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piker/brokers
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provider "spec" (aka backends)
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==============================
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``piker`` abstracts and encapsulates real-time data feeds across a slew
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of providers covering many (pretty much any) instrument class.
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This doc is shoddy attempt as specifying what a backend must provide as
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a basic api, per functionality-feature set, in order to be supported for
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bny of real-time and historical data feeds and order control via the
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``emsd`` clearing system.
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"providers" must offer a top plevel namespace (normally exposed as
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a python module) which offers to a certain set of (async) functions
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to deliver info through a real-time, normalized data layer.
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Generally speaking we break each ``piker.brokers.<backend_name>`` into
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a python package containing 3 sub-modules:
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- ``.api`` containing lowest level client code used to interact
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specifically with the APIs of the exchange, broker or data provider.
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- ``.feed`` which provides historical and real-time quote stream data
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provider endpoints called by piker's data layer in
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``piker.data.feed``.
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- ``.broker`` which defines endpoints expected by
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``pikerd.clearing._ems`` and which are expected to adhere to the msg
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protocol defined in ``piker.clrearing._messages``.
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Our current set of "production" grade backends includes:
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- ``kraken``
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- ``ib``
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data feeds
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----------
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real-time quotes and tick streaming:
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.. code:: python
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async def stream_quotes(
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send_chan: trio.abc.SendChannel,
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symbols: List[str],
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shm: ShmArray,
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feed_is_live: trio.Event,
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loglevel: str = None, # log level passed in from user config
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# startup sync via ``trio``
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task_status: TaskStatus[Tuple[Dict, Dict]] = trio.TASK_STATUS_IGNORED,
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) -> None:
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this routine must eventually deliver realt-time quote messages by sending them on
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the passed in ``send_chan``; these messages must have specific format.
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there is a very simple but required startup sequence:
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message starup sequence:
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************************
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at a minimum, and asap, a first quote message should be returned for
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each requested symbol in ``symbols``. the message should have a minimum
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format:
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.. code:: python
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quote_msg: dict[str, Any] = {
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'symbol': 'xbtusd', # or wtv symbol was requested
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# this field is required in the initial first quote only (though
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# is recommended in all follow up quotes) but can be
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'last': <last clearing price>, # float
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# tick stream fields (see below for schema/format)
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'ticks': list[dict[str, Any]],
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}
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further streamed quote messages should be in this same format.
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``ticks`` is an optional sequence
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historical OHLCV sampling
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-------------------------
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Example endpoint copyed from the ``binance`` backend:
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.. code:: python
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@acm
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async def open_history_client(
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symbol: str,
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) -> tuple[Callable, int]:
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# TODO implement history getter for the new storage layer.
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async with open_cached_client('binance') as client:
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async def get_ohlc(
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timeframe: float,
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end_dt: datetime | None = None,
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start_dt: datetime | None = None,
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) -> tuple[
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np.ndarray,
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datetime, # start
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datetime, # end
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]:
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if timeframe != 60:
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raise DataUnavailable('Only 1m bars are supported')
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array = await client.bars(
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symbol,
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start_dt=start_dt,
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end_dt=end_dt,
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)
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times = array['time']
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if (
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end_dt is None
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):
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inow = round(time.time())
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if (inow - times[-1]) > 60:
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await tractor.breakpoint()
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start_dt = pendulum.from_timestamp(times[0])
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end_dt = pendulum.from_timestamp(times[-1])
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return array, start_dt, end_dt
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yield get_ohlc, {'erlangs': 3, 'rate': 3}
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This `@acm` routine is responsible for setting up an async historical
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data query routine for both charting and any local storage requirements.
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The returned async func should retreive, normalize and deliver
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a ``tuple[np.ndarray, pendulum.dateime, pendulum.dateime]`` of the the
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``numpy``-ified data, the start and stop datetimes for the delivered
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history "frame". The history backloading routines inside
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``piker.data.feed`` expect this interface for both loading history into
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``ShmArrayt`` real-time buffers as well as any configured
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time-series-database (tsdb) and normally the format of this data is
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OHLCV sampled price and volume data but in theory can be high
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reslolution tick/trades/book times series in the future.
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Currently sampling routines for charting and fsp processing expects
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a max resolution of 1s (second) OHLCV sampled data.
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OHLCV minmal schema
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********************
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ohlcv at a minimum is normally pushed to local shared memory (shm)
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numpy compatible arrays which are read by both UI components for display
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as well auto-strats and algorithmic trading engines. shm is obviously
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used for speed. we also intend to eventually support pure shm tick
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streams for ultra low latency processing by external processes/services.
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the provider module at a minimum must define a ``numpy`` structured
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array dtype ``ohlc_dtype = np.dtype(_ohlc_dtype)`` where the
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``_ohlc_dtype`` is normally defined in standard list-tuple synatx as:
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.. code:: python
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# Broker specific ohlc schema which includes a vwap field
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_ohlc_dtype = [
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('index', int),
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('time', int),
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('open', float),
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('high', float),
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('low', float),
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('close', float),
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('volume', float),
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('count', int),
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('bar_wap', float),
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]
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