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 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.
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.
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.
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!
- 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.
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.
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.
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.
Tyler Goodlet
goodboy
Guillermo Rodriguez