Start fsp subpackage, separate momo stuff
Tyler Goodlet
parent
ee6e4d2207
commit
9a59f2408d
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@ -0,0 +1,80 @@
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"""
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Financial signal processing for the peeps.
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"""
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from typing import AsyncIterator, Callable
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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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log = get_logger(__name__)
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_fsps = {'rsi': _rsi}
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async def latency(
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source: 'TickStream[Dict[str, float]]', # noqa
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ohlcv: np.ndarray
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) -> AsyncIterator[np.ndarray]:
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"""Compute High-Low midpoint value.
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"""
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# TODO: do we want to offer yielding this async
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# before the rt data connection comes up?
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# deliver zeros for all prior history
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yield np.zeros(len(ohlcv))
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async for quote in source:
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ts = quote.get('broker_ts')
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if ts:
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# This is codified in the per-broker normalization layer
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# TODO: Add more measure points and diffs for full system
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# stack tracing.
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value = quote['brokerd_ts'] - quote['broker_ts']
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yield value
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async def stream_and_process(
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bars: np.ndarray,
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brokername: str,
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# symbols: List[str],
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symbol: str,
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fsp_func_name: str,
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) -> AsyncIterator[dict]:
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# remember, msgpack-numpy's ``from_buffer` returns read-only array
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# bars = np.array(bars[list(ohlc_dtype.names)])
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# async def _yield_bars():
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# yield bars
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# hist_out: np.ndarray = None
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# Conduct a single iteration of fsp with historical bars input
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# async for hist_out in func(_yield_bars(), bars):
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# yield {symbol: hist_out}
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func: Callable = _fsps[fsp_func_name]
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# open a data feed stream with requested broker
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async with data.open_feed(
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brokername,
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[symbol],
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) as (fquote, stream):
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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 for processed in func(
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filter_by_sym(symbol, stream),
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bars,
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):
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log.info(f"{fsp_func_name}: {processed}")
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yield processed
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@ -1,82 +1,10 @@
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"""
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Financial signal processing for the peeps.
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Momentum bby.
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"""
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from typing import AsyncIterator, List, Callable
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from typing import AsyncIterator
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import tractor
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import numpy as np
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from numba import jit, float64, int64, void, optional
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from .log import get_logger
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from . import data
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log = get_logger(__name__)
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async def latency(
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source: 'TickStream[Dict[str, float]]',
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ohlcv: np.ndarray
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) -> AsyncIterator[np.ndarray]:
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"""Compute High-Low midpoint value.
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"""
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# TODO: do we want to offer yielding this async
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# before the rt data connection comes up?
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# deliver zeros for all prior history
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yield np.zeros(len(ohlcv))
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async for quote in source:
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ts = quote.get('broker_ts')
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if ts:
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# This is codified in the per-broker normalization layer
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# TODO: Add more measure points and diffs for full system
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# stack tracing.
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value = quote['brokerd_ts'] - quote['broker_ts']
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yield value
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async def stream_and_process(
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bars: np.ndarray,
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brokername: str,
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# symbols: List[str],
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symbol: str,
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fsp_func_name: str,
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func: Callable = latency,
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) -> AsyncIterator[dict]:
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# remember, msgpack-numpy's ``from_buffer` returns read-only array
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# bars = np.array(bars[list(ohlc_dtype.names)])
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# async def _yield_bars():
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# yield bars
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# hist_out: np.ndarray = None
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# Conduct a single iteration of fsp with historical bars input
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# async for hist_out in func(_yield_bars(), bars):
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# yield {symbol: hist_out}
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func = _rsi
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# open a data feed stream with requested broker
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async with data.open_feed(
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brokername,
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[symbol],
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) as (fquote, stream):
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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 for processed in func(
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filter_by_sym(symbol, stream),
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bars,
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):
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log.info(f"{fsp_func_name}: {processed}")
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yield processed
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from numba import jit, float64, optional
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# TODO: things to figure the fuck out:
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@ -160,15 +88,15 @@ def rsi(
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down_ema_last: float64 = None,
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) -> 'np.ndarray[float64]':
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alpha = 1/period
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print(signal)
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# print(signal)
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df = np.diff(signal)
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up, down = np.where(df > 0, df, 0), np.where(df < 0, -df, 0)
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up_ema = ema(up, alpha, up_ema_last)
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down_ema = ema(down, alpha, down_ema_last)
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rs = up_ema / down_ema
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print(f'up_ema: {up_ema}\ndown_ema: {down_ema}')
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print(f'rs: {rs}')
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# print(f'up_ema: {up_ema}\ndown_ema: {down_ema}')
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# print(f'rs: {rs}')
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# map rs through sigmoid (with range [0, 100])
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rsi = 100 - 100 / (1 + rs)
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# rsi = 100 * (up_ema / (up_ema + down_ema))
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@ -194,11 +122,14 @@ async def _rsi(
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# deliver history
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yield rsi_h
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_last = sig[-1]
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async for quote in source:
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# tick based updates
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for tick in quote.get('ticks', ()):
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if tick.get('type') == 'trade':
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last = np.array([sig[-1], tick['price']])
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curr = tick['price']
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last = np.array([_last, curr])
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# await tractor.breakpoint()
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rsi_out, up_ema_last, down_ema_last = rsi(
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last,
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@ -206,7 +137,8 @@ async def _rsi(
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up_ema_last=up_ema_last,
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down_ema_last=down_ema_last,
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)
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print(f'last: {last}\n rsi: {rsi_out}')
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_last = curr
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# print(f'last: {last}\n rsi: {rsi_out}')
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yield rsi_out[-1]
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@ -236,5 +168,5 @@ async def wma(
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'valid'
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)
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# todo: handle case where frame_len < length - 1
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_lookback = frame[-(length-1):]
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_lookback = frame[-(length-1):] # noqa
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yield wma
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