Only handle hist discrepancies when market is open
We obviously don't want to be debugging a sample-index issue if/when the market for the asset is closed (since we'll be guaranteed to have a mismatch, lul). Pass in the `feed_is_live: trio.Event` throughout the backfilling routines to allow first checking for the live feed being active so as to avoid breakpointing on false +ves. Also, add a detailed warning log message for when *actually* investigating a mismatch.pre_viz_calls
parent
23e1ecbb04
commit
e57a2649d1
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@ -207,7 +207,7 @@ def get_feed_bus(
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) -> _FeedsBus:
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'''
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Retreive broker-daemon-local data feeds bus from process global
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Retrieve broker-daemon-local data feeds bus from process global
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scope. Serialize task access to lock.
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'''
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@ -250,6 +250,7 @@ async def start_backfill(
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shm: ShmArray,
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timeframe: float,
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sampler_stream: tractor.MsgStream,
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feed_is_live: trio.Event,
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last_tsdb_dt: Optional[datetime] = None,
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storage: Optional[Storage] = None,
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@ -281,9 +282,30 @@ async def start_backfill(
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- pendulum.from_timestamp(times[-2])
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).seconds
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if step_size_s == 60:
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# if the market is open (aka we have a live feed) but the
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# history sample step index seems off we report the surrounding
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# data and drop into a bp. this case shouldn't really ever
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# happen if we're doing history retrieval correctly.
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if (
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step_size_s == 60
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and feed_is_live.is_set()
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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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diff = inow - times[-1]
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if abs(diff) > 60:
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surr = array[-6:]
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diff_in_mins = round(diff/60., ndigits=2)
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log.warning(
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f'STEP ERROR `{bfqsn}` for period {step_size_s}s:\n'
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f'Off by `{diff}` seconds (or `{diff_in_mins}` mins)\n'
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'Surrounding 6 time stamps:\n'
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f'{list(surr["time"])}\n'
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'Here is surrounding 6 samples:\n'
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f'{surr}\nn'
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)
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# for now we expect a hacker to investigate this case
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# manually..
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await tractor.breakpoint()
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# frame's worth of sample-period-steps, in seconds
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@ -485,6 +507,7 @@ async def basic_backfill(
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bfqsn: str,
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shms: dict[int, ShmArray],
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sampler_stream: tractor.MsgStream,
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feed_is_live: trio.Event,
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) -> None:
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@ -504,6 +527,7 @@ async def basic_backfill(
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shm,
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timeframe,
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sampler_stream,
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feed_is_live,
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)
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)
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except DataUnavailable:
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@ -520,6 +544,7 @@ async def tsdb_backfill(
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bfqsn: str,
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shms: dict[int, ShmArray],
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sampler_stream: tractor.MsgStream,
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feed_is_live: trio.Event,
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task_status: TaskStatus[
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tuple[ShmArray, ShmArray]
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@ -554,6 +579,8 @@ async def tsdb_backfill(
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shm,
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timeframe,
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sampler_stream,
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feed_is_live,
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last_tsdb_dt=last_tsdb_dt,
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tsdb_is_up=True,
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storage=storage,
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@ -856,6 +883,7 @@ async def manage_history(
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60: hist_shm,
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},
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sample_stream,
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feed_is_live,
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)
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# yield back after client connect with filled shm
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@ -890,6 +918,7 @@ async def manage_history(
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60: hist_shm,
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},
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sample_stream,
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feed_is_live,
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)
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task_status.started((
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hist_zero_index,
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@ -1051,7 +1080,10 @@ async def allocate_persistent_feed(
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# seed the buffer with a history datum - this is most handy
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# for many backends which don't sample @ 1s OHLC but do have
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# slower data such as 1m OHLC.
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if not len(rt_shm.array):
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if (
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not len(rt_shm.array)
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and hist_shm.array.size
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):
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rt_shm.push(hist_shm.array[-3:-1])
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ohlckeys = ['open', 'high', 'low', 'close']
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rt_shm.array[ohlckeys][-2:] = hist_shm.array['close'][-1]
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@ -1062,6 +1094,9 @@ async def allocate_persistent_feed(
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rt_shm.array['time'][0] = ts
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rt_shm.array['time'][1] = ts + 1
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elif hist_shm.array.size == 0:
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await tractor.breakpoint()
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# wait the spawning parent task to register its subscriber
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# send-stream entry before we start the sample loop.
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await sub_registered.wait()
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