goodboy
GitHub
commit
6166e5900e
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@ -192,6 +192,7 @@ class Client:
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# barSizeSetting='5 secs',
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durationStr='{count} S'.format(count=period_count),
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# barSizeSetting='5 secs',
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barSizeSetting='1 secs',
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# barSizeSetting='1 min',
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@ -328,8 +329,9 @@ class Client:
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exch = 'SMART' if not exch else exch
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contract = (await self.ib.qualifyContractsAsync(con))[0]
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head = await self.get_head_time(contract)
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print(head)
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# head = await self.get_head_time(contract)
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# print(head)
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except IndexError:
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raise ValueError(f"No contract could be found {con}")
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return contract
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@ -617,9 +619,11 @@ async def activate_writer(key: str) -> (bool, trio.Nursery):
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async def fill_bars(
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first_bars,
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shm,
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sym: str,
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first_bars: list,
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shm: 'ShmArray', # type: ignore # noqa
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count: int = 21,
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# count: int = 1,
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) -> None:
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"""Fill historical bars into shared mem / storage afap.
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@ -635,9 +639,7 @@ async def fill_bars(
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try:
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bars, bars_array = await _trio_run_client_method(
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method='bars',
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symbol='.'.join(
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(first_bars.contract.symbol, first_bars.contract.exchange)
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),
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symbol=sym,
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end_dt=next_dt,
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)
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@ -723,7 +725,7 @@ async def stream_quotes(
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# TODO: generalize this for other brokers
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# start bar filler task in bg
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ln.start_soon(fill_bars, bars, shm)
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ln.start_soon(fill_bars, sym, bars, shm)
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times = shm.array['time']
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delay_s = times[-1] - times[times != times[-1]][-1]
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@ -808,7 +810,7 @@ async def stream_quotes(
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['open', 'high', 'low', 'volume']
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]
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new_v = tick['size']
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new_v = tick.get('size', 0)
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if v == 0 and new_v:
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# no trades for this bar yet so the open
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@ -20,18 +20,24 @@ Financial signal processing for the peeps.
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from typing import AsyncIterator, Callable, Tuple
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import trio
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from trio_typing import TaskStatus
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import tractor
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import numpy as np
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from ..log import get_logger
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from .. import data
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from ._momo import _rsi
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from ._momo import _rsi, _wma
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from ._volume import _tina_vwap
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from ..data import attach_shm_array, Feed
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log = get_logger(__name__)
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_fsps = {'rsi': _rsi}
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_fsps = {
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'rsi': _rsi,
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'wma': _wma,
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'vwap': _tina_vwap,
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}
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async def latency(
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@ -70,7 +76,7 @@ async def increment_signals(
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# write new slot to the buffer
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dst_shm.push(last)
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len(dst_shm.array)
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@tractor.stream
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@ -95,66 +101,107 @@ async def cascade(
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async with data.open_feed(brokername, [symbol]) as feed:
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assert src.token == feed.shm.token
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# TODO: load appropriate fsp with input args
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async def filter_by_sym(sym, stream):
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async for quotes in stream:
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for symbol, quotes in quotes.items():
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if symbol == sym:
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yield quotes
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async def fsp_compute(
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task_status: TaskStatus[None] = trio.TASK_STATUS_IGNORED,
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) -> None:
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out_stream = func(
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filter_by_sym(symbol, feed.stream),
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feed.shm,
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)
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# TODO: load appropriate fsp with input args
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# TODO: XXX:
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# THERE'S A BIG BUG HERE WITH THE `index` field since we're
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# prepending a copy of the first value a few times to make
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# sub-curves align with the parent bar chart.
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#
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# This likely needs to be fixed either by,
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# - manually assigning the index and historical data
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# seperately to the shm array (i.e. not using .push())
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# - developing some system on top of the shared mem array that
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# is `index` aware such that historical data can be indexed
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# relative to the true first datum? Not sure if this is sane
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# for derivatives.
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async def filter_by_sym(
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sym: str,
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stream,
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):
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# task cancellation won't kill the channel
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with stream.shield():
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async for quotes in stream:
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for symbol, quotes in quotes.items():
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if symbol == sym:
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yield quotes
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# Conduct a single iteration of fsp with historical bars input
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# and get historical output
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history_output = await out_stream.__anext__()
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out_stream = func(
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filter_by_sym(symbol, feed.stream),
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feed.shm,
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)
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# build a struct array which includes an 'index' field to push
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# as history
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history = np.array(
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np.arange(len(history_output)),
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dtype=dst.array.dtype
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)
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history[fsp_func_name] = history_output
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# TODO: XXX:
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# THERE'S A BIG BUG HERE WITH THE `index` field since we're
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# prepending a copy of the first value a few times to make
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# sub-curves align with the parent bar chart.
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# This likely needs to be fixed either by,
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# - manually assigning the index and historical data
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# seperately to the shm array (i.e. not using .push())
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# - developing some system on top of the shared mem array that
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# is `index` aware such that historical data can be indexed
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# relative to the true first datum? Not sure if this is sane
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# for incremental compuations.
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dst._first.value = src._first.value
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dst._last.value = src._first.value
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# TODO: talk to ``pyqtgraph`` core about proper way to solve this:
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# XXX: hack to get curves aligned with bars graphics: prepend
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# a copy of the first datum..
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# dst.push(history[:1])
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# Conduct a single iteration of fsp with historical bars input
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# and get historical output
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history_output = await out_stream.__anext__()
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# check for data length mis-allignment and fill missing values
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diff = len(src.array) - len(history)
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if diff >= 0:
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print(f"WTF DIFFZZZ {diff}")
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for _ in range(diff):
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dst.push(history[:1])
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# build a struct array which includes an 'index' field to push
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# as history
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history = np.array(
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np.arange(len(history_output)),
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dtype=dst.array.dtype
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)
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history[fsp_func_name] = history_output
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# compare with source signal and time align
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index = dst.push(history)
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yield index
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# check for data length mis-allignment and fill missing values
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diff = len(src.array) - len(history)
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if diff >= 0:
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print(f"WTF DIFF SIGNAL to HISTORY {diff}")
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for _ in range(diff):
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dst.push(history[:1])
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# compare with source signal and time align
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index = dst.push(history)
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await ctx.send_yield(index)
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# setup a respawn handle
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with trio.CancelScope() as cs:
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task_status.started(cs)
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# rt stream
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async for processed in out_stream:
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log.debug(f"{fsp_func_name}: {processed}")
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index = src.index
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dst.array[-1][fsp_func_name] = processed
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# stream latest shm array index entry
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await ctx.send_yield(index)
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last_len = new_len = len(src.array)
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async with trio.open_nursery() as n:
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n.start_soon(increment_signals, feed, dst)
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async for processed in out_stream:
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log.debug(f"{fsp_func_name}: {processed}")
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index = src.index
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dst.array[-1][fsp_func_name] = processed
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await ctx.send_yield(index)
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cs = await n.start(fsp_compute)
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# Increment the underlying shared memory buffer on every "increment"
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# msg received from the underlying data feed.
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async for msg in await feed.index_stream():
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new_len = len(src.array)
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if new_len > last_len + 1:
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# respawn the signal compute task if the source
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# signal has been updated
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cs.cancel()
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cs = await n.start(fsp_compute)
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# TODO: adopt an incremental update engine/approach
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# where possible here eventually!
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array = dst.array
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last = array[-1:].copy()
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# write new slot to the buffer
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dst.push(last)
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last_len = new_len
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@ -151,8 +151,8 @@ def wma(
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return np.convolve(signal, weights, 'valid')
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# @piker.fsp(
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# aggregates=[60, 60*5, 60*60, '4H', '1D'],
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# @piker.fsp.signal(
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# timeframes=['1s', '5s', '15s', '1m', '5m', '1H'],
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# )
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async def _rsi(
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source: 'QuoteStream[Dict[str, Any]]', # noqa
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@ -0,0 +1,93 @@
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# piker: trading gear for hackers
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# Copyright (C) 2018-present Tyler Goodlet (in stewardship of piker0)
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero General Public License as published by
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# the Free Software Foundation, either version 3 of the License, or
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# (at your option) any later version.
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU Affero General Public License for more details.
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# You should have received a copy of the GNU Affero General Public License
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# along with this program. If not, see <https://www.gnu.org/licenses/>.
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from typing import AsyncIterator, Optional
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import numpy as np
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from ..data._normalize import iterticks
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def wap(
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signal: np.ndarray,
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weights: np.ndarray,
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) -> np.ndarray:
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"""Weighted average price from signal and weights.
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"""
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cum_weights = np.cumsum(weights)
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cum_weighted_input = np.cumsum(signal * weights)
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# cum_weighted_input / cum_weights
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# but, avoid divide by zero errors
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avg = np.divide(
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cum_weighted_input,
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cum_weights,
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where=cum_weights != 0
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)
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return (
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avg,
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cum_weighted_input,
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cum_weights,
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)
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async def _tina_vwap(
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source, #: AsyncStream[np.ndarray],
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ohlcv: np.ndarray, # price time-frame "aware"
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anchors: Optional[np.ndarray] = None,
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) -> AsyncIterator[np.ndarray]: # maybe something like like FspStream?
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"""Streaming volume weighted moving average.
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Calling this "tina" for now since we're using HLC3 instead of tick.
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"""
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if anchors is None:
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# TODO:
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# anchor to session start of data if possible
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pass
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a = ohlcv.array
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chl3 = (a['close'] + a['high'] + a['low']) / 3
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v = a['volume']
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h_vwap, cum_wp, cum_v = wap(chl3, v)
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# deliver historical output as "first yield"
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yield h_vwap
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w_tot = cum_wp[-1]
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v_tot = cum_v[-1]
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# vwap_tot = h_vwap[-1]
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async for quote in source:
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for tick in iterticks(quote, types=['trade']):
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# c, h, l, v = ohlcv.array[-1][
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# ['closes', 'high', 'low', 'volume']
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# ]
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# this computes tick-by-tick weightings from here forward
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size = tick['size']
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price = tick['price']
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v_tot += size
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w_tot += price * size
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# yield ((((o + h + l) / 3) * v) weights_tot) / v_tot
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yield w_tot / v_tot
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@ -558,7 +558,9 @@ class ChartPlotWidget(pg.PlotWidget):
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# TODO: see how this handles with custom ohlcv bars graphics
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# and/or if we can implement something similar for OHLC graphics
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clipToView=True,
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# clipToView=True,
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autoDownsample=True,
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downsampleMethod='subsample',
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**pdi_kwargs,
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)
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@ -839,10 +841,6 @@ async def _async_main(
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# eventually we'll support some kind of n-compose syntax
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fsp_conf = {
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# 'vwap': {
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# 'overlay': True,
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# 'anchor': 'session',
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# },
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'rsi': {
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'period': 14,
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'chart_kwargs': {
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@ -852,6 +850,24 @@ async def _async_main(
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}
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# make sure that the instrument supports volume history
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# (sometimes this is not the case for some commodities and derivatives)
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volm = ohlcv.array['volume']
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if (
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np.all(np.isin(volm, -1)) or
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np.all(np.isnan(volm))
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):
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log.warning(
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f"{sym} does not seem to have volume info,"
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" dropping volume signals")
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else:
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fsp_conf.update({
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'vwap': {
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'overlay': True,
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'anchor': 'session',
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},
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})
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async with trio.open_nursery() as n:
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# load initial fsp chain (otherwise known as "indicators")
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@ -1099,11 +1115,11 @@ async def spawn_fsps(
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print(f'FSP NAME: {fsp_name}')
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portal = await n.run_in_actor(
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# name as title of sub-chart
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display_name,
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# subactor entrypoint
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fsp.cascade,
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# name as title of sub-chart
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name=display_name,
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brokername=brokermod.name,
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src_shm_token=src_shm.token,
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dst_shm_token=conf['shm'].token,
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@ -1221,9 +1237,23 @@ async def update_signals(
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# update chart graphics
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async for value in stream:
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# read last
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array = shm.array
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value = array[-1][fsp_func_name]
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# TODO: provide a read sync mechanism to avoid this polling.
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# the underlying issue is that a backfill and subsequent shm
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# array first/last index update could result in an empty array
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# read here since the stream is never torn down on the
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# re-compute steps.
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read_tries = 2
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while read_tries > 0:
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try:
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# read last
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array = shm.array
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value = array[-1][fsp_func_name]
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break
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except IndexError:
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read_tries -= 1
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continue
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if last_val_sticky:
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last_val_sticky.update_from_data(-1, value)
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|
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Loading…
Reference in New Issue