Break out old `.pp` components into submods: `._ledger` and `._pos`

rekt_pps
Tyler Goodlet 2023-03-10 17:19:39 -05:00
parent beb6544bad
commit f549de7c88
3 changed files with 1108 additions and 1019 deletions

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# piker: trading gear for hackers
# Copyright (C) Tyler Goodlet (in stewardship for pikers)
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
from __future__ import annotations
from contextlib import contextmanager as cm
import os
from os import path
import time
from typing import (
Any,
Iterator,
Union,
Generator
)
from pendulum import (
datetime,
)
import tomli
import toml
from .. import config
from ..data._source import Symbol
from ..data.types import Struct
from ..log import get_logger
log = get_logger(__name__)
@cm
def open_trade_ledger(
broker: str,
account: str,
) -> Generator[dict, None, None]:
'''
Indempotently create and read in a trade log file from the
``<configuration_dir>/ledgers/`` directory.
Files are named per broker account of the form
``<brokername>_<accountname>.toml``. The ``accountname`` here is the
name as defined in the user's ``brokers.toml`` config.
'''
ldir = path.join(config._config_dir, 'ledgers')
if not path.isdir(ldir):
os.makedirs(ldir)
fname = f'trades_{broker}_{account}.toml'
tradesfile = path.join(ldir, fname)
if not path.isfile(tradesfile):
log.info(
f'Creating new local trades ledger: {tradesfile}'
)
with open(tradesfile, 'w') as cf:
pass # touch
with open(tradesfile, 'rb') as cf:
start = time.time()
ledger = tomli.load(cf)
log.info(f'Ledger load took {time.time() - start}s')
cpy = ledger.copy()
try:
yield cpy
finally:
if cpy != ledger:
# TODO: show diff output?
# https://stackoverflow.com/questions/12956957/print-diff-of-python-dictionaries
log.info(f'Updating ledger for {tradesfile}:\n')
ledger.update(cpy)
# we write on close the mutated ledger data
with open(tradesfile, 'w') as cf:
toml.dump(ledger, cf)
class Transaction(Struct, frozen=True):
# TODO: should this be ``.to`` (see below)?
fqsn: str
sym: Symbol
tid: Union[str, int] # unique transaction id
size: float
price: float
cost: float # commisions or other additional costs
dt: datetime
expiry: datetime | None = None
# optional key normally derived from the broker
# backend which ensures the instrument-symbol this record
# is for is truly unique.
bsuid: Union[str, int] | None = None
# optional fqsn for the source "asset"/money symbol?
# 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

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# piker: trading gear for hackers
# Copyright (C) Tyler Goodlet (in stewardship for pikers)
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see <https://www.gnu.org/licenses/>.
'''
Personal/Private position parsing, calculating, summarizing in a way
that doesn't try to cuk most humans who prefer to not lose their moneys..
(looking at you `ib` and dirt-bird friends)
'''
from __future__ import annotations
from contextlib import contextmanager as cm
from math import copysign
import re
from typing import (
Any,
Iterator,
Optional,
Union,
Generator
)
import pendulum
from pendulum import datetime, now
import toml
from ._ledger import (
Transaction,
iter_by_dt,
open_trade_ledger,
)
from .. import config
from ..brokers import get_brokermod
from ..clearing._messages import BrokerdPosition, Status
from ..data._source import Symbol, unpack_fqsn
from ..data.types import Struct
from ..log import get_logger
log = get_logger(__name__)
class Position(Struct):
'''
Basic pp (personal/piker position) model with attached clearing
transaction history.
'''
symbol: Symbol
# can be +ve or -ve for long/short
size: float
# "breakeven price" above or below which pnl moves above and below
# zero for the entirety of the current "trade state".
ppu: float
# unique backend symbol id
bsuid: str
split_ratio: Optional[int] = None
# ordered record of known constituent trade messages
clears: dict[
Union[str, int, Status], # trade id
dict[str, Any], # transaction history summaries
] = {}
first_clear_dt: Optional[datetime] = None
expiry: Optional[datetime] = None
def to_dict(self) -> dict:
return {
f: getattr(self, f)
for f in self.__struct_fields__
}
def to_pretoml(self) -> tuple[str, dict]:
'''
Prep this position's data contents for export to toml including
re-structuring of the ``.clears`` table to an array of
inline-subtables for better ``pps.toml`` compactness.
'''
d = self.to_dict()
clears = d.pop('clears')
expiry = d.pop('expiry')
if self.split_ratio is None:
d.pop('split_ratio')
# should be obvious from clears/event table
d.pop('first_clear_dt')
# TODO: we need to figure out how to have one top level
# listing venue here even when the backend isn't providing
# it via the trades ledger..
# drop symbol obj in serialized form
s = d.pop('symbol')
fqsn = s.front_fqsn()
broker, key, suffix = unpack_fqsn(fqsn)
sym_info = s.broker_info[broker]
d['asset_type'] = sym_info['asset_type']
d['price_tick_size'] = (
sym_info.get('price_tick_size')
or
s.tick_size
)
d['lot_tick_size'] = (
sym_info.get('lot_tick_size')
or
s.lot_tick_size
)
if self.expiry is None:
d.pop('expiry', None)
elif expiry:
d['expiry'] = str(expiry)
toml_clears_list = []
# reverse sort so latest clears are at top of section?
for tid, data in iter_by_dt(clears):
inline_table = toml.TomlDecoder().get_empty_inline_table()
# serialize datetime to parsable `str`
inline_table['dt'] = str(data['dt'])
# insert optional clear fields in column order
for k in ['ppu', 'accum_size']:
val = data.get(k)
if val:
inline_table[k] = val
# insert required fields
for k in ['price', 'size', 'cost']:
inline_table[k] = data[k]
inline_table['tid'] = tid
toml_clears_list.append(inline_table)
d['clears'] = toml_clears_list
return fqsn, d
def ensure_state(self) -> None:
'''
Audit either the `.size` and `.ppu` local instance vars against
the clears table calculations and return the calc-ed values if
they differ and log warnings to console.
'''
clears = list(self.clears.values())
self.first_clear_dt = min(list(entry['dt'] for entry in clears))
last_clear = clears[-1]
csize = self.calc_size()
accum = last_clear['accum_size']
if not self.expired():
if (
csize != accum
and csize != round(accum * self.split_ratio or 1)
):
raise ValueError(f'Size mismatch: {csize}')
else:
assert csize == 0, 'Contract is expired but non-zero size?'
if self.size != csize:
log.warning(
'Position state mismatch:\n'
f'{self.size} => {csize}'
)
self.size = csize
cppu = self.calc_ppu()
ppu = last_clear['ppu']
if (
cppu != ppu
and self.split_ratio is not None
# handle any split info entered (for now) manually by user
and cppu != (ppu / self.split_ratio)
):
raise ValueError(f'PPU mismatch: {cppu}')
if self.ppu != cppu:
log.warning(
'Position state mismatch:\n'
f'{self.ppu} => {cppu}'
)
self.ppu = cppu
def update_from_msg(
self,
msg: BrokerdPosition,
) -> None:
# XXX: better place to do this?
symbol = self.symbol
lot_size_digits = symbol.lot_size_digits
ppu, size = (
round(
msg['avg_price'],
ndigits=symbol.tick_size_digits
),
round(
msg['size'],
ndigits=lot_size_digits
),
)
self.ppu = ppu
self.size = size
@property
def dsize(self) -> float:
'''
The "dollar" size of the pp, normally in trading (fiat) unit
terms.
'''
return self.ppu * self.size
# TODO: idea: "real LIFO" dynamic positioning.
# - when a trade takes place where the pnl for
# the (set of) trade(s) is below the breakeven price
# it may be that the trader took a +ve pnl on a short(er)
# term trade in the same account.
# - in this case we could recalc the be price to
# be reverted back to it's prior value before the nearest term
# trade was opened.?
# 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
# and presume the worst case of the same cost
# to exit this transaction (even though in reality
# it will be dynamic based on exit stratetgy).
cost_scalar: float = 2,
) -> float:
'''
Compute the "price-per-unit" price for the given non-zero sized
rolling position.
The recurrence relation which computes this (exponential) mean
per new clear which **increases** the accumulative postiion size
is:
ppu[-1] = (
ppu[-2] * accum_size[-2]
+
ppu[-1] * size
) / accum_size[-1]
where `cost_basis` for the current step is simply the price
* size of the most recent clearing transaction.
'''
asize_h: list[float] = [] # historical accumulative size
ppu_h: list[float] = [] # historical price-per-unit
tid: str
entry: dict[str, Any]
for (tid, entry) in self.iter_clears():
clear_size = entry['size']
clear_price = entry['price']
last_accum_size = asize_h[-1] if asize_h else 0
accum_size = last_accum_size + clear_size
accum_sign = copysign(1, accum_size)
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)
continue
# test if the pp somehow went "passed" a net zero size state
# resulting in a change of the "sign" of the size (+ve for
# long, -ve for short).
sign_change = (
copysign(1, last_accum_size) + accum_sign == 0
and last_accum_size != 0
)
# since we passed the net-zero-size state the new size
# after sum should be the remaining size the new
# "direction" (aka, long vs. short) for this clear.
if sign_change:
clear_size = accum_size
abs_diff = abs(accum_size)
asize_h.append(0)
ppu_h.append(0)
else:
# old size minus the new size gives us size diff with
# +ve -> increase in pp size
# -ve -> decrease in pp size
abs_diff = abs(accum_size) - abs(last_accum_size)
# XXX: LIFO breakeven price update. only an increaze in size
# of the position contributes the breakeven price,
# a decrease does not (i.e. the position is being made
# smaller).
# abs_clear_size = abs(clear_size)
abs_new_size = abs(accum_size)
if abs_diff > 0:
cost_basis = (
# cost basis for this clear
clear_price * abs(clear_size)
+
# transaction cost
accum_sign * cost_scalar * entry['cost']
)
if asize_h:
size_last = abs(asize_h[-1])
cb_last = ppu_h[-1] * size_last
ppu = (cost_basis + cb_last) / abs_new_size
else:
ppu = cost_basis / abs_new_size
ppu_h.append(ppu)
asize_h.append(accum_size)
else:
# on "exit" clears from a given direction,
# only the size changes not the price-per-unit
# need to be updated since the ppu remains constant
# and gets weighted by the new size.
asize_h.append(accum_size)
ppu_h.append(ppu_h[-1])
final_ppu = ppu_h[-1] if ppu_h else 0
# handle any split info entered (for now) manually by user
if self.split_ratio is not None:
final_ppu /= self.split_ratio
return final_ppu
def expired(self) -> bool:
'''
Predicate which checks if the contract/instrument is past its expiry.
'''
return bool(self.expiry) and self.expiry < now()
def calc_size(self) -> float:
'''
Calculate the unit size of this position in the destination
asset using the clears/trade event table; zero if expired.
'''
size: float = 0
# time-expired pps (normally derivatives) are "closed"
# and have a zero size.
if self.expired():
return 0
for tid, entry in self.clears.items():
size += entry['size']
if self.split_ratio is not None:
size = round(size * self.split_ratio)
return float(
self.symbol.quantize_size(size),
)
def minimize_clears(
self,
) -> dict[str, dict]:
'''
Minimize the position's clears entries by removing
all transactions before the last net zero size to avoid
unecessary history irrelevant to the current pp state.
'''
size: float = 0
clears_since_zero: list[tuple(str, dict)] = []
# TODO: we might just want to always do this when iterating
# a ledger? keep a state of the last net-zero and only do the
# full iterate when no state was stashed?
# scan for the last "net zero" position by iterating
# transactions until the next net-zero size, rinse, repeat.
for tid, clear in self.clears.items():
size += clear['size']
clears_since_zero.append((tid, clear))
if size == 0:
clears_since_zero.clear()
self.clears = dict(clears_since_zero)
return self.clears
def add_clear(
self,
t: Transaction,
) -> dict:
'''
Update clearing table and populate rolling ppu and accumulative
size in both the clears entry and local attrs state.
'''
clear = self.clears[t.tid] = {
'cost': t.cost,
'price': t.price,
'size': t.size,
'dt': t.dt
}
# TODO: compute these incrementally instead
# of re-looping through each time resulting in O(n**2)
# behaviour..?
# NOTE: we compute these **after** adding the entry in order to
# make the recurrence relation math work inside
# ``.calc_size()``.
self.size = clear['accum_size'] = self.calc_size()
self.ppu = clear['ppu'] = self.calc_ppu()
return clear
def sugest_split(self) -> float:
...
class PpTable(Struct):
brokername: str
acctid: str
pps: dict[str, Position]
conf: Optional[dict] = {}
def update_from_trans(
self,
trans: dict[str, Transaction],
cost_scalar: float = 2,
) -> dict[str, Position]:
pps = self.pps
updated: dict[str, Position] = {}
# lifo update all pps from records, ensuring
# we compute the PPU and size sorted in time!
for t in sorted(
trans.values(),
key=lambda t: t.dt,
reverse=True,
):
pp = pps.setdefault(
t.bsuid,
# if no existing pp, allocate fresh one.
Position(
Symbol.from_fqsn(
t.fqsn,
info={},
) if not t.sym else t.sym,
size=0.0,
ppu=0.0,
bsuid=t.bsuid,
expiry=t.expiry,
)
)
clears = pp.clears
if clears:
first_clear_dt = pp.first_clear_dt
# don't do updates for ledger records we already have
# included in the current pps state.
if (
t.tid in clears
or (
first_clear_dt
and t.dt < first_clear_dt
)
):
# NOTE: likely you'll see repeats of the same
# ``Transaction`` passed in here if/when you are restarting
# a ``brokerd.ib`` where the API will re-report trades from
# the current session, so we need to make sure we don't
# "double count" these in pp calculations.
continue
# update clearing table
pp.add_clear(t)
updated[t.bsuid] = pp
# minimize clears tables and update sizing.
for bsuid, pp in updated.items():
pp.ensure_state()
# deliver only the position entries that were actually updated
# (modified the state) from the input transaction set.
return updated
def dump_active(
self,
) -> tuple[
dict[str, Position],
dict[str, Position]
]:
'''
Iterate all tabulated positions, render active positions to
a ``dict`` format amenable to serialization (via TOML) and drop
from state (``.pps``) as well as return in a ``dict`` all
``Position``s which have recently closed.
'''
# NOTE: newly closed position are also important to report/return
# since a consumer, like an order mode UI ;), might want to react
# based on the closure (for example removing the breakeven line
# and clearing the entry from any lists/monitors).
closed_pp_objs: dict[str, Position] = {}
open_pp_objs: dict[str, Position] = {}
pp_objs = self.pps
for bsuid in list(pp_objs):
pp = pp_objs[bsuid]
# XXX: debug hook for size mismatches
# qqqbsuid = 320227571
# if bsuid == qqqbsuid:
# breakpoint()
pp.ensure_state()
if (
# "net-zero" is a "closed" position
pp.size == 0
# time-expired pps (normally derivatives) are "closed"
or (pp.expiry and pp.expiry < now())
):
# for expired cases
pp.size = 0
# NOTE: we DO NOT pop the pp here since it can still be
# used to check for duplicate clears that may come in as
# new transaction from some backend API and need to be
# ignored; the closed positions won't be written to the
# ``pps.toml`` since ``pp_active_entries`` above is what's
# written.
closed_pp_objs[bsuid] = pp
else:
open_pp_objs[bsuid] = pp
return open_pp_objs, closed_pp_objs
def to_toml(
self,
) -> dict[str, Any]:
active, closed = self.dump_active()
# ONLY dict-serialize all active positions; those that are closed
# we don't store in the ``pps.toml``.
to_toml_dict = {}
for bsuid, pos in active.items():
# keep the minimal amount of clears that make up this
# position since the last net-zero state.
pos.minimize_clears()
pos.ensure_state()
# serialize to pre-toml form
fqsn, asdict = pos.to_pretoml()
log.info(f'Updating active pp: {fqsn}')
# XXX: ugh, it's cuz we push the section under
# the broker name.. maybe we need to rethink this?
brokerless_key = fqsn.removeprefix(f'{self.brokername}.')
to_toml_dict[brokerless_key] = asdict
return to_toml_dict
def write_config(self) -> None:
'''
Write the current position table to the user's ``pps.toml``.
'''
# TODO: show diff output?
# https://stackoverflow.com/questions/12956957/print-diff-of-python-dictionaries
# active, closed_pp_objs = table.dump_active()
pp_entries = self.to_toml()
if pp_entries:
log.info(
f'Updating ``pps.toml``:\n'
f'Current positions:\n{pp_entries}'
)
self.conf[self.brokername][self.acctid] = pp_entries
elif (
self.brokername in self.conf and
self.acctid in self.conf[self.brokername]
):
del self.conf[self.brokername][self.acctid]
if len(self.conf[self.brokername]) == 0:
del self.conf[self.brokername]
# TODO: why tf haven't they already done this for inline
# tables smh..
enc = PpsEncoder(preserve=True)
# table_bs_type = type(toml.TomlDecoder().get_empty_inline_table())
enc.dump_funcs[
toml.decoder.InlineTableDict
] = enc.dump_inline_table
config.write(
self.conf,
'pps',
encoder=enc,
fail_empty=False
)
def load_pps_from_ledger(
brokername: str,
acctname: str,
# post normalization filter on ledger entries to be processed
filter_by: Optional[list[dict]] = None,
) -> tuple[
dict[str, Transaction],
dict[str, Position],
]:
'''
Open a ledger file by broker name and account and read in and
process any trade records into our normalized ``Transaction`` form
and then update the equivalent ``Pptable`` and deliver the two
bsuid-mapped dict-sets of the transactions and pps.
'''
with (
open_trade_ledger(brokername, acctname) as ledger,
open_pps(brokername, acctname) as table,
):
if not ledger:
# null case, no ledger file with content
return {}
mod = get_brokermod(brokername)
src_records: dict[str, Transaction] = mod.norm_trade_records(ledger)
if filter_by:
records = {}
bsuids = set(filter_by)
for tid, r in src_records.items():
if r.bsuid in bsuids:
records[tid] = r
else:
records = src_records
updated = table.update_from_trans(records)
return records, updated
# TODO: instead see if we can hack tomli and tomli-w to do the same:
# - https://github.com/hukkin/tomli
# - https://github.com/hukkin/tomli-w
class PpsEncoder(toml.TomlEncoder):
'''
Special "styled" encoder that makes a ``pps.toml`` redable and
compact by putting `.clears` tables inline and everything else
flat-ish.
'''
separator = ','
def dump_list(self, v):
'''
Dump an inline list with a newline after every element and
with consideration for denoted inline table types.
'''
retval = "[\n"
for u in v:
if isinstance(u, toml.decoder.InlineTableDict):
out = self.dump_inline_table(u)
else:
out = str(self.dump_value(u))
retval += " " + out + "," + "\n"
retval += "]"
return retval
def dump_inline_table(self, section):
"""Preserve inline table in its compact syntax instead of expanding
into subsection.
https://github.com/toml-lang/toml#user-content-inline-table
"""
val_list = []
for k, v in section.items():
# if isinstance(v, toml.decoder.InlineTableDict):
if isinstance(v, dict):
val = self.dump_inline_table(v)
else:
val = str(self.dump_value(v))
val_list.append(k + " = " + val)
retval = "{ " + ", ".join(val_list) + " }"
return retval
def dump_sections(self, o, sup):
retstr = ""
if sup != "" and sup[-1] != ".":
sup += '.'
retdict = self._dict()
arraystr = ""
for section in o:
qsection = str(section)
value = o[section]
if not re.match(r'^[A-Za-z0-9_-]+$', section):
qsection = toml.encoder._dump_str(section)
# arrayoftables = False
if (
self.preserve
and isinstance(value, toml.decoder.InlineTableDict)
):
retstr += (
qsection
+
" = "
+
self.dump_inline_table(o[section])
+
'\n' # only on the final terminating left brace
)
# XXX: this code i'm pretty sure is just blatantly bad
# and/or wrong..
# if isinstance(o[section], list):
# for a in o[section]:
# if isinstance(a, dict):
# arrayoftables = True
# if arrayoftables:
# for a in o[section]:
# arraytabstr = "\n"
# arraystr += "[[" + sup + qsection + "]]\n"
# s, d = self.dump_sections(a, sup + qsection)
# if s:
# if s[0] == "[":
# arraytabstr += s
# else:
# arraystr += s
# while d:
# newd = self._dict()
# for dsec in d:
# s1, d1 = self.dump_sections(d[dsec], sup +
# qsection + "." +
# dsec)
# if s1:
# arraytabstr += ("[" + sup + qsection +
# "." + dsec + "]\n")
# arraytabstr += s1
# for s1 in d1:
# newd[dsec + "." + s1] = d1[s1]
# d = newd
# arraystr += arraytabstr
elif isinstance(value, dict):
retdict[qsection] = o[section]
elif o[section] is not None:
retstr += (
qsection
+
" = "
+
str(self.dump_value(o[section]))
)
# if not isinstance(value, dict):
if not isinstance(value, toml.decoder.InlineTableDict):
# inline tables should not contain newlines:
# https://toml.io/en/v1.0.0#inline-table
retstr += '\n'
else:
raise ValueError(value)
retstr += arraystr
return (retstr, retdict)
@cm
def open_pps(
brokername: str,
acctid: str,
write_on_exit: bool = False,
) -> Generator[PpTable, None, None]:
'''
Read out broker-specific position entries from
incremental update file: ``pps.toml``.
'''
conf, path = config.load('pps')
brokersection = conf.setdefault(brokername, {})
pps = brokersection.setdefault(acctid, {})
# TODO: ideally we can pass in an existing
# pps state to this right? such that we
# don't have to do a ledger reload all the
# time.. a couple ideas I can think of,
# - mirror this in some client side actor which
# does the actual ledger updates (say the paper
# engine proc if we decide to always spawn it?),
# - do diffs against updates from the ledger writer
# actor and the in-mem state here?
pp_objs = {}
table = PpTable(
brokername,
acctid,
pp_objs,
conf=conf,
)
# unmarshal/load ``pps.toml`` config entries into object form
# and update `PpTable` obj entries.
for fqsn, entry in pps.items():
bsuid = entry['bsuid']
symbol = Symbol.from_fqsn(
fqsn,
# NOTE & TODO: right now we fill in the defaults from
# `.data._source.Symbol` but eventually these should always
# either be already written to the pos table or provided at
# write time to ensure always having these values somewhere
# and thus allowing us to get our pos sizing precision
# correct!
info={
'asset_type': entry.get('asset_type', '<unknown>'),
'price_tick_size': entry.get('price_tick_size', 0.01),
'lot_tick_size': entry.get('lot_tick_size', 0.0),
}
)
# convert clears sub-tables (only in this form
# for toml re-presentation) back into a master table.
clears_list = entry['clears']
# index clears entries in "object" form by tid in a top
# level dict instead of a list (as is presented in our
# ``pps.toml``).
clears = pp_objs.setdefault(bsuid, {})
# TODO: should be make a ``Struct`` for clear/event entries?
# convert "clear events table" from the toml config (list of
# a dicts) and load it into object form for use in position
# processing of new clear events.
trans: list[Transaction] = []
for clears_table in clears_list:
tid = clears_table.pop('tid')
dtstr = clears_table['dt']
dt = pendulum.parse(dtstr)
clears_table['dt'] = dt
trans.append(Transaction(
fqsn=bsuid,
sym=symbol,
bsuid=bsuid,
tid=tid,
size=clears_table['size'],
price=clears_table['price'],
cost=clears_table['cost'],
dt=dt,
))
clears[tid] = clears_table
size = entry['size']
# TODO: remove but, handle old field name for now
ppu = entry.get(
'ppu',
entry.get('be_price', 0),
)
split_ratio = entry.get('split_ratio')
expiry = entry.get('expiry')
if expiry:
expiry = pendulum.parse(expiry)
pp = pp_objs[bsuid] = Position(
symbol,
size=size,
ppu=ppu,
split_ratio=split_ratio,
expiry=expiry,
bsuid=entry['bsuid'],
)
# XXX: super critical, we need to be sure to include
# all pps.toml clears to avoid reusing clears that were
# already included in the current incremental update
# state, since today's records may have already been
# processed!
for t in trans:
pp.add_clear(t)
# audit entries loaded from toml
pp.ensure_state()
try:
yield table
finally:
if write_on_exit:
table.write_config()