Use shm in fsp cascading

This kicks off what will be the beginning of hopefully a very nice
(soft) real-time financial signal processing system. We're keeping the
hack to "time align" curves (for now) with the bars for now by slapping
in an extra datum at index 0.

piker/fsp/__init__.py

@@ -1,13 +1,16 @@
 """
 Financial signal processing for the peeps.
 """
-from typing import AsyncIterator, Callable
+from typing import AsyncIterator, Callable, Tuple
 
+import trio
+import tractor
 import numpy as np
 
 from ..log import get_logger
 from .. import data
 from ._momo import _rsi
+from ..data import attach_shm_array, Feed
 
 log = get_logger(__name__)
 
@@ -19,7 +22,7 @@ async def latency(
     source: 'TickStream[Dict[str, float]]',  # noqa
     ohlcv: np.ndarray
 ) -> AsyncIterator[np.ndarray]:
-    """Compute High-Low midpoint value.
+    """Latency measurements, broker to piker.
     """
     # TODO: do we want to offer yielding this async
     # before the rt data connection comes up?
@@ -37,33 +40,40 @@ async def latency(
             yield value
 
 
-async def stream_and_process(
-    bars: np.ndarray,
+async def increment_signals(
+    feed: Feed,
+    dst_shm: 'SharedArray',  # noqa
+) -> None:
+    async for msg in await feed.index_stream():
+        array = dst_shm.array
+        last = array[-1:].copy()
+
+        # write new slot to the buffer
+        dst_shm.push(last)
+
+
+
+@tractor.stream
+async def cascade(
+    ctx: tractor.Context,
     brokername: str,
-    # symbols: List[str],
+    src_shm_token: dict,
+    dst_shm_token: Tuple[str, np.dtype],
     symbol: str,
     fsp_func_name: str,
 ) -> AsyncIterator[dict]:
+    """Chain streaming signal processors and deliver output to
+    destination mem buf.
+    """
+    src = attach_shm_array(token=src_shm_token)
+    dst = attach_shm_array(readonly=False, token=dst_shm_token)
 
-    # remember, msgpack-numpy's ``from_buffer` returns read-only array
-    # bars = np.array(bars[list(ohlc_dtype.names)])
-
-    # async def _yield_bars():
-    #     yield bars
-
-    # hist_out: np.ndarray = None
-
-    # Conduct a single iteration of fsp with historical bars input
-    # async for hist_out in func(_yield_bars(), bars):
-    #     yield {symbol: hist_out}
     func: Callable = _fsps[fsp_func_name]
 
     # open a data feed stream with requested broker
-    async with data.open_feed(
-        brokername,
-        [symbol],
-    ) as (fquote, stream):
+    async with data.open_feed(brokername, [symbol]) as feed:
 
+        assert src.token == feed.shm.token
         # TODO: load appropriate fsp with input args
 
         async def filter_by_sym(sym, stream):
@@ -72,9 +82,37 @@ async def stream_and_process(
                     if symbol == sym:
                         yield quotes
 
-        async for processed in func(
-            filter_by_sym(symbol, stream),
-            bars,
-        ):
+        out_stream = func(
+            filter_by_sym(symbol, feed.stream),
+            feed.shm,
+        )
+
+        # Conduct a single iteration of fsp with historical bars input
+        # and get historical output
+        history = await out_stream.__anext__()
+
+
+        # TODO: talk to ``pyqtgraph`` core about proper way to solve this:
+        # XXX: hack to get curves aligned with bars graphics: prepend
+        # a copy of the first datum..
+        dst.push(history[:1])
+
+        # check for data length mis-allignment and fill missing values
+        diff = len(src.array) - len(history)
+        if diff >= 0:
+            for _ in range(diff):
+                dst.push(history[:1])
+
+        # compare with source signal and time align
+        index = dst.push(history)
+
+        yield index
+
+        async with trio.open_nursery() as n:
+            n.start_soon(increment_signals, feed, dst)
+
+            async for processed in out_stream:
                 log.info(f"{fsp_func_name}: {processed}")
-            yield processed
+                index = src.index
+                dst.array[-1][fsp_func_name] = processed
+                await ctx.send_yield(index)