This was just implemented totally wrong but somehow worked XD
The idea was to include all trades that contribute to ongoing position
size since the last time the position was "net zero", i.e. no position
in the asset. Adjust arithmetic to *subtract* from the current size
until a zero size condition is met and then keep all those clears as
part of the "current state" clears table.
Additionally this fixes another bug where the positions freshly loaded
from a ledger *were not* being merged with the current `pps.toml` state.
Gah, was a remaining bug where if you tried to update the pps state with
both new trades and from the ledger you'd do a double add of
transactions that were cleared during a `update_pps()` loop. Instead now
keep all clears in tact until ready to serialize to the `pps.toml` file
in which cases we call a new method `Position.minimize_clears()` which
does the work of only keep clears since the last net-zero size.
Re-implement `update_pps_conf()` update logic as a single pass loop
which does expiry and size checking for closed pps all in one pass thus
allowing us to drop `dump_active()` which was kinda redundant anyway..
Before we weren't emitting pp msgs when a position went back to "net
zero" (aka the size is zero) nor when a new one was opened (wasn't
previously loaded from the `pps.toml`). This reworks a bunch of the
incremental update logic as well as ports to the changes in the
`piker.pp` module:
- rename a few of the normalizing helpers to be more explicit.
- drop calling `pp.get_pps()` in the trades dialog task and instead
create msgs iteratively, per account, by iterating through collected
position and API trade records and calling instead
`pp.update_pps_conf()`.
- always from-ledger-update both positions reported from ib's pp sys and
session api trades detected on ems-trade-dialog startup.
- `update_ledger_from_api_trades()` now does **just** that: only updates
the trades ledger and returns the transaction set.
- `update_and_audit_msgs()` now only the input list of msgs and properly
generates new msgs for newly created positions that weren't previously
loaded from the `pps.toml`.
- use `tomli` package for reading since it's the fastest pure python
reader available apparently.
- add new fields to each pp's clears table: price, size, dt
- make `load_pps_from_toml()`'s `reload_records` a dict that can be
passed in by the caller and is verbatim used to re-read a ledger and
filter to the specified symbol set to build out fresh pp objects.
- add a `update_from_ledger: bool` flag to `load_pps_from_toml()`
to allow forcing a full backend ledger read.
- if a set of trades records is passed into `update_pps_conf()` parse
out the meta data required to cause a ledger reload as per 2 bullets
above.
- return active and closed pps in separate by-account maps from
`update_pps_conf()`.
- drop the `key_by` kwarg.
This makes it possible to refresh a single fqsn-position in one's
`pps.toml` by simply deleting the file entry, in which case, if there is
new trade records passed to `load_pps_from_toml()` via the new
`reload_records` kwarg, then the backend ledger entries matching that
symbol will be filtered and used to recompute a fresh position.
This turns out to be super handy when you have crashes that prevent
a `pps.toml` entry from being updated correctly but where the ledger
does have all the data necessary to calculate a fresh correct entry.
Since some positions obviously expire and thus shouldn't continually
exist inside a `pps.toml` add naive support for tracking and discarding
expired contracts:
- add `Transaction.expiry: Optional[pendulum.datetime]`.
- add `Position.expiry: Optional[pendulum.datetime]` which can be parsed
from a transaction ledger.
- only write pps with a non-none expiry to the `pps.toml`
- change `Position.avg_price` -> `.be_price` (be is "breakeven")
since it's a much less ambiguous name.
- change `load_pps_from_legder()` to *not* call `dump_active()` since
for the only use case it ends up getting called later anyway.
We can probably make this better (and with less file sys accesses) later
such that we keep a consistent pps state in mem and only write async
maybe from another side-task?
What a nightmare this was.. main holdup was that cost (commissions)
reports are fired independent from "fills" so you can't really emit
a proper full position update until they both arrive.
Deatz:
- move `push_tradesies()` and relay loop in `deliver_trade_events()` to
the new py3.10 `match:` syntax B)
- subscribe for, and handle `CommissionReport` events from `ib_insync`
and repack as a `cost` event type.
- handle cons with no primary/listing exchange (like futes) in
`update_ledger_from_api_trades()` by falling back to the plain
'exchange' field.
- drop reverse fqsn lookup from ib positions map; just use contract
lookup for api trade logs since we're already connected..
- make validation in `update_and_audit()` optional via flag.
- pass in the accounts def, ib pp msg table and the proxies table to the
trade event relay task-loop.
- add `emit_pp_update()` too encapsulate a full api trade entry
incremental update which calls into the `piker.pp` apis to,
- update the ledger
- update the pps.toml
- generate a new `BrokerdPosition` msg to send to the ems
- adjust trades relay loop to only emit pp updates when a cost report
arrives for the fill/execution by maintaining a small table per exec
id.
I don't want to rant too much any more since it's pretty clear `ib` has
either zero concern for its (api) user's or a severely terrible data
management team and/or general inter-team coordination system, but this
patch more or less hacks the flex report records to be similar enough to
API "execution" / "fill" records such that they can be similarly
normalized and stored as well as processed for position calculations..
Dirty deats,
- use the `IB.fills()` method for pulling current session trade events
since it's both recommended in the docs and does seem to capture
more extensive meta-data.
- add a `update_ledger_from_api()` helper which does all the insane work
of making sure api trade entries are usable both within piker's global
fqsn system but also compatible with incremental updates of positions
computed from trade ledgers derived from ib's "flex reports".
- add "auditting" of `ib`'s reported positioning API messages by
comparison with piker's new "traders first" breakeven price style and
complain via logging on mismatches.
- handle buy vs. sell arithmetic (via a +ve or -ve multiplier) to make
"size" arithmetic work for API trade entries..
- draft out options contract transaction parsing but skip in pps
generation for now.
- always use the "execution id" as ledger keys both in flex and api
trade processing.
- for whatever weird reason `ib_insync` doesn't include the so called
"primary exchange" in contracts reported in fill events, so do manual
contract lookups in such cases such that pps entries can be placed
in the right fqsn section...
Still ToDo:
- incremental update on trade clears / position updates
- pps audit from ledger depending on user config?
This makes a few major changes but mostly is centered around including
transaction (aka trade-clear) costs in the avg breakeven price
calculation.
TL;DR:
- rename `TradeRecord` -> `Transaction`.
- make `Position.fills` a `dict[str, float]` which holds each clear's
cost value.
- change `Transaction.symkey` -> `.bsuid` for "backend symbol unique id".
- drop `brokername: str` arg to `update_pps()`
- rename `._split_active()` -> `dump_active()` and use input keys
verbatim in output map.
- in `update_pps_conf()` always incrementally update from trade records
even when no `pps.toml` exists yet since it may be both the case that
the ledger needs loading **and** the caller is handing new records not
yet in the ledger.
Begins the position tracking incremental update API which supports both
constructing a `pps.toml` both from trade ledgers as well diff-oriented
incremental update from an existing config assumed to be previously
generated from some prior ledger.
New set of routines includes:
- `_split_active()` a helper to split a position table into the active
and closed positions (aka pps of size 0) for determining entry updates
in the `pps.toml`.
- `update_pps_conf()` to maybe load a `pps.toml` and update it from
an input trades ledger including necessary (de)serialization to and
from `Position` object form(s).
- `load_pps_from_ledger()` a ledger parser-loader which constructs
a table of pps strictly from the broker-account ledger data without
any consideration for any existing pps file.
Each "entry" in `pps.toml` also contains a `fills: list` attr (name may
change) which references the set of trade records which make up its
state since the last net-zero position in the instrument.
Add a `TradeRecord` struct which holds the minimal field set to build
out position entries. Add `.update_pps()` to convert a set of records
into LIFO position entries, optionally allowing for an update to some
existing pp input set. Add `load_pps_from_ledger()` which does a full
ledger extraction to pp objects, ready for writing a `pps.toml`.
Since "flex reports" are only available for the current session's trades
the day after, this adds support for also collecting trade execution
records for the current session and writing them to the equivalent
ledger file.
Summary:
- add `trades_to_records()` to handle parsing both flex and API event
objects into a common record form.
- add `norm_trade_records()` to handle converting ledger entries into
`TradeRecord` types from the new `piker.pps` mod (coming in next
commit).
Start a generic "position related" util mod and bring in the `Position`
type from the allocator , convert it to a `msgspec.Struct` and add
a `.lifo_update()` method. Implement a WIP pp parser from a trades
ledger and use the new lifo method to gather position entries.
Add `ChartPlotWidget._on_screen: bool` which allows detecting for the
first state where there is y-range-able flow data loaded and able to be
drawn. Check for this flag to be set in `.maxmin()` such that until the
historical data is loaded `.default_view()` will be called to ensure
that a blank view is never shown: race with the UI starting versus the
data layer loading flow graphics can have this outcome.
This should hopefully make teardown more reliable and includes better
logic to fail over to a hard kill path after a 3 second timeout waiting
for the instance to complete using the `docker-py` wait API. Also
generalize the supervisor teardown loop by allowing the container config
endpoint to return 2 msgs to expect:
- a startup message that can be read from the container's internal
process logging that indicates it is fully up and ready.
- a teardown msg that can be polled for that indicates the container has
gracefully terminated after a cancellation request which is passed to
our container wrappers `.cancel()` method.
Make the marketstore config endpoint return the 2 messages we previously
had hard coded and use this new api.
This was introduced in #302 but after thorough testing was clear to be
not working XD. Adjust the display loop to update the last graphics
segment on both the OHLC and vlm charts (as well as all deriving fsp
flows) whenever the uppx >= 1 and there is no current path append
taking place (since more datums are needed to span an x-pixel in view).
Summary of tweaks:
- move vlm chart update code to be at the end of the cycle routine and
have that block include the tests for a "interpolated last datum in
view" line.
- make `do_append: bool` compare with a floor of the uppx value (i.e.
appends should happen when we're just fractionally over a pixel of
x units).
- never update the "volume" chart.
Allows for optionally updating a "downsampled" graphics type which is
currently necessary in the `BarItems` -> `FlattenedOHLC` curve switching
case; we don't want to be needlessly redrawing the `Flow.graphics`
object (which will be an OHLC curve) when in flattened curve mode.
Further add a `only_last_uppx: bool` flag to `.draw_last()` to allow
forcing a "last uppx's worth of data max/min" style interpolating line
as needed.
The single-file module was getting way out of hand size-wise with the
new flex report parsing stuff so this starts the process of breaking
things up into smaller modules oriented around trade, data, and ledger
related endpoints.
Add support for backends to declare sub-modules to enable in
a `__enable_modules__: list[str]` module var which is parsed by the
daemon spawning code passed to `tractor`'s `enable_modules: list[str]`
input.
When using m4, we downsample to the max and min of each
pixel-column's-worth of data thus preserving range / dispersion details
whilst not drawing more graphics then can be displayed by the available
amount of horizontal pixels.
Take and apply this exact same concept to the "last datum" graphics
elements for any `Flow` that is reported as being in a downsampled
state:
- take the xy output from the `Curve.draw_last_datum()`,
- slice out all data that fits in the last pixel's worth of x-range
by using the uppx,
- compute the highest and lowest value from that data,
- draw a singe line segment which spans this yrange thus creating
a simple vertical set of pixels which are "filled in" and show the
entire y-range for the most recent data "contained with that pixel".
Instead of using a bunch of internal logic to modify low level paint-able
elements create a `Curve` lineage that allows for graphics "style"
customization via a small set of public methods:
- `Curve.declare_paintables()` to allow setup of state/elements to be
drawn in later methods.
- `.sub_paint()` to allow painting additional elements along with the
defaults.
- `.sub_br()` to customize the `.boundingRect()` dimensions.
- `.draw_last_datum()` which is expected to produce the paintable
elements which will show the last datum in view.
Introduce the new sub-types and load as necessary in
`ChartPlotWidget.draw_curve()`:
- `FlattenedOHLC`
- `StepCurve`
Reimplement all `.draw_last()` routines as a `Curve` method
and call it the same way from `Flow.update_graphics()`
The basic logic is now this:
- when zooming out, uppx (units per pixel in x) can be >= 1
- if the uppx is `n` then the next pixel in view becomes occupied by
a new datum-x-coordinate-value when the diff between the last
datum step (since the last such update) is greater then the
current uppx -> `datums_diff >= n`
- if we're less then some constant uppx we just always update (because
it's not costly enough and we're not downsampling.
More or less this just avoids unnecessary real-time updates to flow
graphics until they would actually be noticeable via the next pixel
column on screen.
This was a bit of a nightmare to figure out but, it seems that the
coordinate caching system will really be a dick (like the nickname for
richard for you serious types) about leaving stale graphics if we don't
reset the cache on downsample full-redraw updates...Sooo, instead we do
this manual reset to avoid such artifacts and consequently (for now)
return a `reset: bool` flag in the return tuple from `Renderer.render()`
to indicate as such.
Some further shite:
- move the step mode `.draw_last()` equivalent graphics updates down
with the rest..
- drop some superfluous `should_redraw` logic from
`Renderer.render()` and compound it in the full path redraw block.
Adds a new pre-graphics data-format callback incremental update api to
our `Renderer`. `Renderer` instance can now overload these custom routines:
- `.update_xy()` a routine which accepts the latest [pre/a]pended data
sliced out from shm and returns it in a format suitable to store in
the optional `.[x/y]_data` arrays.
- `.allocate_xy()` which initially does the work of pre-allocating the
`.[x/y]_data` arrays based on the source shm sizing such that new
data can be filled in (to memory).
- `._xy_[first/last]: int` attrs to track index diffs between src shm
and the xy format data updates.
Implement the step curve data format with 3 super simple routines:
- `.allocate_xy()` -> `._pathops.to_step_format()`
- `.update_xy()` -> `._flows.update_step_xy()`
- `.format_xy()` -> `._flows.step_to_xy()`
Further, adjust `._pathops.gen_ohlc_qpath()` to adhere to the new
call signature.
We're doing this in `Flow.update_graphics()` atm and probably are going
to in general want custom graphics objects for all the diff curve / path
types. The new flows work seems to fix the bounding rect width calcs to
not require the ad-hoc extra `+ 1` in the step mode case; before it was
always a bit hacky anyway. This also tries to add a more correct
bounding rect adjustment for the `._last_line` segment.
Finally this gets us much closer to a generic incremental update system
for graphics wherein the input array diffing, pre-graphical format data
processing, downsampler activation and incremental update and storage of
any of these data flow stages can be managed in one modular sub-system
:surfer_boi:.
Dirty deatz:
- reorg and move all path logic into `Renderer.render()` and have it
take in pretty much the same flags as the old
`FastAppendCurve.update_from_array()` and instead storing all update
state vars (even copies of the downsampler related ones) on the
renderer instance:
- new state vars: `._last_uppx, ._in_ds, ._vr, ._avr`
- `.render()` input bools: `new_sample_rate, should_redraw,
should_ds, showing_src_data`
- add a hack-around for passing in incremental update data (for now)
via a `input_data: tuple` of numpy arrays
- a default `uppx: float = 1`
- add new render interface attrs:
- `.format_xy()` which takes in the source data array and produces out
x, y arrays (and maybe a `connect` array) that can be passed to
`.draw_path()` (the default for this is just to slice out the index
and `array_key: str` columns from the input struct array),
- `.draw_path()` which takes in the x, y, connect arrays and generates
a `QPainterPath`
- `.fast_path`, for "appendable" updates like there was on the fast
append curve
- move redraw (aka `.clear()` calls) into `.draw_path()` and trigger
via `redraw: bool` flag.
- our graphics objects no longer set their own `.path` state, it's done
by the `Flow.update_graphics()` method using output from
`Renderer.render()` (and it's state if necessary)
A bit hacky to get all graphics types working but this is hopefully the
first step toward moving all the generic update logic into `Renderer`
types which can be themselves managed more compactly and cached per
uppx-m4 level.
Which is basically just "deleting" rows from a column series.
You can only use the trim command from the `.cmd` cli and only with a so
called `LocalClient` currently; it's also sketchy af and caused
a machine to hang due to mem usage..
Ideally we can patch in this functionality for use by the rpc api
and have it not hang like this XD
Pertains to https://github.com/alpacahq/marketstore/issues/264
Yet another path ops routine which converts a 1d array into a data
format suitable for rendering a "step curve" graphics path (aka a "bar
graph" but implemented as a continuous line).
Also, factor the `BarItems` rendering logic (which determines whether to
render the literal bars lines or a downsampled curve) into a routine
`render_baritems()` until we figure out the right abstraction layer for
it.
Starts a module for grouping together all our `QPainterpath` related
generation and data format operations for creation of fast curve
graphics. To start, drops `FastAppendCurve.downsample()` and moves
it to a new `._pathops.xy_downsample()`.
Mostly just dropping old commented code for "step mode" format
generation. Always slice the tail part of the input data and move to the
new `ms_threshold` in the `pg` profiler'
Relates to the bug discovered in #310, this should avoid out-of-order
msgs which do not have a `.reqid` set to be error logged to console.
Further, add `pformat()` to kraken logging of ems msging.
Since downsampling with the more correct version of m4 (uppx driven
windows sizing) is super fast now we don't need to avoid downsampling
on low uppx values. Further all graphics objects now support in-view
slicing so make sure to use it on interaction updates. Pass in the view
profiler to update method calls for more detailed measuring.
Even moar,
- Add a manual call to `.maybe_downsample_graphics()` inside the mouse
wheel event handler since it seems that sometimes trailing events get
lost from the `.sigRangeChangedManually` signal which can result in
"non-downsampled-enough" graphics on chart given the scroll amount;
this manual call seems to entirely fix this?
- drop "max zoom" guard since internals now support (near) infinite
scroll out to graphics becoming a single pixel column line XD
- add back in commented xrange signal connect code for easy testing to
verify against range updates not happening without it
This took longer then i care to admit XD but it definitely adds a huge
speedup and with only a few outstanding correctness bugs:
- panning from left to right causes strange trailing artifacts in the
flows fsp (vlm) sub-plot but only when some data is off-screen on the
left but doesn't appear to be an issue if we keep the `._set_yrange()`
handler hooked up to the `.sigXRangeChanged` signal (but we aren't
going to because this makes panning way slower). i've got a feeling
this is a bug todo with the device coordinate cache stuff and we may
need to report to Qt core?
- factoring out the step curve logic from
`FastAppendCurve.update_from_array()` (un)fortunately required some
logic branch uncoupling but also meant we needed special input controls
to avoid things like redraws and curve appends for special cases,
this will hopefully all be better rectified in code when the core of
this method is moved into a renderer type/implementation.
- the `tina_vwap` fsp curve now somehow causes hangs when doing erratic
scrolling on downsampled graphics data. i have no idea why or how but
disabling it makes the issue go away (ui will literally just freeze
and gobble CPU on a `.paint()` call until you ctrl-c the hell out of
it). my guess is that something in the logic for standard line curves
and appends on large data sets is the issue?
Code related changes/hacks:
- drop use of `step_path_arrays_from_1d()`, it was always a bit hacky
(being based on `pyqtgraph` internals) and was generally hard to
understand since it returns 1d data instead of the more expected (N,2)
array of "step levels"; instead this is now implemented (uglily) in
the `Flow.update_graphics()` block for step curves (which will
obviously get cleaned up and factored elsewhere).
- add a bunch of new flags to the update method on the fast append
curve: `draw_last: bool`, `slice_to_head: int`, `do_append: bool`,
`should_redraw: bool` which are all controls to aid with previously
mentioned issues specific to getting step curve updates working
correctly.
- add a ton of commented tinkering related code (that we may end up
using) to both the flow and append curve methods that was written as
part of the effort to get this all working.
- implement all step curve updating inline in `Flow.update_graphics()`
including prepend and append logic for pre-graphics incremental step
data maintenance and in-view slicing as well as "last step" graphics
updating.
Obviously clean up commits coming stat B)
Since we have in-view style rendering working for all curve types
(finally) we can avoid the guard for low uppx levels and without losing
interaction speed. Further don't delay the profiler so that the nested
method calls correctly report upward - which wasn't working likely due
to some kinda GC collection related issue.
More or less this improves update latency like mad. Only draw data in
view and avoid full path regen as much as possible within a given
(down)sampling setting. We now support append path updates with in-view
data and the *SPECIAL CAVEAT* is that we avoid redrawing the whole curve
**only when** we calc an `append_length <= 1` **even if the view range
changed**. XXX: this should change in the future probably such that the
caller graphics update code can pass a flag which says whether or not to
do a full redraw based on it knowing where it's an interaction based
view-range change or a flow update change which doesn't require a full
path re-render.
After much effort (and exhaustion) but failure to get a view into our
`numpy` OHLC struct-array, this instead allocates an in-thread-memory
array which is updated with flattened data every flow update cycle.
I need to report what I think is a bug to `numpy` core about the whole
view thing not working but, more or less this gets the same behaviour
and minimizes work to flatten the sampled data for line-graphics drawing
thus improving refresh latency when drawing large downsampled curves.
Update the OHLC ds curve with view aware data sliced out from the
pre-allocated and incrementally updated data (we had to add a last index
var `._iflat` to track appends - this should be moved into a renderer
eventually?).
This begins the removal of data processing / analysis methods from the
chart widget and instead moving them to our new `Flow` API (in the new
module introduce here) and delegating the old chart methods to the
respective internal flow. Most importantly is no longer storing the
"last read" of an array from shm in an internal chart table (was
`._arrays`) and instead the `ShmArray` instance is passed as input and
stored in the `Flow` instance. This greatly simplifies lookup logic such
that the display loop now doesn't have to worry about reading shm, it
can be done by internal graphics logic as desired. Generally speaking,
all previous `._arrays`/`._graphics` lookups are now delegated to the
entries in the chart's `._flows` table.
The new `Flow` methods are generally better factored and provide more
detailed output regarding data-stream <-> graphics inter-relations for
the future purpose of allowing much more efficient update calls in the
display loop as well as supporting low latency interaction UX.
The concept here is that we're introducing an intermediary layer that
ties together graphics and real-time data flows such that widget code is
oriented around plot layout and the flow apis are oriented around
real-time low latency updates and providing an efficient high level
metric layer for the UX.
The summary api transition is something like:
- `update_graphics_from_array()` -> `.update_graphics_from_flow()`
- `.bars_range()` -> `Flow.datums_range()`
- `.bars_range()` -> `Flow.datums_range()`
Given that naming the port map is mostly pointless, since accounts can
be detected once the client connects, just expect a `brokers.toml` to
define a simple sequence of port numbers. Toss in a warning for using
the old map/`dict` style.
Now that we have working client auth thanks to:
https://github.com/barneygale/asyncvnc/pull/4 and related issue,
we can use a pw for the vnc server, though we should eventually
auto-generate a random one from a docker super obviously.
Add logic to the data reset hack loop to do a connection reset after
2 failed/timeout attempts at the regular data reset. We need to also add
this logic around reconnectionn events that are due to the host
network connection: aka roaming that's faster then timing logic
builtin to the gateway.
`ib-gw` seems particularly fragile to connections from clients with the
same id (can result in weird connect hangs and even crashes) and
`ib_insync` doesn't handle intermittent tcp disconnects that
well..(especially on dockerized IBC setups). This adds a bunch of
changes to our client caching and scan loop as well a proper
task-locking-to-cache-proxies so that,
- `asyncio`-side clients aren't double-loaded/connected even when
explicitly trying to reconnect repeatedly with a given client to work
around the unreliability of the `asyncio.Transport` design in
`ib_insync`.
- we can use `tractor.trionics.maybe_open_context()` to lock the `trio`
side from loading more then one `Client` on the `asyncio` side and
instead on cache hits only making a new `MethodProxy` around the
reused `asyncio`-side client (since each `trio` task needs its own
inter-task msg channel).
- a `finally:` block teardown on all clients loaded in the scan loop
avoids stale connections.
- the connect params are now exposed as named args to
`load_aio_clients()` can be easily controlled from caller code.
Oh, and we properly hooked up the internal `ib_insync` logging to our
own internal schema - makes it a lot easier to debug wtf is going on XD
In order to expose more `asyncio` powered `Client` methods to endpoint
task-code this adds a more extensive and layered set of `MethodProxy`
loading routines, in dependency order these are:
- `load_clients_for_trio()` a `tractor.to_asyncio.open_channel_from()`
entry-point factory for loading all scanned clients on the `asyncio` side
and delivering them over the inter-task channel to a `trio`-side task.
- `get_preferred_data_client()` a simple client instance loading routine
which reads from the users `brokers.toml -> `prefer_data_account:
list[str]` which must list account names, in priority order, that are
acceptable to be used as the main "data connection client" such that
only one of the detected clients is used for data (whereas the rest
are used only for order entry).
- `open_client_proxies()` which delivers the detected `Client` set
wrapped each in a `MethodProxy`.
- `open_data_client()` which directly delivers the preferred data client
as a proxy for `trio` tasks.
- update `open_client_method_proxy()` and `open_client_proxy` to require
an input `Client` instance.
Further impl details:
- add `MethodProxy._aio_ns` to ref the original `asyncio` side proxied instance
- add `Client.trades()` to pull executions from the last day/session
- load proxies inside `trades_dialogue` and use the new `.trades()`
method to try and pull a fill ledger for eventual correct pp price
calcs (pertains to #307)..
We return a copy (since since a view doesn't seem to work..) of the
(field filtered) shm array contents which is the same index-length as
the source data.
Further, fence off the resource tracker disable-hack into a helper
routine.
It seems once in a while a frame can get missed or dropped (at least
with binance?) so in those cases, when the request erlangs is already at
max, we just manually request the missing frame and presume things will
work out XD
Further, discard out of order frames that are "from the future" that
somehow end up in the async queue once in a while? Not sure why this
happens but it seems thus far just discarding them is nbd.
Bleh/🤦, the ``end_dt`` in scope is not the "earliest" frame's
`end_dt` in the async response queue.. Parse the queue's latest epoch
and use **that** to compare to the last last pushed datetime index..
Add more detailed logging to help debug any (un)expected datetime index
gaps.
When the tsdb has a last datum that is in the past less then a "frame's
worth" of sample steps we need to slice out only the data from the
latest frame that doesn't overlap; this fixes that slice logic..
Previously i dunno wth it was doing..
When the market isn't open the feed layer won't create a subscriber
entry in the sampler broadcast loop and so if a manual call to
``broadcast()`` is made (like when trying to update a chart from
a history prepend) we need to handle that case and just broadcast
a random `-1` for now..BD
Expect each backend to deliver a `config: dict[str, Any]` which provides
concurrency controls to `trimeter`'s batch task scheduler such that
backends can define their own concurrency limits.
The dirty deats in this patch include handling history "gaps" where
a query returns a history-frame-result which spans more then the typical
frame size (in seconds). In such cases we reset the target frame index
(datetime index sequence implemented with a `pendulum.Period`) using
a generator protocol `.send()` such that the sequence can be dynamically
re-indexed starting at the new (possibly) pre-gap datetime. The new gap
logic also allows us to detect out of order frames easier and thus wait
for the next-in-order to arrive before making more requests.
Use the new `open_history_client()` endpoint/API and expect backends to
provide a history "getter" routine that can be called to load historical
data into shm even when **not** using a tsdb. Add logic for filling in
data from the tsdb once the backend has provided data up to the last
recorded in the db. Add logic for avoiding overruns of the shm buffer
with more-then-necessary queries of tsdb data.
It turns out (i guess not so shockingly?) that `marketstore` doesn't
always teardown "gracefully" under SIGINT (seems to hang if there are
open client connections which are also in the midst of teardown?) so
this instead first tries the SIGINT and then fails over to a SIGKILL
(destroy loop) which seems to be much more reliable to ensure shutdown
without any downside - in terms of a "hard kill".
Originally i was thinking the issue was root perms related (which get
relegated solely to the `marketstored` daemon actor after spawn) but
actually it was indeed the signalling / application layer causing the
hold-up/latency on teardown. There's a bunch of lingering (now
commented) code which tried to solve this non-problem as well as a bunch
logging/prints to help decipher the root of the issue - this will all
get cleaned out shortly.
If `marketstore` is detected try to only load most recent missing data
from the data provider (broker) and the rest from the tsdb and push it
all to shm for display in the UI. If the provider/broker doesn't have
the history client endpoint, just use the old one for now so we can
start to incrementally add support. Don't start the ohlc step
incrementer task until the backend signals that the feed is live.
Add some basic `numpy` epoch slice logic to generate append and prepend
arrays to write to the db.
Mooar cool things,
- add a `Storage.delete_ts()` method to wipe a column series from the db
easily.
- don't attempt to read in any OHLC series by default on client load
- add some `pyqtgraph` profiling and drop manual latency measures
- if no db series for the fqsn exists write the entire shm array
Also, Start tinkering with `tractor.trionics.ipython_embed()`
In effort to get back to a usable REPL around the mkts client
this adds usage of the new `tractor` integration api as well as logic
for skipping backfilling if existing tsdb arrays are found.
Starts a wrapper around the `marketstore` client to do basic ohlcv query
and retrieval and prototypes out write methods for ohlc and tick.
Try to connect to `marketstore` automatically (which will fail if not
started currently) but we will eventually first do a service query.
Further:
- get `pikerd` working with and without `--tsdb` flag.
- support spawning `brokerd` with no real-time quotes.
- bring back in "fqsn" support that was originally not
in this history before commits factoring.
Not sure how I missed mapping the 5995 grpc port 🤦; done now.
Also adds graceful teardown using SIGINT with included container
logging relayed to the piker console B).
Found this caused breakage on `kraken` orders which triggered the
"insufficient funds" error response. Makes sense since they won't
generate an order id if the order can't ever be submitted.
The most important changes include:
- iterating the new `Flow` type and updating graphics
- adding detailed profiling
- increasing the min uppx before graphics updates are throttled
- including the L1 spread in y-range calcs so that you never have the
bid/ask go "out of view"..
- pass around `Flow`s instead of shms
- drop all the old prototyped downsampling code
If manually managing an overlay you'll likely call `.overlay_plotitem()`
and then a plotting method so we need to accept a plot item input so
that the chart's pi doesn't get assigned incorrectly in the `Flow` entry
(though it is by default if no input is provided).
More,
- add a `Flow.graphics` field and set it to the `pg.GraphicsObject`.
- make `Flow.maxmin()` return `None` in the "can't calculate" cases.
- set shm refs on `Flow` entries.
- don't run a graphics cycle on 'update' msgs from the engine
if the containing chart is hidden.
- drop `volume` from flows map and disable auto-yranging
once $vlm comes up.
Allows for removing resize callbacks for a flow/overlay that you wish to
remove from view (eg. unit volume after dollar volume is up) and thus
less general interaction callback overhead for any plot you don't wish
to show or resize.
Further,
- drop the `autoscale_linked_plots` block for now since with
multi-view-box overlays each register their own vb resize slots
- pull the graphics object from the chart's `Flow` map inside
`.maybe_downsample_graphics()`
This new type wraps a shm data flow and will eventually include things
like incremental path-graphics updates and serialization + bg downsampling
techniques. The main immediate motivation was to get a cached y-range max/min
calc going since profiling revealed the `numpy` equivalents were
actually quite slow as the data set grows large. Likely we can use all
this to drive a streaming mx/mn routine that's always launched as part
of each on-host flow.
This is our official foray into use of `msgspec.Struct` B) and I have to
say, pretty impressed; we'll likely completely ditch `pydantic` from
here on out.
We don't need update graphics on every x-range change since that's what
the display loop does. Instead, only on manual changes do we make manual
calls into `.update_graphics_from_array()` and be sure to iterate all
linked subplots and all their embedded graphics.
The pg profiler seems to have trouble with early `return`s in function
calls (likely muckery with the GC/`.__delete__()`) so let's just try
to avoid it for now until we either fix it (probably by implementing as
a ctx mngr) or use diff one.
Ugh, turns out the wacky `ChartView.maxmin` callback stuff we did (for
determining y-range sizings) currently requires that the volume array
has a "bars in view" result.. so let's make that keep working without
rendering the graphics for the curve (since we're disabling them once
$vlm comes up).
As with the `BarItems` graphics, this makes it possible to pass in a "in
view" range of array data that can be *only* rendered improving
performance for large(r) data sets. All the other normal behaviour is
kept (i.e a persistent, (pre/ap)pendable path can still be maintained)
if a ``view_range`` is not provided.
Further updates,
- drop the `.should_ds_or_redraw()` and `.maybe_downsample()` predicates
instead moving all that logic inside `.update_from_array()`.
- disable the "cache flipping", which doesn't seem to be needed to avoid
artifacts any more?
- handle all redraw/dowsampling logic in `.update_from_array()`.
- even more profiling.
- drop path `.reserve()` stuff until we better figure out how it's
supposed to work.
Drop all the logic originally in `.update_ds_line()` which is now done
internal to our `FastAppendCurve`. Add incremental update of the
flattened OHLC -> line curve (unfortunately using `np.concatenate()` for
the moment) and maintain a new `._ds_line_xy` arrays tuple which keeps
the internal state. Add `.maybe_downsample()` as per the new interaction
update method requirement. Draft out some fast path curve stuff like in
our line graphic. Short-circuit bars path updates when we downsample to
line. Oh, and add a ton more profiling in prep for getting
all this stuff faf.
Build out an interface that makes it super easy to downsample curves
using the m4 algorithm while keeping our incremental `QPainterPath`
update feature. A lot of hard work and tinkering went into getting this
working all in-thread correctly and there are quite a few details..
New interface methods:
- `.x_uppx()` which returns the x-axis "view units per pixel"
- `.px_width()` which returns the total (rounded) x-axis pixels spanned
by the curve in view.
- `.should_ds_or_redraw()` a predicate which checks internal state to
see if either downsampling of the curve should take place, or the curve
should have all downsampling removed and be redrawn with source array
data.
- `.downsample()` the actual ds processing routine which delegates into
the m4 algo impl.
- `.maybe_downsample()` a simple update method which can be called by
the view box when the user changes the zoom level.
Implementation details/changes:
- make `.update_from_array()` check for downsample (or revert to source
aka de-downsample) conditions exist and then downsample and re-draw
path graphics accordingly.
- in order to even further speed up path appends (since our main
bottleneck is measured to be `QPainter.drawPath()` calls with large
paths which are frequently updates), add a secondary path `.fast_path`
which is the path that is real-time updates by incremental appends and
which is painted separately for speed in `.pain()`.
- drop all the `QPolyLine` stuff since it was tested to be much slower
in general and especially so for append-updates.
- stop disabling the cache settings on updates since it doesn't seem to
be required any more?
- more move toward deprecating and removing all lingering interface
requirements from `pg.PlotCurveItem` (like `.xData`/`.yData`).
- adjust `.paint()` and `.boundingRect()` to compensate for the new
`.fast_path`
- add a butt-load of profiling B)
Pretty sure this was most of the cause of the stale (more downsampled)
curves showing when zooming in and out from bars mode quickly. All this
stuff needs to get factored out into a new abstraction anyway, but
i think this get's mostly correct functionality.
Only draw new ds curve on uppx steps >= 4 and stop adding/removing
graphics objects from the scene; doesn't seem to speed anything up
afaict. Add better reporting of ds scale changes.
Only if the uppx increases by more then 2 we redraw the entire line
otherwise just ds with previous params and update the current curve.
This *should* avoid strange lower sample rate artefacts from showing on
updates.
Summary:
- stash both uppx and px width in `._dsi` (downsample info)
- use the new `ohlc_to_m4_line()` flags
- add notes about using `.reserve()` and friends
- always delete last `._array` ref prior to line updates
In an effort to try and make `QPainterPath.reserve()` work, add internal
logic to use the same object without de-allocating memory from
a previous path write/creation.
Note this required the addition of a `._redraw` flag (to be used in
`.clear()` and a small patch to `pyqtgraph.functions.arrayToQPath` to
allow passing in an existing path (thus reusing the same underlying mem
alloc) which will likely be first pushed to our fork.
We were previously ad-hoc scaling up the px count/width to get more
detail at lower uppx values. Add a log scaling sigmoid that range scales
between 1 < px_width < 16.
Add in a flag to use the mxmn OH tracer in `ohlc_flatten()` if desired.
Helpers to quickly convert ohlc struct-array sequences into lines
for consumption by the m4 downsampler. Strip trailing zero entries
from the `ds_m4()` output if found (avoids lines back to origin).
This makes the `'r'` hotkey snap the last bar to the middle of the pp
line arrow marker no matter the zoom level. Now we also boot with
approximately the most number of x units on screen that keep the bars
graphics drawn in full (just before downsampling to a line).
Moved some internals around to get this all in place,
- drop `_anchors.marker_right_points()` and move it to a chart method.
- change `.pre_l1_x()` -> `.pre_l1_xs()` and just have it return the
two view-mapped x values from the former method.
Instead of using a guess about how many x-indexes to reset the last
datum in-view to, calculate and shift the latest index such that it's
just before any L1 spread labels on the y-axis. This makes the view
placement "widget aware" and gives a much more cross-display UX.
Summary:
- add `ChartPlotWidget.pre_l1_x()` which returns a `tuple` of
x view-coord points for the absolute x-pos and length of any L1
line/labels
- make `.default_view()` only shift to see the xlast just outside
the l1 but keep whatever view range xfirst as the first datum in view
- drop `LevelLine.right_point()` since this is now just a
`.pre_l1_x()` call and can be retrieved from the line's internal chart
ref
- drop `._style.bars_from/to_..` vars since we aren't using hard coded
offsets any more
`ChartPlotWidget.curve_width_pxs()` now can be used to get the total
horizontal (x) pixels on screen that are occupied by the current curve
graphics for a given chart. This will be used for downsampling large
data sets to the pixel domain using M4.
Probably the best place to root the profiler since we can get a better
top down view of bottlenecks in the graphics stack.
More,
- add in draft M4 downsampling code (commented) after getting it mostly
working; next step is to move this processing into an FSP subactor.
- always update the vlm chart last y-axis sticky
- set call `.default_view()` just before inf sleep on startup
Obviously determining the x-range from indices was wrong and was the
reason for the incorrect (downsampled) output size XD. Instead correctly
determine the x range and start value from the *values of* the input
x-array. Pretty sure this makes the implementation nearly production
ready.
Relates to #109
All the refs are in the comments and original sample code from infinite
has been reworked to expect the input x/y arrays to already be sliced
(though we can later support passing in the start-end indexes if
desired).
The new routines are `ds_m4()` the python top level API and `_m4()` the
fast `numba` implementation.
- the chart's uppx (units-per-pixel) is > 4 (i.e. zoomed out a lot)
- don't shift the chart (to keep the most recent step in view) if the
last datum isn't in view (aka the user is probably looking at history)
When a bars graphic is zoomed out enough you get a high uppx, datum
units-per-pixel, and there is no point in drawing the 6-lines in each
bar element-graphic if you can't see them on the screen/display device.
Instead here we offer converting to a `FastAppendCurve` which traces
the high-low outline and instead display that when it's impossible to see the
details of bars - approximately when the uppx >= 2.
There is also some draft-commented code in here for downsampling the
outlines as zoom level increases but it's not fully working and should
likely be factored out into a higher level api anyway.
In effort to start getting some graphics speedups as detailed in #109,
this adds a `FastAppendCurve`to every `BarItems` as a `._ds_line` which
is only displayed (instead of the normal mult-line bars curve) when the
"width" of a bar is indistinguishable on screen from a line -> so once
the view coordinates map to > 2 pixels on the display device.
`BarItems.maybe_paint_line()` takes care of this scaling detection logic and is
called by the associated view's `.sigXRangeChanged` signal handler.
The graphics update loop is much easier to grok when all the UI
components which potentially need to be updated on a cycle are arranged
together in a high-level composite namespace, thus this new
`DisplayState` addition. Create and set this state on each
`LinkedSplits` chart set and add a new method `.graphics_cycle()` which
let's a caller trigger a graphics loop update manually. Use this method
in the fsp graphics manager such that a chain can update new history
output even if there is no real-time feed driving the display loop (eg.
when a market is "closed").
As per https://github.com/erdewit/ib_insync/pull/454 the more correct
way to do this is with `.reqContractDetailsAsync()` which we wrap with
`Client.con_deats()` and which works just as well. Further drop all the
`dict`-ifying that was being done in that method and instead always
return `ContractDetails` object in an fqsn-like explicitly keyed `dict`.
ib has a throttle limit for "hft" bars but contained in here is some
hackery using ``xdotool`` to reset data farms auto-magically B)
This copies the working script into the ib backend mod as a routine and
now uses `trio.run_process()` and calls into it from the `get_bars()`
history retriever and then waits for "data re-established" events to be
received from the client before making more history queries.
TL;DR summary of changes:
- relay ib's "system status" events (like for data farm statuses)
as a new "event" msg that can be processed by registers of
`Client.inline_errors()` (though we should probably make a new
method for this).
- add `MethodProxy.status_event()` which allows a proxy user to register
for a particular "system event" (as mentioned above), which puts
a `trio.Event` entry in a small table can be set by an relay task if
there are any detected waiters.
- start a "msg relay task" when opening the method proxy which does
the event setting mentioned above in the background.
- drop the request error handling around the proxy creation, doesn't
seem necessary any more now that we have better error propagation from
`asyncio`.
- add event waiting logic around the data feed reset hackzorin.
- change the order relay task to only log system events for now (though
we need to do some better parsing/logic to get tws-external order
updates to work again..
Found an issue (that was predictably brushed aside XD) where the
`ib_insync.util.df()` helper was changing the timestamps on bars data to
be way off (probably a `pandas.Timestamp` timezone thing?).
Anyway, dropped all that (which will hopefully let us drop `pandas` as
a hard dep) and added a buncha timestamp checking as well as start/end
datetime return values using `pendulum` so that consumer code can know
which "slice" is output.
Also added some WIP code to work around "no history found" request
errors where instead now we try to increment backward another 200
seconds - not sure if this actually correct yet.
Make the throttle error propagate through to `trio` again by adding
`dict`-msg support between the two loops such that errors can be
re-raised on the `trio` side. This is all integrated into the
`MethoProxy` and accompanying result relay task.
Further fix a longer standing issue where sometimes the `ib_insync`
order entry method will raise a weird assertion error because it detects
some internal order-id state issue.. Just ignore those and make relay
back an error to the ems in such cases.
Add a bunch of notes for todos surrounding data feed reset hackery.
To start we only have futes working but this allows both searching
and loading multiple expiries of the same instrument by specifying
different expiries with a `.<expiry>` suffix in the symbol key (eg.
`mnq.globex.20220617`). This also paves the way for options contracts
which will need something similar plus a strike property. This change
set also required a patch to `ib_insync` to allow retrieving multiple
"ambiguous" contracts from the `IB.reqContractDetailsAcync()` method,
see https://github.com/erdewit/ib_insync/pull/454 for further discussion
since the approach here might change.
This patch also includes a lot of serious reworking of some `trio`-`asyncio`
integration to use the newer `tractor.to_asyncio.open_channel_from()`
api and use it (with a relay task) to open a persistent connection with
an in-actor `ib_insync` `Client` mostly for history requests.
Deats,
- annot the module with a `_infect_asyncio: bool` for `tractor` spawning
- add a futes venu list
- support ambiguous futes contracts lookups so that all expiries will
show in search
- support both continuous and specific expiry fute contract
qualification
- allow searching with "fqsn" keys
- don't crash on "data not found" errors in history requests
- move all quotes msg "topic-key" generation (which should now be
a broker-specific fqsn) and per-contract quote processing into
`normalize()`
- set the fqsn key in the symbol info init msg
- use `open_client_proxy()` in bars backfiller endpoint
- include expiry suffix in position update keys
This adds a new client manager-factory: `open_client_proxy()` which uses
the newer `tractor.to_asyncio.open_channel_from()` (and thus the
inter-loop-task-channel style) a `aio_client_method_relay()` and
a re-implemented `MethodProxy` wrapper to allow transparently calling
`asyncio` client methods from `trio` tasks. Use this proxy in the
history backfiller task and add a new (prototype)
`open_history_client()` which will be used in the new storage management
layer. Drop `get_client()` which was the portal wrapping equivalent of
the same proxy but with a one-task-per-call approach. Oh, and
`Client.bars()` can take `datetime`, so let's use it B)
Use fqsn as input to the client-side EMS apis but strip broker-name
stuff before generating and sending `Brokerd*` msgs to each backend for
live order requests (since it's weird for a backend to expect it's own
name, though maybe that could be a sanity check?).
Summary of fqsn use vs. broker native keys:
- client side pps, order requests and general UX for order management
use an fqsn for tracking
- brokerd side order dialogs use the broker-specific symbol which is
usually nearly the same key minus the broker name
- internal dark book and quote feed lookups use the fqsn where possible
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.
Tyler Goodlet
dinkus
Guillermo Rodriguez
goodboy
Konstantine Tsafatinos
wattygetlood
wattygetlood