Move cum-calcs to `open_ledger_dfs()`, always parse `str`->`Datetime`
Previously the cum-size calc(s) was in the `disect` CLI but it's better stuffed into the backing df converter. Also, ensure that whenever a `dt` field is type-detected as a `str` we parse it to `DateTime`.account_tests
Tyler Goodlet
parent
69314e9fca
commit
3d20490ee5
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@ -20,6 +20,7 @@ you know when you're losing money (if possible) XD
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'''
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from __future__ import annotations
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from collections.abc import ValuesView
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from contextlib import contextmanager as cm
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from math import copysign
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from typing import (
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@ -39,6 +40,7 @@ from pendulum import (
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if TYPE_CHECKING:
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from ._ledger import (
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Transaction,
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TransactionLedger,
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)
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def ppu(
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@ -72,18 +74,23 @@ def ppu(
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where `cost_basis` for the current step is simply the price
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* size of the most recent clearing transaction.
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-----
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TODO: get the BEP computed and working similarly!
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-----
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the equivalent "break even price" or bep at each new clear
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event step conversely only changes when an "position exiting
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clear" which **decreases** the cumulative dst asset size:
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bep[-1] = ppu[-1] - (cum_pnl[-1] / cumsize[-1])
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'''
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asize_h: list[float] = [] # historical accumulative size
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ppu_h: list[float] = [] # historical price-per-unit
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ledger: dict[str, dict] = {}
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# entry: dict[str, Any] | Transaction
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t: Transaction
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for t in clears:
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# tid: str = entry['tid']
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# clear_size = entry['size']
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clear_size: float = t.size
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# clear_price: str | float = entry['price']
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clear_price: str | float = t.price
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is_clear: bool = not isinstance(clear_price, str)
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@ -152,7 +159,6 @@ def ppu(
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clear_price * abs(clear_size)
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+
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# transaction cost
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# accum_sign * cost_scalar * entry['cost']
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accum_sign * cost_scalar * t.cost
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)
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@ -187,13 +193,13 @@ def ppu(
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asize_h.append(accum_size)
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# ledger[t.tid] = {
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# 'tx': t,
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# 'txn': t,
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ledger[t.tid] = t.to_dict() | {
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'ppu': ppu,
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'cumsize': accum_size,
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'sign_change': sign_change,
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# TODO: cumpnl, bep
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# TODO: cum_pnl, bep
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}
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final_ppu = ppu_h[-1] if ppu_h else 0
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@ -212,6 +218,7 @@ def ppu(
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def iter_by_dt(
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records: (
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dict[str, dict[str, Any]]
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| ValuesView[dict] # eg. `Position._events.values()`
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| list[dict]
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| list[Transaction] # XXX preferred!
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),
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@ -220,7 +227,7 @@ def iter_by_dt(
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# so if you know that the record stats show some field
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# is more common then others, stick it at the top B)
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parsers: dict[str, Callable | None] = {
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'dt': None, # parity case
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'dt': parse, # parity case
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'datetime': parse, # datetime-str
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'time': from_timestamp, # float epoch
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},
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@ -259,8 +266,13 @@ def iter_by_dt(
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# the `parsers` table above (when NOT using
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# `.get()`), otherwise pass through the value and
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# sort on it directly
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parser: Callable | None = parsers[k]
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return parser(v) if (parser is not None) else v
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if (
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not isinstance(v, DateTime)
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and (parser := parsers.get(k))
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):
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return parser(v)
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else:
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return v
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else:
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# XXX: should never get here..
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@ -271,6 +283,7 @@ def iter_by_dt(
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records,
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key=key or dyn_parse_to_dt,
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):
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# NOTE the type sig above; either pairs or txns B)
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yield entry
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@ -331,7 +344,14 @@ def open_ledger_dfs(
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brokername: str,
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acctname: str,
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) -> dict[str, pl.DataFrame]:
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ledger: TransactionLedger | None = None,
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**kwargs,
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) -> tuple[
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dict[str, pl.DataFrame],
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TransactionLedger,
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]:
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'''
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Open a ledger of trade records (presumably from some broker
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backend), normalize the records into `Transactions` via the
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@ -341,86 +361,89 @@ def open_ledger_dfs(
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'''
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from ._ledger import (
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open_trade_ledger,
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# Transaction,
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TransactionLedger,
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)
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ledger: TransactionLedger
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import time
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now = time.time()
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with (
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open_trade_ledger(
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brokername,
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acctname,
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rewrite=True,
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allow_from_sync_code=True,
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) as ledger,
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):
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if not ledger:
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raise ValueError(f'No ledger for {acctname}@{brokername} exists?')
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if not ledger:
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import time
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from tractor._debug import open_crash_handler
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now = time.time()
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print(f'LEDGER LOAD TIME: {time.time() - now}')
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# process raw TOML ledger into txns using the
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# appropriate backend normalizer.
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# cache: AssetsInfo = get_symcache(
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# brokername,
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# allow_reload=True,
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# )
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with (
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open_crash_handler(),
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txns: dict[str, Transaction]
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if acctname != 'paper':
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txns = ledger.mod.norm_trade_records(ledger)
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else:
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txns = ledger.to_txns()
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open_trade_ledger(
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brokername,
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acctname,
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rewrite=True,
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allow_from_sync_code=True,
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ldf = pl.DataFrame(
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list(txn.to_dict() for txn in txns.values()),
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# schema=[
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# ('tid', str),
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# ('fqme', str),
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# ('dt', str),
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# ('size', pl.Float64),
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# ('price', pl.Float64),
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# ('cost', pl.Float64),
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# ('expiry', str),
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# ('bs_mktid', str),
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# ],
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).sort('dt').select([
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pl.col('fqme'),
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pl.col('dt').str.to_datetime(),
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# pl.col('expiry').dt.datetime(),
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pl.col('bs_mktid'),
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pl.col('size'),
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pl.col('price'),
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# proxied through from caller
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**kwargs,
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) as ledger,
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):
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if not ledger:
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raise ValueError(f'No ledger for {acctname}@{brokername} exists?')
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print(f'LEDGER LOAD TIME: {time.time() - now}')
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# process raw TOML ledger into txns using the
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# appropriate backend normalizer.
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# cache: AssetsInfo = get_symcache(
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# brokername,
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# allow_reload=True,
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# )
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txns: dict[str, Transaction] = ledger.to_txns()
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ldf = pl.DataFrame(
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list(txn.to_dict() for txn in txns.values()),
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# schema=[
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# ('tid', str),
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# ('fqme', str),
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# ('dt', str),
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# ('size', pl.Float64),
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# ('price', pl.Float64),
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# ('cost', pl.Float64),
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# ('expiry', str),
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# ('bs_mktid', str),
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# ],
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# ).sort('dt').select([
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).sort('dt').with_columns([
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# pl.col('fqme'),
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pl.col('dt').str.to_datetime(),
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# pl.col('expiry').dt.datetime(),
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# pl.col('bs_mktid'),
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# pl.col('size'),
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# pl.col('price'),
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])
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# filter out to the columns matching values filter passed
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# as input.
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# if filter_by_ids:
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# for col, vals in filter_by_ids.items():
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# str_vals = set(map(str, vals))
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# pred: pl.Expr = pl.col(col).eq(str_vals.pop())
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# for val in str_vals:
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# pred |= pl.col(col).eq(val)
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# fdf = df.filter(pred)
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# bs_mktid: str = fdf[0]['bs_mktid']
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# pos: Position = acnt.pps[bs_mktid]
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# TODO: not sure if this is even possible but..
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# ppt = df.groupby('fqme').agg([
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# # TODO: ppu and bep !!
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# pl.cumsum('size').alias('cumsum'),
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# ])
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dfs: dict[str, pl.DataFrame] = ldf.partition_by(
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'fqme',
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as_dict=True,
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)
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for key in dfs:
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df = dfs[key]
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dfs[key] = df.with_columns([
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pl.cumsum('size').alias('cumsize'),
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])
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# filter out to the columns matching values filter passed
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# as input.
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# if filter_by_ids:
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# for col, vals in filter_by_ids.items():
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# str_vals = set(map(str, vals))
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# pred: pl.Expr = pl.col(col).eq(str_vals.pop())
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# for val in str_vals:
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# pred |= pl.col(col).eq(val)
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# fdf = df.filter(pred)
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# bs_mktid: str = fdf[0]['bs_mktid']
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# pos: Position = acnt.pps[bs_mktid]
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# ppt = df.groupby('fqme').agg([
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# # TODO: ppu and bep !!
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# pl.cumsum('size').alias('cumsum'),
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# ])
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dfs: dict[str, pl.DataFrame] = ldf.partition_by(
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'fqme',
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as_dict=True,
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)
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# for fqme, ppt in act.items():
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# ppt.with_columns
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# # TODO: ppu and bep !!
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# pl.cumsum('size').alias('cumsum'),
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# ])
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yield dfs
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yield dfs, ledger
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@ -37,8 +37,8 @@ from ..calc import humanize
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from ..brokers._daemon import broker_init
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from ._ledger import (
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load_ledger,
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TransactionLedger,
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# open_trade_ledger,
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# TransactionLedger,
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)
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from .calc import (
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open_ledger_dfs,
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@ -263,20 +263,17 @@ def disect(
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# ledger dfs groupby-partitioned by fqme
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dfs: dict[str, pl.DataFrame]
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# actual ledger ref filled in with all txns
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ldgr: TransactionLedger
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with open_ledger_dfs(
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brokername,
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account,
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) as dfs:
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) as (dfs, ldgr):
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for key in dfs:
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df = dfs[key]
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dfs[key] = df.with_columns([
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pl.cumsum('size').alias('cumsum'),
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])
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ppt = dfs[fqme]
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# look up specific frame for fqme-selected asset
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df = dfs[fqme]
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assert not df.is_empty()
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assert not ppt.is_empty()
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# TODO: we REALLY need a better console REPL for this
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# kinda thing..
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