Merge pull request #441 from pikers/dark_clearing_repairs

Dark clearing repairs

piker/clearing/_ems.py

@@ -46,6 +46,7 @@ from ..data._normalize import iterticks
 from ..data._source import (
     unpack_fqsn,
     mk_fqsn,
+    float_digits,
 )
 from ..data.feed import (
     Feed,
@@ -1250,6 +1251,7 @@ async def process_client_order_cmds(
 
                 spread_slap: float = 5
                 min_tick = flume.symbol.tick_size
+                min_tick_digits = float_digits(min_tick)
 
                 if action == 'buy':
                     tickfilter = ('ask', 'last', 'trade')
@@ -1258,12 +1260,18 @@ async def process_client_order_cmds(
                     # TODO: we probably need to scale this based
                     # on some near term historical spread
                     # measure?
-                    abs_diff_away = spread_slap * min_tick
+                    abs_diff_away = round(
+                        spread_slap * min_tick,
+                        ndigits=min_tick_digits,
+                    )
 
                 elif action == 'sell':
                     tickfilter = ('bid', 'last', 'trade')
                     percent_away = -0.005
-                    abs_diff_away = -spread_slap * min_tick
+                    abs_diff_away = round(
+                        -spread_slap * min_tick,
+                        ndigits=min_tick_digits,
+                    )
 
                 else:  # alert
                     tickfilter = ('trade', 'utrade', 'last')

piker/pp.py

@@ -29,6 +29,7 @@ import re
 import time
 from typing import (
     Any,
+    Iterator,
     Optional,
     Union,
 )
@@ -116,6 +117,21 @@ class Transaction(Struct, frozen=True):
     # from: Optional[str] = None
 
 
+def iter_by_dt(
+    clears: dict[str, Any],
+) -> Iterator[tuple[str, dict]]:
+    '''
+    Iterate entries of a ``clears: dict`` table sorted by entry recorded
+    datetime presumably set at the ``'dt'`` field in each entry.
+
+    '''
+    for tid, data in sorted(
+        list(clears.items()),
+        key=lambda item: item[1]['dt'],
+    ):
+        yield tid, data
+
+
 class Position(Struct):
     '''
     Basic pp (personal/piker position) model with attached clearing
@@ -183,12 +199,7 @@ class Position(Struct):
         toml_clears_list = []
 
         # reverse sort so latest clears are at top of section?
-        for tid, data in sorted(
-            list(clears.items()),
-
-            # sort by datetime
-            key=lambda item: item[1]['dt'],
-        ):
+        for tid, data in iter_by_dt(clears):
             inline_table = toml.TomlDecoder().get_empty_inline_table()
 
             # serialize datetime to parsable `str`
@@ -301,6 +312,14 @@ class Position(Struct):
     # def lifo_price() -> float:
     #     ...
 
+    def iter_clears(self) -> Iterator[tuple[str, dict]]:
+        '''
+        Iterate the internally managed ``.clears: dict`` table in
+        datetime-stamped order.
+
+        '''
+        return iter_by_dt(self.clears)
+
     def calc_ppu(
         self,
         # include transaction cost in breakeven price
@@ -331,10 +350,9 @@ class Position(Struct):
         asize_h: list[float] = []  # historical accumulative size
         ppu_h: list[float] = []  # historical price-per-unit
 
-        clears = list(self.clears.items())
-
-        for i, (tid, entry) in enumerate(clears):
-
+        tid: str
+        entry: dict[str, Any]
+        for (tid, entry) in self.iter_clears():
             clear_size = entry['size']
             clear_price = entry['price']
 
@@ -344,6 +362,11 @@ class Position(Struct):
 
             sign_change: bool = False
 
+            if accum_size == 0:
+                ppu_h.append(0)
+                asize_h.append(0)
+                continue
+
             if accum_size == 0:
                 ppu_h.append(0)
                 asize_h.append(0)