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Merge pull request #365 from pikers/ppu_history 

Ppu history
pydantic_zombie
goodboy 2022-07-27 12:25:23 -04:00 committed by GitHub
parent 927bbc7258 ae71168216
commit d81e629c29
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6 changed files with 429 additions and 428 deletions
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4
piker/brokers/ib/broker.py
View File

@ -383,7 +383,7 @@ async def update_and_audit_msgs(
symbol=ibppmsg.symbol,
currency=ibppmsg.currency,
size=p.size,
avg_price=p.be_price,
avg_price=p.ppu,
)
msgs.append(msg)
@ -430,7 +430,7 @@ async def update_and_audit_msgs(
symbol=p.symbol.front_fqsn(),
# currency=ibppmsg.currency,
size=p.size,
avg_price=p.be_price,
avg_price=p.ppu,
)
if validate and p.size:
raise ValueError(

12
piker/clearing/_allocate.py
View File

@ -126,7 +126,7 @@ class Allocator(Struct):
l_sub_pp = self.units_limit - abs_live_size
elif size_unit == 'currency':
live_cost_basis = abs_live_size * live_pp.be_price
live_cost_basis = abs_live_size * live_pp.ppu
slot_size = currency_per_slot / price
l_sub_pp = (self.currency_limit - live_cost_basis) / price
@ -158,7 +158,7 @@ class Allocator(Struct):
if size_unit == 'currency':
# compute the "projected" limit's worth of units at the
# current pp (weighted) price:
slot_size = currency_per_slot / live_pp.be_price
slot_size = currency_per_slot / live_pp.ppu
else:
slot_size = u_per_slot
@ -200,7 +200,7 @@ class Allocator(Struct):
Position(
symbol=sym,
size=order_size,
be_price=price,
ppu=price,
bsuid=sym,
)
)
@ -229,8 +229,8 @@ class Allocator(Struct):
abs_pp_size = abs(pp.size)
if self.size_unit == 'currency':
# live_currency_size = size or (abs_pp_size * pp.be_price)
live_currency_size = abs_pp_size * pp.be_price
# live_currency_size = size or (abs_pp_size * pp.ppu)
live_currency_size = abs_pp_size * pp.ppu
prop = live_currency_size / self.currency_limit
else:
@ -303,7 +303,7 @@ def mk_allocator(
# if the current position is already greater then the limit
# settings, increase the limit to the current position
if alloc.size_unit == 'currency':
startup_size = startup_pp.size * startup_pp.be_price
startup_size = startup_pp.size * startup_pp.ppu
if startup_size > alloc.currency_limit:
alloc.currency_limit = round(startup_size, ndigits=2)

119
piker/clearing/_paper_engine.py
View File

@ -22,17 +22,25 @@ from contextlib import asynccontextmanager
from datetime import datetime
from operator import itemgetter
import time
from typing import Tuple, Optional, Callable
from typing import (
Any,
Optional,
Callable,
)
import uuid
from bidict import bidict
import pendulum
import trio
import tractor
from dataclasses import dataclass
from .. import data
from ..data._source import Symbol
from ..pp import Position
from ..pp import (
Position,
Transaction,
)
from ..data._normalize import iterticks
from ..data._source import unpack_fqsn
from ..log import get_logger
@ -63,11 +71,12 @@ class PaperBoi:
_buys: bidict
_sells: bidict
_reqids: bidict
_positions: dict[str, BrokerdPosition]
_positions: dict[str, Position]
_trade_ledger: dict[str, Any]
# init edge case L1 spread
last_ask: Tuple[float, float] = (float('inf'), 0) # price, size
last_bid: Tuple[float, float] = (0, 0)
last_ask: tuple[float, float] = (float('inf'), 0) # price, size
last_bid: tuple[float, float] = (0, 0)
async def submit_limit(
self,
@ -77,20 +86,21 @@ class PaperBoi:
action: str,
size: float,
reqid: Optional[str],
) -> int:
"""Place an order and return integer request id provided by client.
'''
Place an order and return integer request id provided by client.
"""
'''
is_modify: bool = False
if reqid is None:
reqid = str(uuid.uuid4())
else:
entry = self._reqids.get(reqid)
if entry:
# order is already existing, this is a modify
(oid, symbol, action, old_price) = self._reqids[reqid]
(oid, symbol, action, old_price) = entry
assert old_price != price
is_modify = True
else:
# register order internally
self._reqids[reqid] = (oid, symbol, action, price)
@ -197,16 +207,15 @@ class PaperBoi:
"""
# TODO: net latency model
await trio.sleep(0.05)
fill_time_ns = time.time_ns()
fill_time_s = time.time()
msg = BrokerdFill(
fill_msg = BrokerdFill(
reqid=reqid,
time_ns=time.time_ns(),
time_ns=fill_time_ns,
action=action,
size=size,
price=price,
broker_time=datetime.now().timestamp(),
broker_details={
'paper_info': {
@ -216,7 +225,9 @@ class PaperBoi:
'name': self.broker + '_paper',
},
)
await self.ems_trades_stream.send(msg)
await self.ems_trades_stream.send(fill_msg)
self._trade_ledger.update(fill_msg.to_dict())
if order_complete:
@ -243,9 +254,27 @@ class PaperBoi:
# lookup any existing position
token = f'{symbol}.{self.broker}'
pp_msg = self._positions.setdefault(
pp = self._positions.setdefault(
token,
BrokerdPosition(
Position(
Symbol(key=symbol),
size=size,
ppu=price,
bsuid=symbol,
)
)
t = Transaction(
fqsn=symbol,
tid=oid,
size=size,
price=price,
cost=1., # todo cost model
dt=pendulum.from_timestamp(fill_time_s),
bsuid=symbol,
)
pp.add_clear(t)
pp_msg = BrokerdPosition(
broker=self.broker,
account='paper',
symbol=symbol,
@ -253,19 +282,9 @@ class PaperBoi:
# broker info. i guess for crypto this can be
# inferred from the pair?
currency='',
size=0.0,
avg_price=0,
size=pp.size,
avg_price=pp.ppu,
)
)
# delegate update to `.pp.Position.lifo_update()`
pp = Position(
Symbol(key=symbol),
size=pp_msg.size,
be_price=pp_msg.avg_price,
bsuid=symbol,
)
pp_msg.size, pp_msg.avg_price = pp.lifo_update(size, price)
await self.ems_trades_stream.send(pp_msg)
@ -273,6 +292,7 @@ class PaperBoi:
async def simulate_fills(
quote_stream: 'tractor.ReceiveStream', # noqa
client: PaperBoi,
) -> None:
# TODO: more machinery to better simulate real-world market things:
@ -389,6 +409,24 @@ async def handle_order_requests(
# validate
order = BrokerdOrder(**request_msg)
if order.reqid is None:
reqid = str(uuid.uuid4())
else:
reqid = order.reqid
# deliver ack that order has been submitted to broker routing
await ems_order_stream.send(
BrokerdOrderAck(
# ems order request id
oid=order.oid,
# broker specific request id
reqid=reqid,
)
)
# call our client api to submit the order
reqid = await client.submit_limit(
@ -402,20 +440,7 @@ async def handle_order_requests(
# there is no existing order so ask the client to create
# a new one (which it seems to do by allocating an int
# counter - collision prone..)
reqid=order.reqid,
)
# deliver ack that order has been submitted to broker routing
await ems_order_stream.send(
BrokerdOrderAck(
# ems order request id
oid=order.oid,
# broker specific request id
reqid=reqid,
)
)
elif action == 'cancel':
@ -468,6 +493,9 @@ async def trades_dialogue(
# TODO: load paper positions from ``positions.toml``
_positions={},
# TODO: load postions from ledger file
_trade_ledger={},
)
n.start_soon(handle_order_requests, client, ems_stream)
@ -510,5 +538,4 @@ async def open_paperboi(
loglevel=loglevel,
) as (ctx, first):
yield ctx, first

650
piker/pp.py
View File

@ -20,9 +20,8 @@ that doesn't try to cuk most humans who prefer to not lose their moneys..
(looking at you `ib` and dirt-bird friends)
'''
from collections import deque
from contextlib import contextmanager as cm
# from pprint import pformat
from pprint import pformat
import os
from os import path
from math import copysign
@ -130,7 +129,7 @@ class Position(Struct):
# "breakeven price" above or below which pnl moves above and below
# zero for the entirety of the current "trade state".
be_price: float
ppu: float
# unique backend symbol id
bsuid: str
@ -149,7 +148,7 @@ class Position(Struct):
for f in self.__struct_fields__
}
def to_pretoml(self) -> dict:
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
@ -160,23 +159,79 @@ class Position(Struct):
clears = d.pop('clears')
expiry = d.pop('expiry')
if expiry:
# 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()
size = d.pop('size')
ppu = d.pop('ppu')
d['size'], d['ppu'] = self.audit_sizing(size, ppu)
if self.expiry is None:
d.pop('expiry', None)
elif expiry:
d['expiry'] = str(expiry)
clears_list = []
toml_clears_list = []
for tid, data in sorted(
list(clears.items()),
for tid, data in clears.items():
# sort by datetime
key=lambda item: item[1]['dt'],
):
inline_table = toml.TomlDecoder().get_empty_inline_table()
inline_table['dt'] = 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)
for k, v in data.items():
inline_table[k] = v
d['clears'] = toml_clears_list
clears_list.append(inline_table)
return fqsn, d
d['clears'] = clears_list
def audit_sizing(
self,
size: Optional[float] = None,
ppu: Optional[float] = None,
return d
) -> tuple[float, float]:
'''
Audit either the `.size` and `.ppu` values or equvialent
passed in values against the clears table calculations and
return the calc-ed values if they differ and log warnings to
console.
'''
size = size or self.size
ppu = ppu or self.ppu
csize = self.calc_size()
cppu = self.calc_ppu()
if size != csize:
log.warning(f'size != calculated size: {size} != {csize}')
size = csize
if ppu != cppu:
log.warning(
f'ppu != calculated ppu: {ppu} != {cppu}'
)
ppu = cppu
return size, ppu
def update_from_msg(
self,
@ -188,7 +243,7 @@ class Position(Struct):
symbol = self.symbol
lot_size_digits = symbol.lot_size_digits
be_price, size = (
ppu, size = (
round(
msg['avg_price'],
ndigits=symbol.tick_size_digits
@ -199,7 +254,7 @@ class Position(Struct):
),
)
self.be_price = be_price
self.ppu = ppu
self.size = size
@property
@ -209,25 +264,7 @@ class Position(Struct):
terms.
'''
return self.be_price * self.size
def update(
self,
t: Transaction,
) -> None:
self.clears[t.tid] = {
'cost': t.cost,
'price': t.price,
'size': t.size,
'dt': str(t.dt),
}
def lifo_update(
self,
size: float,
price: float,
cost: float = 0,
return self.ppu * self.size
# TODO: idea: "real LIFO" dynamic positioning.
# - when a trade takes place where the pnl for
@ -237,95 +274,117 @@ class Position(Struct):
# - 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.?
# dynamic_breakeven_price: bool = False,
# def lifo_price() -> float:
# ...
) -> (float, float):
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:
'''
Incremental update using a LIFO-style weighted mean.
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.
'''
# "avg position price" calcs
# TODO: eventually it'd be nice to have a small set of routines
# to do this stuff from a sequence of cleared orders to enable
# so called "contextual positions".
new_size = self.size + size
asize_h: list[float] = [] # historical accumulative size
ppu_h: list[float] = [] # historical price-per-unit
# old size minus the new size gives us size diff with
# +ve -> increase in pp size
# -ve -> decrease in pp size
size_diff = abs(new_size) - abs(self.size)
clears = list(self.clears.items())
if new_size == 0:
self.be_price = 0
for i, (tid, entry) in enumerate(clears):
elif size_diff > 0:
# XXX: LOFI incremental update:
# only update the "average price" when
# the size increases not when it decreases (i.e. the
# position is being made smaller)
self.be_price = (
# weight of current exec = (size * price) + cost
(abs(size) * price)
+
(copysign(1, new_size) * cost) # transaction cost
+
# weight of existing be price
self.be_price * abs(self.size) # weight of previous pp
) / abs(new_size) # normalized by the new size: weighted mean.
self.size = new_size
return new_size, self.be_price
def calc_be_price(self) -> float:
size: float = 0
cb_tot_size: float = 0
cost_basis: float = 0
be_price: float = 0
for tid, entry in self.clears.items():
clear_size = entry['size']
clear_price = entry['price']
new_size = size + clear_size
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
# 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
size_diff = abs(new_size) - abs(size)
abs_diff = abs(accum_size) - abs(last_accum_size)
if new_size == 0:
cost_basis = 0
cb_tot_size = 0
be_price = 0
# 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)
elif size_diff > 0:
# only an increaze in size of the position contributes
# the breakeven price, a decrease does not.
if abs_diff > 0:
cost_basis += (
# weighted price per unit of
cost_basis = (
# cost basis for this clear
clear_price * abs(clear_size)
+
# transaction cost
(copysign(1, new_size) * entry['cost'] * 2)
accum_sign * cost_scalar * entry['cost']
)
cb_tot_size += abs(clear_size)
be_price = cost_basis / cb_tot_size
size = new_size
if asize_h:
size_last = abs(asize_h[-1])
cb_last = ppu_h[-1] * size_last
ppu = (cost_basis + cb_last) / abs_new_size
# print(
# f'cb: {cost_basis}\n'
# f'size: {size}\n'
# f'clear_size: {clear_size}\n'
# f'clear_price: {clear_price}\n\n'
else:
ppu = cost_basis / abs_new_size
# f'cb_tot_size: {cb_tot_size}\n'
# f'be_price: {be_price}\n\n'
# )
ppu_h.append(ppu)
asize_h.append(accum_size)
return be_price
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])
return ppu_h[-1] if ppu_h else 0
def calc_size(self) -> float:
size: float = 0
@ -343,24 +402,57 @@ class Position(Struct):
unecessary history irrelevant to the current pp state.
'''
size: float = self.size
clears_since_zero: deque[tuple(str, dict)] = deque()
size: float = 0
clears_since_zero: list[tuple(str, dict)] = []
# scan for the last "net zero" position by
# iterating clears in reverse.
for tid, clear in reversed(self.clears.items()):
size -= clear['size']
clears_since_zero.appendleft((tid, clear))
# 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:
break
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': str(t.dt),
}
# TODO: compute these incrementally instead
# of re-looping through each time resulting in O(n**2)
# behaviour..
# 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
class PpTable(Struct):
brokername: str
acctid: str
pps: dict[str, Position]
conf: Optional[dict] = {}
@ -372,31 +464,30 @@ class PpTable(Struct):
) -> dict[str, Position]:
pps = self.pps
updated: dict[str, Position] = {}
# lifo update all pps from records
for tid, r in trans.items():
for tid, t in trans.items():
pp = pps.setdefault(
r.bsuid,
t.bsuid,
# if no existing pp, allocate fresh one.
Position(
Symbol.from_fqsn(
r.fqsn,
t.fqsn,
info={},
),
size=0.0,
be_price=0.0,
bsuid=r.bsuid,
expiry=r.expiry,
ppu=0.0,
bsuid=t.bsuid,
expiry=t.expiry,
)
)
# don't do updates for ledger records we already have
# included in the current pps state.
if r.tid in pp.clears:
if t.tid in pp.clears:
# 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
@ -404,30 +495,20 @@ class PpTable(Struct):
# "double count" these in pp calculations.
continue
# lifo style "breakeven" price calc
pp.lifo_update(
r.size,
r.price,
# update clearing table
pp.add_clear(t)
updated[t.bsuid] = pp
# 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=cost_scalar*r.cost,
)
# track clearing data
pp.update(r)
updated[r.bsuid] = pp
# minimize clears tables and update sizing.
for bsuid, pp in updated.items():
pp.size, pp.ppu = pp.audit_sizing()
return updated
def dump_active(
self,
brokername: str,
) -> tuple[
dict[str, Any],
dict[str, Position],
dict[str, Position]
]:
'''
@ -437,13 +518,12 @@ class PpTable(Struct):
``Position``s which have recently closed.
'''
# ONLY dict-serialize all active positions; those that are closed
# we don't store in the ``pps.toml``.
# 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.
pp_entries = {}
# 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):
@ -454,7 +534,7 @@ class PpTable(Struct):
# if bsuid == qqqbsuid:
# breakpoint()
pp.minimize_clears()
pp.size, pp.ppu = pp.audit_sizing()
if (
# "net-zero" is a "closed" position
@ -470,53 +550,71 @@ class PpTable(Struct):
# 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_entries`` above is what's
# ``pps.toml`` since ``pp_active_entries`` above is what's
# written.
# closed_pp = pp_objs.pop(bsuid, None)
closed_pp = pp_objs.get(bsuid)
if closed_pp:
closed_pp_objs[bsuid] = closed_pp
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()
# serialize to pre-toml form
asdict = pp.to_pretoml()
if pp.expiry is None:
asdict.pop('expiry', None)
# 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 = asdict.pop('symbol')
fqsn = s.front_fqsn()
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'{brokername}.')
brokerless_key = fqsn.removeprefix(f'{self.brokername}.')
to_toml_dict[brokerless_key] = asdict
pp_entries[brokerless_key] = asdict
return to_toml_dict
return pp_entries, closed_pp_objs
def update_pps(
records: dict[str, Transaction],
pps: Optional[dict[str, Position]] = None
) -> dict[str, Position]:
def write_config(self) -> None:
'''
Compile a set of positions from a trades ledger.
Write the current position table to the user's ``pps.toml``.
'''
pps: dict[str, Position] = pps or {}
table = PpTable(pps)
table.update_from_trans(records)
return table.pps
# TODO: show diff output?
# https://stackoverflow.com/questions/12956957/print-diff-of-python-dictionaries
print(f'Updating ``pps.toml`` for {path}:\n')
# active, closed_pp_objs = table.dump_active()
pp_entries = self.to_toml()
self.conf[self.brokername][self.acctid] = pp_entries
# 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,
)
def load_trans_from_ledger(
def load_pps_from_ledger(
brokername: str,
acctname: str,
@ -524,24 +622,27 @@ def load_trans_from_ledger(
# post normalization filter on ledger entries to be processed
filter_by: Optional[list[dict]] = None,
) -> dict[str, Position]:
) -> 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 pass these into the position processing routine
and deliver the two dict-sets of the active and closed pps.
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:
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 {}
brokermod = get_brokermod(brokername)
src_records: dict[str, Transaction] = brokermod.norm_trade_records(ledger)
mod = get_brokermod(brokername)
src_records: dict[str, Transaction] = mod.norm_trade_records(ledger)
if filter_by:
records = {}
@ -552,7 +653,9 @@ def load_trans_from_ledger(
else:
records = src_records
return 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:
@ -686,67 +789,6 @@ class PpsEncoder(toml.TomlEncoder):
return (retstr, retdict)
def load_pps_from_toml(
brokername: str,
acctid: str,
# XXX: there is an edge case here where we may want to either audit
# the retrieved ``pps.toml`` output or reprocess it since there was
# an error on write on the last attempt to update the state file
# even though the ledger *was* updated. For this cases we allow the
# caller to pass in a symbol set they'd like to reload from the
# underlying ledger to be reprocessed in computing pps state.
reload_records: Optional[dict[str, str]] = None,
# XXX: this is "global" update from ledger flag which
# does a full refresh of pps from the available ledger.
update_from_ledger: bool = False,
) -> tuple[PpTable, dict[str, str]]:
'''
Load and marshal to objects all pps from either an existing
``pps.toml`` config, or from scratch from a ledger file when
none yet exists.
'''
with open_pps(
brokername,
acctid,
write_on_exit=False,
) as table:
pp_objs = table.pps
# no pps entry yet for this broker/account so parse any available
# ledgers to build a brand new pps state.
if not pp_objs or update_from_ledger:
trans = load_trans_from_ledger(
brokername,
acctid,
)
table.update_from_trans(trans)
# Reload symbol specific ledger entries if requested by the
# caller **AND** none exist in the current pps state table.
elif (
pp_objs and reload_records
):
# no pps entry yet for this broker/account so parse
# any available ledgers to build a pps state.
trans = load_trans_from_ledger(
brokername,
acctid,
filter_by=reload_records,
)
table.update_from_trans(trans)
if not table.pps:
log.warning(
f'No `pps.toml` values could be loaded {brokername}:{acctid}'
)
return table, table.conf
@cm
def open_pps(
brokername: str,
@ -763,8 +805,23 @@ def open_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(pp_objs, conf=conf)
table = PpTable(
brokername,
acctid,
pp_objs,
conf=conf,
)
# unmarshal/load ``pps.toml`` config entries into object form
# and update `PpTable` obj entries.
@ -789,29 +846,17 @@ def open_pps(
clears[tid] = clears_table
size = entry['size']
# TODO: an audit system for existing pps entries?
# if not len(clears) == abs(size):
# pp_objs = load_pps_from_ledger(
# brokername,
# acctid,
# filter_by=reload_records,
# )
# reason = 'size <-> len(clears) mismatch'
# raise ValueError(
# '`pps.toml` entry is invalid:\n'
# f'{fqsn}\n'
# f'{pformat(entry)}'
# )
# TODO: remove but, handle old field name for now
ppu = entry.get('ppu', entry.get('be_price', 0))
expiry = entry.get('expiry')
if expiry:
expiry = pendulum.parse(expiry)
pp_objs[bsuid] = Position(
pp = pp_objs[bsuid] = Position(
Symbol.from_fqsn(fqsn, info={}),
size=size,
be_price=entry['be_price'],
ppu=ppu,
expiry=expiry,
bsuid=entry['bsuid'],
@ -823,90 +868,14 @@ def open_pps(
clears=clears,
)
# audit entries loaded from toml
pp.size, pp.ppu = pp.audit_sizing()
try:
yield table
finally:
if write_on_exit:
# TODO: show diff output?
# https://stackoverflow.com/questions/12956957/print-diff-of-python-dictionaries
print(f'Updating ``pps.toml`` for {path}:\n')
pp_entries, closed_pp_objs = table.dump_active(brokername)
conf[brokername][acctid] = pp_entries
# 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(
conf,
'pps',
encoder=enc,
)
def update_pps_conf(
brokername: str,
acctid: str,
trade_records: Optional[dict[str, Transaction]] = None,
ledger_reload: Optional[dict[str, str]] = None,
) -> tuple[
dict[str, Position],
dict[str, Position],
]:
# 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,
# - load pps once after backend ledger state
# is loaded and keep maintainend in memory
# inside a with block,
# - 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?
if trade_records and ledger_reload:
for tid, r in trade_records.items():
ledger_reload[r.bsuid] = r.fqsn
table, conf = load_pps_from_toml(
brokername,
acctid,
reload_records=ledger_reload,
)
# update all pp objects from any (new) trade records which
# were passed in (aka incremental update case).
if trade_records:
table.update_from_trans(trade_records)
# this maps `.bsuid` values to positions
pp_entries, closed_pp_objs = table.dump_active(brokername)
pp_objs: dict[Union[str, int], Position] = table.pps
conf[brokername][acctid] = pp_entries
# 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(
conf,
'pps',
encoder=enc,
)
# deliver object form of all pps in table to caller
return pp_objs, closed_pp_objs
table.write_config()
if __name__ == '__main__':
@ -917,4 +886,9 @@ if __name__ == '__main__':
args = args[1:]
for acctid in args:
broker, name = acctid.split('.')
update_pps_conf(broker, name)
trans, updated_pps = load_pps_from_ledger(broker, name)
print(
f'Processing transactions into pps for {broker}:{acctid}\n'
f'{pformat(trans)}\n\n'
f'{pformat(updated_pps)}'
)

10
piker/ui/_position.py
View File

@ -106,8 +106,8 @@ async def update_pnl_from_feed(
# compute and display pnl status
order_mode.pane.pnl_label.format(
pnl=copysign(1, size) * pnl(
# live.be_price,
order_mode.current_pp.live_pp.be_price,
# live.ppu,
order_mode.current_pp.live_pp.ppu,
tick['price'],
),
)
@ -357,7 +357,7 @@ class SettingsPane:
# last historical close price
last = feed.shm.array[-1][['close']][0]
pnl_value = copysign(1, size) * pnl(
tracker.live_pp.be_price,
tracker.live_pp.ppu,
last,
)
@ -557,7 +557,7 @@ class PositionTracker:
pp = position or self.live_pp
self.update_line(
pp.be_price,
pp.ppu,
pp.size,
self.chart.linked.symbol.lot_size_digits,
)
@ -571,7 +571,7 @@ class PositionTracker:
self.hide()
else:
self._level_marker.level = pp.be_price
self._level_marker.level = pp.ppu
# these updates are critical to avoid lag on view/scene changes
self._level_marker.update() # trigger paint

2
piker/ui/order_mode.py
View File

@ -610,7 +610,7 @@ async def open_order_mode(
startup_pp = Position(
symbol=symbol,
size=0,
be_price=0,
ppu=0,
# XXX: BLEH, do we care about this on the client side?
bsuid=symbol,