of the last shm index.
Oh, and toss in some draft symbol search label code.
Not sure what fixed it exactly, and I guess we didn't need any relative
DPI scaling factor after all. Using the 3px margin on the level label
seems to make it look nice for any font size (i think) as well.
Gonna need some cleanup after this one.
Make our own ``Axis`` and have it call an impl specific ``.resize()``
such that different axes can size to their own spec. Allow passing in a
"typical maximum value string" which will be used by default for sizing
the axis' minor dimension; a common value should be passed to all axes
in a linked split charts widget. Add size hinting for axes labels such
that they can check their parent (axis) for desired dimensions if
needed.
Compute the size in pixels the label based on the label's contents.
Eventually we want to have an update system that can iterate through
axes and labels to do this whenever needed (eg. after widget is moved
to a new screen with a different DPI).
Avoid drawing a new new sticky position if the mouse hasn't moved to the
next (rounded) index in terms of the scene's coordinates. This completes
the "discrete-ization" of the mouse/cursor UX.
Finalizing this feature helped discover and solve
pyqtgraph/pyqtgraph#1418 which masssively improves interaction
performance throughout the whole lib!
Hide stickys on startup until cursor shows up on plot.
This is likely a marginal improvement but is slightly less execution and
adds a coolio black border around the label. Drop all the legacy code
from quantdom which was quite a convoluted mess for "coloring".
Had to tweak sticky offsets to get the crosshair to line up right; not
sure what that's all about yet.
With the improved update logic on `BarsItems` it doesn't seem to be
necessary. Remove y sticky for overlays for now to avoid clutter that
looks like double draws when the last overlay value is close to the last
price.
It seems a plethora of problems (including drawing performance) are due
to trying to hack around the strange rendering bug in Qt with `QLineF`
with y1 == y2. There was all sorts of weirdness that would show up with
trying (a hack) to just set all 4 points to the same value including
strange infinite diagonal ghost lines randomly on charts. Instead, just
place hold these flat bar's 'body' line with a `None` and filter the
null values out before calling `QPainter.drawLines()`. This results
in simply no body lines drawn for these datums. We can probably `numba`
the filtering too if it turns out to be a bottleneck.
Add a new graphic `LineDot` which is a `pg.CurvePoint` that draws
a simple filled dot over a curve at the specified index.
Add support for adding these cursor-dots to the crosshair/mouse through
a new `CrossHair.add_curve_cursor()`. Discretized the vertical line
updates on the crosshair such that it's only drawn in the middle of
the current bar in the main chart.
Makes the chart act like tws where each new time step increment the
chart shifts to the right so that the last bar stays in place. This
gets things looking like a proper auto-trading UX.
Added a couple methods to ``ChartPlotWidget`` to make this work:
- ``.default_view()`` to set the preferred view based on user settings
- ``.increment_view()`` to shift the view one time frame right
Also, split up the `.update_from_array()` method to be curve/ohlc
specific allowing for passing in a struct array with a named field
containing curve data more straightforwardly. This also simplifies the
contest label update functions.
Lookup overlay contents from the OHLC struct array (for now / to make
things work) and fix anchoring logic with better offsets to keep
contents labels super tight to the edge of the view box. Unfortunately,
had to hack the label-height-calc thing for avoiding overlap of graphics
with the label; haven't found a better solution yet and pyqtgraph seems
to require more rabbit holing to figure out something better. Slap in
some inf lines for over[sold/bought] rsi conditions thresholding.
If you have a common broker feed daemon then likely you don't want to
create superfluous shared mem buffers for the same symbol. This adds an
ad hoc little context manger which keeps a bool state of whether
a buffer writer task currently is running in this process. Before we
were checking the shared array token cache and **not** clearing it when
the writer task exited, resulting in incorrect writer/loader logic on
the next entry..
Really, we need a better set of SC semantics around the shared mem stuff
presuming there's only ever one writer per shared buffer at given time.
Hopefully that will come soon!
If we know the max and min in view then on datum updates we can avoid
resizing the y-range when a new max/min has not yet arrived.
This adds a very naive numpy calc in the drawing thread which we can
likely improve with a more efficient streaming alternative which can
also likely be run in a fsp subactor. Also, since this same calc is
essentially done inside `._set_yrange()` we will likely want to allow
passing the result into the method to avoid duplicate work.
This kicks off what will be the beginning of hopefully a very nice
(soft) real-time financial signal processing system. We're keeping the
hack to "time align" curves (for now) with the bars for now by slapping
in an extra datum at index 0.
Just like for the source OHLC, we now have the chart parent actor create
an fsp shm array and use it to read back signal data for plotting.
Some tweaks to get the price chart (and sub-charts) to load historical
datums immediately instead of waiting on an initial quote.
- Move to new shared mem system only writing on the first (by process)
entry to `stream_quotes()`.
- Deliver bars before first quote arrives so that chart can populate and
then wait for initial arrival.
- Allow caching clients per actor.
- Load bars using the same (cached) client that starts the quote stream
thus speeding up initialization.
Wraps the growing tuple of items being delivered by `open_feed()`.
Add lazy loading of the broker's signal step stream with
a `Feed.index_stream()` method.
Add an internal `_Token` to do interchange (un)packing for passing
"references" to shm blocks between actors. Part of the token involves
providing the `numpy.dtype` in a cross-actor format. Add a module
variable for caching "known tokens" per actor. Drop use of context
managers since they tear down shm blocks too soon in debug mode and
there seems to be no reason to unlink/close shm before the process has
terminated; if code needs it torn down explicitly, it can.
Adjust the `data.open_feed()` api to take a shm token so the
broker-daemon can attach a previously created (by the parent actor) mem
buf and push real-time tick data. There's still some sloppiness here in
terms of ensuring only one mem buf per symbol (can be seen in
`stream_quotes()`) which should really managed at the data api level.
Add a bar incrementing stream-task which delivers increment msgs to any
consumers.
Logic in `SharedArray.push()` was totally wrong.
Remove all the `multiprocessing.resource_tracker` crap such that we
aren't loading an extra process at every layer and we don't get tons of
errors when cleaning on in an SC way.
This adds a shared memory "incrementing array" sub-sys interface
for single writer, multi-reader style data passing. The main motivation
is to avoid multiple copies of the same `numpy` array across actors
(plus now we can start being fancy like ray).
There still seems to be some odd issues with the "resource tracker"
complaining at teardown (likely partially to do with SIGINT stuff) so
some further digging in the stdlib code is likely coming.
Pertains to #107 and #98
Added a comment to clarify, ish.
Add `ChartPlotWidget._overlays` as registry of curves added on top of
main graphics. Hackishly (ad-hoc-ishly?) update the curve assuming the
data resides in the same `._array` for now (which it does for historical
vwap).
Allow passing a fixed ylow, yhigh tuple to `._set_yrange()` which avoids
recomputing the range from data if desired (eg. rsi-like bounded
signals). Add support for overlay curves to the OHLC chart and add basic
support to brokers which provide a historical 'vwap`. The data array
increment logic had to be tweaked to copy the vwap from the last bar.
Oh, and hack the subchart curves with two extra prepended datums to make
them align "better" with the ohlc main chart; need to talk to
`pyqtgraph` core about how to do this more correctly.
By mapping any in view "contents labels" to the range of the
``ViewBox``'s data we can avoid having graphics overlap with labels.
Take this approach instead of specifying a min y-range using the std
and activate the range compute on resize and mouser scrolling.
Also, add y-sticky update for signal plots.
Use two separate `QPicture` instances:
- one for the 3 lines for the last bar
- one for all the historical bars lines
On price changes update the last bar and only update historical bars
when the current bar's period expires (when a new bar is "added").
Add a flag `just_history` for this `BarItems.draw_lines()`.
Also, switch the internal lines array/buffer to a 2D numpy array to avoid
the type-cast step and instead just flatten using `numpy.ravel()`.
Overall this should avoid the problem of draws getting slower over time
as new bars are added to the history since price updates only redraw
a single bar to the "last" `QPicture` instance. Ideally in the future we
can make the `history` `QPicture` a `QPixmap` but it looks like this
will require some internal work in `pyqtgraph` to support it.
Use a ``rec2array`` struct array converter to generate lines sequence
faster. Start looking into using a `QPixmap` to avoid redrawing all
bars every update.
Add a default "contents label" (eg. OHLC values for bar charts) to each
chart and update on crosshair interaction.
Few technical changes to make this happen:
- adjust bar graphics to have the HL line be in the "middle" of the
underlying arrays' "index range" in the containing view.
- add a label dict each chart's graphics name to a label + update routine
- use symbol names instead of this "main" identifier crap for referring to
particular price curves/graphics
This is a first attempt at a financial signal processing subsystem which
utilizes async generators for streaming frames of numpy array data
between actors. In this initial attempt the focus is on processing price
data and relaying it to the chart app for real-time display. So far this
seems to work (with decent latency) but much more work is likely needed
around improving the data model for even better latency and less data
duplication.
Surprisingly (or not?) a lot of simplifications to the charting code
came out of this in terms of conducting graphics updates in streaming
tasks instead of hiding them inside the obfuscated mess that is the
Qt-style-inheritance-OO-90s-trash. The goal from here on wards will be
to enforce strict semantics around reading and writing of data such that
state is kept outside "object trees" as much as possible and streaming
function semantics guide our flow model. Unsurprisingly, this reduction
in "instance state" is happening wherever we use `trio` ;)
A little summary on the technical changes:
- not going to explain the fsp system yet; it's too nascent and
probably going to get some heavy editing.
- drop any "update" methods from the `LinkedCharts` type since each
sub-chart will have it's own update task and thus a separate update
loop; further individual graphics (per chart) may eventually require
this same design.
- delete `ChartView`; moved into separate mod.
- add "stream from fsp" task to start our foray into real-time actor
processed numpy streaming.
Wait for a first actual real-time quote before starting graphics update
tasks. Use the new normalized tick format brokers are expected to emit
as a `quotes['ticks']` list. Auto detect time frame from historical
bars.
Add `ChartPlotWidget.add_plot()` to add sub charts for indicators which
can be updated independently. Clean up rt bar update code and drop some
legacy ohlc loading cruft.
Stop with all this "main chart" special treatment.
Manage all lines in the same way across all referenced plots.
Add `CrossHair.add_plot()` for adding new plots dynamically.
Just, smh.
There's really nothing coupling it to the graphics class (which frankly
also seems like it doesn't need to be a class.. Qt).
Add support to `.update_from_array()` for diffing with the input array
and creating additional bar-lines where necessary. Note, there are still
issues with the "correctness" here in terms of bucketing open/close
values in the time frame / bar range. Also, this jamming of each bar's 3
lines into a homogeneous array seems like it could be better done with
struct arrays and avoid all this "index + 3" stuff.
Flat bars have a rendering issue we work around by hacking values in `QLineF`
but we have to revert those on any last bar that is being updated in
real-time. Comment out candle implementations for now; we can get back
to it if/when the tinas unite. Oh, and make bars have a little space
between them.
Don't allow zooming to less then a min number of data points. Allow
panning "outside" the data set (i.e. moving one of the sequence "ends"
to the middle of the view. Start adding logging.
For whatever reason if the `QLineF` high/low values are the same a weird
little rectangle is drawn (my guess is a `float` precision error of some
sort). Instead, if they're the same just use one of the values.
Also, store local vars to avoid so many lookups.
`pg.PlotCurveItem.setData()` is normally used for real-time updates to
curves and takes in a whole new array of data to graphics.
It makes sense to stick with this interface especially if
the current datum graphic will originally be drawn from tick quotes and
later filled in when bars data is available (eg. IB has this option in
TWS charts for volume). Additionally, having a data feed api where the push
process/task can write to shared memory and the UI task(s) can read from
that space is ideal. It allows for indicator and algo calculations to be
run in parallel (via actors) with initial price draw instructions
such that plotting of downstream metrics can be "pipelined" into the
chart UI's render loop. This essentially makes the chart UI async
programmable from multiple remote processes (or at least that's the
goal).
Some details:
- Only store a single ref to the source array data on the
`LinkedSplitCharts`. There should only be one reference since the main
relation is **that** x-time aligned sequence.
- Add `LinkedSplitCharts.update_from_quote()` which takes in a quote
dict and updates the OHLC array from it's contents.
- Add `ChartPlotWidget.update_from_array()` method to trigger graphics
updates per chart with consideration for overlay curves.
This makes a OHLC graphics "sequence" update very similar (actually API
compatible) with `pg.PlotCurveItem.setData()`. The difference here is
that only latest OHLC datum is used to update the charts last bar.
This was a mess before with a weird loop using the parent split charts
to update all "indicators". Instead just have each plot do its own
yrange updates since the signals are being handled just fine per plot.
Handle both the OHLC and plane line chart cases with a hacky `try:,
except IndexError:` for now.
Oh, and move the main entry point for the chart app to the relevant
module. I added some WIP bar update code for the moment.
Speed up the lines array creation using proper slice assignment.
This gives another 10% speedup to the historical price rendering.
Drop ``_tina_mode`` support for now since we're not testing it.
Previously graphics were loaded and rendered implicitly during the
import and creation of certain objects. Remove all this and instead
expect client code to pass in the OHLC sequence to plot. Speed up
the bars graphics rendering by simplifying to a single iteration of
the input array; gives about a 2x speedup.
Move chart resize code into our ``ViewBox`` subtype (a ``ChartView``)
in an effort to start organizing interaction behaviour closer to the
appropriate underlying objects. Add some docs for all this and do some
renaming.
Modify the default ``ViewBox`` scroll to zoom behaviour such that
whatever right-most point is visible is used as the "center" for
zooming. Add a "traditional" cross-hair cursor.
- Move out equity plotting to new module.
- Make axis margins and fonts look good on i3.
- Adjust axis labels colors to gray.
- Start commenting a lot of the code after figuring out what it all does
when cross referencing with ``pyqtgraph``.
- Add option to move date axis to middle.
Hand select necessary components to get real-time charting with
`pyqtgraph` from the `Quantdom` projects:
https://github.com/constverum/Quantdom
We've offered to collaborate with the author but have received no
response and the project has not been updated in over a year.
Given this, we are moving forward with taking the required components to
make further improvements upon especially since the `pyqtgraph` project
is now being actively maintained again.
If the author comes back we will be more then happy to contribute
modified components upstream:
https://github.com/constverum/Quantdom/issues/18
Relates to #80
Since the new FSP system will require time aligned data amongst actors,
it makes sense to share broker data feeds as much as possible on a local
system. There doesn't seem to be downside to this approach either since
if not fanning-out in our code, the broker (server) has to do it anyway
(and who knows how junk their implementation is) though with more
clients, sockets etc. in memory on our end. It also preps the code for
introducing a more "serious" pub-sub systems like zeromq/nanomessage.
Start a draft normalization format for (sampled) tick data.
Ideally we move toward the dense tick format (DFT) enforced by
techtonicDB, but for now let's just get a dict of something simple
going: `{'type': 'trade', 'price': <price}` kind of thing. This
gets us started being able to real-time chart from all data feed
back-ends. Oh, and hack in support for XAUUSD..and get subactor
logging workin.
Add a `Client.find_contract()` which internally takes
a <symbol>.<exchange> str as input and uses `IB.qualifyContractsAsync()`
internally to try and validate the most likely contract. Make the module
script call this using `asyncio.run()` for console testing.
Infected `asyncio` support is being added to `tractor` in
goodboy/tractor#121 so delegate to all that new machinery.
Start building out an "actor-aware" api which takes care of all the
`trio`-`asyncio` interaction for data streaming and request handling.
Add a little (shudder) method proxy system which can be used to invoke
client methods from another actor. Start on a streaming api in
preparation for real-time charting.
Start working towards meeting the backend client api.
Infect `asyncio` using `trio`'s new guest mode and demonstrate
real-time ticker streaming to console.
Since the new FSP system will require time aligned data amongst actors,
it makes sense to share broker data feeds as much as possible on a local
system. There doesn't seem to be downside to this approach either since
if not fanning-out in our code, the broker (server) has to do it anyway
(and who knows how junk their implementation is) though with more
clients, sockets etc. in memory on our end. It also preps the code for
introducing a more "serious" pub-sub systems like zeromq/nanomessage.
Wrap the sync client in an async interface in anticipation of an actual
async client. This starts support for the `open_fee()`/`stream_quotes()`
api though the tick normalization isn't correct yet.
This is something I've been meaning to try for a while and will likely
make writing tick data to a db more straight forward (filling in NaN
values is more matter of fact) plus it should minimize bandwidth usage.
Note, it'll require stream consumers to be considerate of non-full
quotes arriving and thus using the first "full" quote message to fill
out dynamically formatted systems or displays.
For easy testing of questrade historical data from cli.
Re-org the common cli components into a new package to avoid having all
commands defined in a top-level module.
There's some expected limitations with the number of sticks allowed in
a single query (they say 2k but I've been able to pull 20k). Also note
without a paid data sub there's a 15m delay on 1m sticks (we'll hack
around that shortly, don't worry).
Gets us better throughput when polling multiple endpoints (eg. option
and stock quotes simultaneously) since slower round trip request won't
block faster ones when using multiple connections.
This required some copy-paste of code from @matham's branch:
https://github.com/kivy/kivy/pull/5241
namely, the stuff in the `utils_async.py` module. I've added all that as
a standalone file for now.
Update the pipfile to use `kivy`'s master branch (since there seems to
be some lingering cython issues in the current release wheels).
- stop displaying search bar widget on <ctrl-c>
- if there's existing search bar content highlight it automatically
to allow user to start typing new content right away
- when activated allow search bar to insert its own set of keybinding
controls; restore prior bindings on exit
Look up the broker module and set up the loglevel locally.
Ask the arbiter for a portal to the data daemon since we can't
pass one to a subactor by reference.
Fixes to `tractor` that resolve issues with async generators being
non-task safe make the need for the mutex lock in
`DataFeed.open_stream()` unnecessary. Also, don't bother pushing empty
quotes from the publisher; avoids hitting the network when possible.
Questrade's API is half baked and can't handle concurrency.
It allows multiple concurrent requests to most endpoints *except*
for the auth endpoint used to refresh tokens:
https://www.questrade.com/api/documentation/security
I've gone through extensive dialogue with their API team and despite
making what I think are very good arguments for doing the request
serialization on the server side, they decided that I should instead
do the "locking" on the client side. Frankly it doesn't seem like they
have that competent an engineering department as it took me a long time
to explain the issue even though it's rather trivial and probably not
that hard to fix; maybe it's better this way.
This adds a few things to ensure more reliable token refreshes on
expiry:
- add a `@refresh_token_on_err` decorator which can be used on `_API`
methods that should refresh tokens on failure
- decorate most endpoints with this *except* for the auth ep
- add locking logic for the troublesome scenario as follows:
* every time a request is sent out set a "request in progress" event
variable that can be used to determine when no requests are currently
outstanding
* every time the auth end point is hit in order to refresh tokens set
an event that locks out other tasks from making requests
* only allow hitting the auth endpoint when there are no "requests in
progress" using the first event
* mutex all auth endpoint requests; there can only be one outstanding
- don't hit the accounts endpoint at client startup; we want to
eventually support keys from multiple accounts and you can disable
account info per key and just share the market data function
Adjust feed locking around internal manager `yields` to make this work.
Also, change quote publisher to deliver a list of quotes for each
retrieved batch. This was actually broken for option streaming since
each quote was being overwritten due to a common `key` value for all
expiries. Asjust the `packetizer` function accordingly to work for
both options and stocks.
The pub-sub data feed system was factored into `tractor` as an
experimental api / subsystem. Move to using that which greatly
simplifies the data feed architecture.
Start working toward a more general (on-demand) pub-sub system which
can be brought into ``tractor``. Right now this just means making
the code in the `fan_out_to_ctxs()` less specific but, eventually
I think this function should be coupled with a decorator and shipped
as a standard "message pattern".
Additionally,
- try out making `BrokerFeed` a `@dataclass`
- strip out all the `trio.Event` / uneeded nursery / extra task crap
from `start_quote_stream()`
This allows for using a monitor to select the current option chain
symbol!
The deats:
- start a bg task which streams the monitor selected symbol
- dynamically repopulate expiry buttons on a newly published symbol
- move static widget creation into a chain method to avoid multiple
quotes requests at startup
- rename a bunch of methods
If quotes are pushed using the adjusted contract symbol (i.e. with
trailing '-1' suffix) the subscriber won't receive them under the
normal symbol. The logic was wrong for determining whether to add
a suffix (was failing for any symbol with an exchange suffix)
which was causing normal data feed subscriptions to fail to match
in every case.
I did some testing of the `optionsIds` parameter to the option quote
endpoint and found that it limits you to 100 symbols so it's not
practical for real-time "all-strike"" chain updating; we have to stick
to filters for now. The only real downside of this is that it seems
multiple filters across multiple symbols is quite latent. I need to
toy with it more to be sure it's not something slow on the client side.
Oh, and store option contract to ids in a `dict` for now as we may want
to try the `optionsIds` thing again down the road as I coordinate with
the QT tech team.
This is an optimization to improve performance when the UI is fed real
time data. Instead of resorting all rows on every quote update, only
re-render when the sort key appears in the quote data, and further, only
resort rows which are changed using bisection based widget insertion to
avoid having `kivy` re-add widgets (and thus re-render graphics) more
often than absolutely necessary.
There's still a ton to polish (and some bugs to fix) but this is a first
working draft of a real-time option chain!
Insights and todos:
- `kivy` widgets need to be cached and reused (eg. rows, cells, etc.)
for speed since it seems creating new ones constantly is quite taxing
on the CPU
- the chain will tear down and re-setup the option data feed stream each
time a different contract expiry button set is clicked
- there's still some weird bug with row highlighting where it seems rows
added from a new expiry set (which weren't previously rendered) aren't
being highlighted reliably
`Row`:
- `no_cell`: support a list of keys for which no cells will be created
- allow passing in a `cell_type` at instantiation
`TickerTable`:
- keep track of rendered rows via a private `_rendered` set
- don't create rows inside `append_row()` expect caller to do it
- never render already rendered widgets in `render_rows()`
Miscellaneous:
- generalize `update_quotes()` to not be tied to specific quote fields
and allow passing in a quote `formatter()` func
- don't bother creating a nursery block until necessary in main
- more commenting
Add some extra fields to each quote that QT should already be
providing (instead of hiding them in the symbol and request contract
info); namely, the expiry and contact type (i.e. put or call).
Define the base set of fields to be displayed in an option chain
UI and add a quote formatter.
Copy out `kivy.clock.triggered` from version 1.10.1 since it isn't yet
available in the `trio`/async branch and use it to throttle the callback
rate. Use a `collections.deque` to LIFO iterate widgets each call
using the heuristic that it's more likely the mouse is still within the
currently highlighted (or it's adjacent neighbors) widget as opposed
to some far away widget (the case when the mouse is moved very
drastically across the window).
Thanks yet again to @tshirtman for suggesting this.
Instead of defining a `on_mouse_pos()` on every widget simply
register and track each widget and loop through them all once (or as much
as is necessary) in a single callback. The assumption here is that we
get a performance boost by looping widgets instead of having `kivy` loop
and call back each widget thus avoiding costly python function calls.
Well that was a doozy; had to rejig pretty much all of it.
The deats:
- Track broker components in a new `DataFeed` namedtuple
- port to new list based batch quotes (not dicts any more)
- lock access to cached broker-client / data-feed instantiation
- respawn tasks that fail due to the network
So much changed to get this working for both stocks and options:
- Index contracts by a new `ContractsKey` named tuple
- Move to pushing lists of quotes instead of dicts since option
subscriptions are often not identified by their "symbol" key and
this makes it difficult at fan out time to know how a quote should
be indexed and delivered. Instead add a special `key` entry to each
quote dict which is the quote's subscription key.
Instead of all this adding/removing of canvas instructions nonsense
simple add a static "highlighted" rectangle to each row and make its
size very small when there's no mouse over.
Mad props to @tshirtman for showing me the light :D
It's still a bit of a shit show, and I've left a lot of commented tweaks
that need to be further played with, but I think this is a much
better look for what I'm considering to be one of the main "entry point"
apps for `piker`. To get any more serious fine tuning the way I want
I may have to talk to some kivy experts as I'm having some headaches
with button borders, padding, and the header row height..
Some of the new changes include:
- port to the new `brokers.data` module
- much darker theme with a stronger terminal vibe
- last trade price and volume amount flash on each trade
- fixed the symbol search bar to be a static height; before it was
getting squashed oddly when using stacked windows
- make all the cells transparent (for now) such that I can just use
a row color (relates to cell padding/spacing - can't seem to ditch it)
- start adding type annotations
Add `contracts` and `optsquote` commands for querying option contracts
info and market quotes respectively. Add a `record` command for
streaming real-time data feed quotes to disk. Port `monitor` to the
new `piker.brokers.data` module. Forward loglevel flags through to
`tractor` for relevant commands.
Add a couple functions for storing and retrieving live json data feed
recordings to disk using a very rudimentary character + newline delimited
format.
Also, split out the pub-sub logic from `stream_quotes()` into a new
func, `fan_out_to_chans()`. Eventually I want to formalize this pattern
into a decorator exposed through `tractor`.
Makes it easy to request all the option contracts for a particular symbol.
Also, let `option_chain()` accept a `date` arg which can be used to only
retrieve quotes for a single expiry date (much faster then getting all
of them).
Every actor now registers (and unregisters) with the arbiter at
startup/teardown. For now the registry is stored in a plain `dict` in
the arbiter's memory. This makes it possible to easily coordinate actors
started as plain Python processes or via `multiprocessing`.
A whole smörgåsbord of changes was required to accomplish this:
- factor handshake steps into a func
- track *every* channel connected to an actor including multiples to the
same remote peer (may want to optimize this later)
- handle `trio.ClosedStreamError` gracefully in the message loop
- add an `open_portal` asynccontextmanager which handles channel
creation, handshaking, and spawning a bg task for msg processing
- add a `start_actor()` for starting in-process actors directly
- add working `get_arbiter()` and `find_actor()` public routines
- `_main` now tries an anonymous channel connect to the stated
arbiter sockaddr and uses that to determine whether to crown itself
Fail gracefully (by "aborting") the same way `trio` does. This avoids
ugly sub-proc tracebacks thrown at the console. Unset the local actor
when `tractor._main` completes. Cancel all tasks for a peer channel on
disconnect.
Drop all channel/connection handling from the core and break up all the
start up steps into compact and useful functions. The main difference is
the daemon now only needs to worry about spawning per broker streaming
tasks and handling symbol list subscription requests.
When an error is raised inside a nursery block (in the local actor)
cancel all spawned children and ensure the error is properly
unsuppressed.
Also,
- change `invoke_cmd` to `send_cmd` and expect the caller to use
`result_from_q` (avoids implicit blocking for responses that might
never arrive)
- `nursery.start()` the channel server block such that we wait for the
underlying listener to spawn before making outbound connections
- cancel the channel server when an actor's main task completes
(given that `outlive_main == False`)
- raise subactor errors directly in the local actors's msg loop
- enforce that `treat_as_gen` async functions respond with a caller id
(`cid`) in each yield packet
Command requests are sent out and responses are handled in a "message
loop" where each command is associated with a "caller id" and multiple
cmds and results are multiplexed on the came inter-actor channel. When
a cmd result arrives it is pushed into a local queue and delivered to the
appropriate calling actor's task. Errors from a remote actor are always shipped
in an "error" packet back to their spawning-parent actor such that any error
in a subactor is always raised directly in the parent. Based on the
first response to a cmd (either a 'return' or 'yield' packet) the caller
side portal will retrieve values by wrapping the local response queue in
either of an async function or generator as appropriate.
- Rename the `Client` to `Channel`
- Add better `__repr__()`
- use laddr, raddr instead of sockaddr, peer
- don't allow re-entrant `Channel.connect()` calls
- Make `Channel` an async iterable
Couple fixes here:
- if no tickers for a watchlist name -> bail
- swallow the symbol data response in the reconnect handler coro
- don't sleep 5 seconds before connecting to subproc daemon...
Resolves#43
When a client loses a connection it will currently need to re-subscribe
for symbols and receive a symbol data summary as a first quote response.
Only run the provided coroutine on reconnect and call the kwarg
`on_reconnect`. The client consuming code is entirely expected at this
point to know how the symbol registration protocol works.
Event if a broker client is already spawned new clients should still
receive a detailed symbol data packet as the first response. Avoid
exposing the new client's queue to the broker (i.e. subscribing it for
quotes) until after first pushing this packet with all bad symbols
filtered out.
Oh boy where to start.
- Handle broken streams in the `StreamQueue` gracefully; terminate the
async generator.
- When a stream queue connection is unwritable discard its subscriptions
inside the quoter task
- If all subscriptions are discarded for a broker then tear down its
quoter task
- Use listener parent nursery for spawning quoter tasks
- Make broker subs data structures global/shared between conn
handler tasks
- Register the `tickers2qs` entry *after* instantiating broker client(s)
(avoids race condition when mulitple client connections are coming
online simultaneously)
- Push smoke quotes to every client not just the first that connects
- Track quoter tasks in a cross-task set
- Handle unsubscriptions more correctly
In order to start working toward a HA distributed
architecture make apps use a `Client` type to talk to daemons.
The `Client` provides fault-tolerance for connection failures such
that the app will continue running until a connection to the original
service can be made or the process is killed. This will make it easier
to simply spawn up new daemon child processes when faults are detected.
Filter out bad symbols by processing an initial batch quote and
pushing to the subscribing client before spawning a quoter task.
This also avoids exposing the quoter task to anything but the
broker module and a `get_quotes()` routine.
Allow client connections to subscribe for quote streams from specific
brokers and spawn broker-client quoter tasks on-demand according
to client connection demands. Support multiple subscribers to a
single daemon process.
Async generators are faster and less code. Handle segmented packets
which can happen during periods of high quote volume. Move per-broker
rate limit logic into daemon task.
Quote queries will hang indefinitely when the network goes down.
Instead poll for network reestablishment such that roaming on
wifi is supported and real-time feeds will resume once the network is
back.
- Add a rate limit cli option
- Allow broker backends to define a max quote query limit
- Add an index ETF list to demonstrate robinhood's real-time prices
- make the % daily change use the previous days close as the reference
price
- color each cell on every change (results in "pulsed" colors on changes)
- tweak some quote fields
- redraw and sort all rows on every quotes update cycle
- error when the QT api is returning None values
Push all ticker quotes to the queue regardless of duplicate
content. That is, don't worry about only pushing new quote changes
(turns out it is useful when coloring a watchlist where multiple
of the same quote may indicate multiple similar trades and we only
want to quickly "pulse" color changes on value changes).
If it is desired to only push new changes, the ``cache`` flag enables
the old behaviour.
Also add `Client.symbols()` for returning symbol data from a sequence of
tickers.
Add `piker quote <tickerA> <tickerB> <tickerC>` command for easily
dumping quote data to the console. With `-df` will dump as a pandas data
frame. Add key filtering to `piker api` calls.
- Extend the qt api to include candles (not working yet), balances, positions.
- Add a `quote()` method to the `Client` for batch ticker quotes and expose
it through a CLI subcommand.
- Make `poll_tickers` push new quotes to a `trio.Queue`
Add a ``poll_tickers`` coro which can be used to "stream" quotes at
a requested rate. Expose through a cli subcommand `piker stream`.
Drop the `pikerd` command for now.
- colorize json response data in logs
- support ``refresh_token`` retrieval from user if the token for some
reason expires while the client is live
- extend api method support for markets, search, symbols, and quotes
- support "proxying" through api calls via an ``api`` coro for one off
client queries (useful for cli testing)
Store tokens in a local config file avoiding any refresh delay
unless necessary when the current access token expires.
Summary:
- move draft main routine into the `brokers` package mod
- start an api wrapper type
- always write the current access tokens to the config on teardown
Tyler Goodlet
Konstantine Tsafatinos
wattygetlood
wattygetlood
goodboy
Guillermo Rodriguez
Conrad Steenberg
Tyler Goodlet
goodboy