If a backend declares a top level `get_cost()` (provisional name)
we call it in the paper engine to try and simulate costs according to
the provider's own schedule. For now only `binance` has support (via the
ep def) but ideally we can fill these in incrementally as users start
forward testing on multiple cexes.
Since it's depended on by `.data` stuff as well as pretty much
everything else, makes more sense to expose it as a top level module
(and maybe eventually as a subpkg as we add to it).
In order to attempt giving the user a realistic prediction for a BEP per
txn we need to model what the (worst case) anticipated exit txn costs
will be during the equivalent, paired entries. For now we use a simple
"symmetric cost prediction" model where we assume the exit costs will be
simply the same as the enter txn costs and thus on every entry we apply
2x the enter txn cost; on exit txns we then unroll these predictions by
keeping a cumulative sum of the cost-per-unit and reversing the charges
based on applying that mean to the current exit txn's size. Once
unrolled we apply the actual exit txn cost received from the
broker-provider.
Since it appears impossible to compute the recurrence relations for PPU
(at least sanely) without using embedded `polars.List` elements, this
instead just implements price-per-unit and break-even-price calcs
doing a plain-ol-for-loop imperative approach with logic branching.
I burned wayy too much time trying to implement this in some kinda
`polars` DF native way without luck, so hopefuly someone smarter can
come in and make it work at some point xD
Resolves a related bullet in #515
Took a little while to get right using declarative style but it's
finally workin and seems (mostly correct B)
Computes the ppu (price per unit) using the PnL since last
net-zero-cumsize (aka the pnl from open to close) and uses it to calc
the pnl-per-exit trade (using the ppu).
Next up, bep (break even price both) per position and maybe since
ledger start or an arbitrary ref point?
Since some backends are going to have the issue of supporting multiple
venues for a given "position distinguishing instrument", like IB, we
can't presume that every `Position` can be uniquely keyed by
a `MktPair.fqme` (since the venue part can change and still be the same
"pair" relationship in accounting terms) so instead presume the
"backend system's market id" is the unique key (at least for now)
instead of the fqme.
More practically we use the `bs_mktid` to groupby-partition the per
pair DFs from the trades ledger and attempt to scan-match the input
fqme (in `ledger disect` cli) against the fqme column values set.
Not sure why i ever thought it would work otherwise but, obviously if
you're replicating a `Position` from a **summary** (IPC) msg we
need to wipe any prior clearing events from the events history..
The main use for this loading mechanism is precisely if you don't have
local access to the txn ledger and need to represent a position from
a summary 🤦
Also, never bother with ledger file fqme "rewriting" if the backend has
no symcache support (yet) since obviously there's then no symbol set to
search for a better key xD
- start flipping over internals to `Position.cumsize`
- allow passing in a `_mktmap_table` to `Account.update_from_ledger()`
for cases where the caller wants to per-call-dyamically insert the
`MktPair` via a one-off table (cough IB).
- use `polars.from_dicts()` in `.calc.open_ledger_dfs()`. and wrap the
whole func in a new `toolz.open_crash_handler()`.
Since apparently rendering to dict from a sorted generator func clearly
doesn't preserve the order when using a `dict`-comprehension.. Further,
there's really no reason to strictly return a `dict`. Adjust
`.calc.ppu()` to make the return value instead a `list[tuple[str,
dict]]`; this results in the current df cumsum values matching the
original impl and the existing `binance.paper` unit tests now passing XD
Other details that fix a variety of nonsense..
- adjust all `.clearsitems()` consumers to the new list output.
- use `str(pendulum.now())` in `Position.from_msg()` since adding
multiples with an `unknown` str will obviously discard them, facepalm.
- fix `.calc.ppu()` to NOT short circuit when `accum_size` is 0; it's
been causing all sorts of incorrect size outputs in the clearing
table.. lel, this is what fixed the unit test!
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`.
For testing (and probably hacking) it's handy to be able to point
somewhere other the default user-config dir for a ledger or account file
to test offline processing apis from `.accounting` subsystems. For now
it's a private optional named-arg: `_fp: Path` and it's obviously passed
down into the `load_account()` config getter.
Note that in the non-paper account case `Account.update_from_ledger()`
will use the ledger's `.symcache` and `.iter_txns()` method to acquite
actual txn-structs to compute positions.
Since each broker backend generally needs to define a specific
field-name-schema to determine the exact instantiation arguments to
`Transaction`, we generally need each backend to define an endpoint
function to conduct this transaction from an input `dict[str, Any]`
received either directly from provided ledger APIs or from previously
stored `.accounting._ledger` saved trades ledger TOML files.
To accomplish this we now require backends to declare a new routine:
```python
def norm_trade(
tid: str, # the uuid for the transaction
txdict: dict, # the input record-dict
# a table of mkt-symbols to backend
# struct objects which define the (meta-data) for the backend specific
# venue's symbology
pairs: dict[str, Struct],
) -> Transaction:
...
```
which implements that record conversion (at least for trades)
and can thus be used in `TransactionLedger.iter_txns()` which requires
"some code" to implement the loading from a serialization format (aka
the input `dict` record) to our local `Transaction` struct, normally
also using a `Pair`-struct table defined (and maybe previously cached)
by the specific backend such our (normalization layer's) `MktPair`'s
fields can be set.
For the case of our `.clearing._paper_engine` we def the routine to
simply extract the exact same fields from the TOML ledger records that
we previously had written (to it) and define it in that module.
Also, we always pass `pairs=SymbologyCache.pairs: dict[str, Struct]` on
norm trade calls such that offline ledger and accounting processing
clients can use a previously cached symbology set without having to
necessarily start the async-actor runtime to query the actual backend API
if the data has already been saved locally on the system B)
Other related:
- always passthrough kwargs in overridden `.to_dict()` method.
- only do fqme related trade record field name rewrites/names when
operating on a paper ledger; normally a backend's records don't
contain these.
- fix `pendulum.DateTime` type annots.
- just deliver `Transaction`s from `.iter_txns()`
Require passing an explicit flag when entering from sync code with an
extra super duper explicit runtime error to indicate how to use in the
async case as well!
Also, do rewrites of both the fqme (from best match in the symcache
according to search - the worst case) or from the `bs_mktid` field if
it exists (should only be true for paper engine accounts) AND the
`bs_mktid` for paper accounts if it seems un-fully-qualified.
Drop all the old `polars` (groupby + agg related) mangling to get a df
per fqme by delegating to the new routine and add in the `.cumsum()`ing
(per frame) as a first start on computing pps using dfs instead of
python dicts + loops as in `ppu()`.
To isolate it from the ledger/account mods and bc it is actually for
doing (eventual) position calcs / anal, might as well put it in this
mod. Add in the old-masked `ensure_state()` method content in case we
want to use it later for testing. Also tighten up the parser loading
inside `dyn_parse_to_dt()`.
Rename `open_pps()` -> `open_account()` for obvious reasons as well as
expect a bit tighter integration with `SymbologyCache` and consequently
`LedgerTransaction` in order to drop `Transaction.sym: MktPair`
dependence when compiling / allocating new `Position`s from a ledger.
Also we drop a bunch of prior attrs and do some cleaning,
- `Position.first_clear_dt` we no longer sort during insert.
- `._clears` now replaces by `._events` table.
- drop the now masked `.ensure_state()` method (eventually moved to
`.calc` submod for maybe-later-use).
- drop `.sym=` from all remaining txns init calls.
- clean out the `Position.add_clear()` method and only add the provided
txn directly to the `._events` table.
Improve some `Account` docs and interface:
- fill out the main type descr.
- add the backend broker modules as `Account.mod` allowing to drop
`.brokername` as input and instead wrap as a `@property`.
- make `.update_from_trans()` now a new `.update_from_ledger()` and
expect either of a `TransactionLedger` (user-dict) or a dict of txns;
in the latter case if we have not been also passed a symcache as input
then runtime error since the symcache is necessary to allocate
positions.
- also, delegate to `TransactionLedger.iter_txns()` instead of
a manual datetime sorted iter-loop.
- drop all the clears datetime don't-insert-if-earlier-then-first
logic.
- rename `.to_toml()` -> `.prep_toml()`.
- drop old `PpTable` alias.
- rename `load_pps_from_ledger()` -> `load_account_from_ledger()` and
make it only deliver the account instance and also move out all the
`polars.DataFrame` related stuff (to `.calc`).
And tweak some account clears table formatting,
- store datetimes as TOML native equivs.
- drop `be_price` fixing.
- obvsly drop `.ensure_state()` call to pps.
Turns out we don't really need it directly for most "txn processing" AND
if we do it's usually related to some `Account`-ing related calcs; which
means we can instead just rely on the new `SymbologyCache` lookup to get
it when needed. So, basically just get rid of it and rely instead on the
`.fqme` to be the god-key to getting `MktPair` info (from the cache).
Further, extend the `TransactionLedger` to contain much more info on the
pertaining backend:
- `.mod` mapping to the (pkg) py mod.
- `.filepath` pointing to the actual ledger TOML file.
- `_symcache` for doing any needed asset or mkt lookup as mentioned
above.
- rename `.iter_trans()` -> `.iter_txns()` and allow passing in
a symcache or using the init provided one.
- rename `.to_trans()` similarly.
- delegate paper account txn processing to the `.clearing._paper_engine`
mod's `norm_trade()` (and expect this similarly from other backends!)
- use new `SymbologyCache.search()` to find the best but
un-fully-qualified fqme for a given `txdict` being processed when
writing a config (aka always try to expand to the most verbose `.fqme`
possible).
- add a `rewrite: bool` control to `open_trade_ledger()`.
Previously we weren't necessarily serializing mkt pairs (for IPC msging)
entirely bc the assets `.src/.dst` were being sent just by their
str-names. This now properly supports fully serializing `Asset`s as
`dict`-msgs such that use of `MktPair.to_dict()` can be transmitted over
`tractor.MsgStream`s and deserialized entirely back to struct from on
the receiver end.
Deats:
- implement `Asset.to_dict()` and `.from_msg()`
- adjust `MktPair.to_dict()` and `.from_msg()` to use these methods.
- drop all the hacky "if .src/.dst is str" handling.
- add better `MktPair.from_fqme()` input handling for expiry and venue;
ensure that either can be extracted from passed fqme *and* if so they
are also popped from any duplicate passed in `**kwargs**`.
We're probably going to move to implementing all accounting using
`polars.DataFrame` and friends and thus this rejig preps for a much more
"stateless" implementation of our `Position` type and its internal
pos-accounting metrics: `ppu` and `cumsize`.
Summary:
- wrt to `._pos.Position`:
- rename `.size`/`.accum_size` to `.cumsize` to be more in line
with `polars.DataFrame.cumsum()`.
- make `Position.expiry` delegate to the underlying `.mkt: MktPair`
handling (hopefully) all edge cases..
- change over to a new `._events: dict[str, Transaction]` in prep
for #510 (and friends) and enforce a new `Transaction.etype: str`
which is by default `clear`.
- add `.iter_by_type()` which iterates, filters and sorts the
entries in `._events` from above.
- add `Position.clearsdict()` which returns the dict-ified and
datetime-sorted table which can more-or-less be stored in the
toml account file.
- add `.minimized_clears()` a new (and close) version of the old
method which always grabs at least one clear before
a position-side-polarity-change.
- mask-drop `.ensure_state()` since there is no more `.size`/`.price`
state vars (per say) as we always re-calc the ppu and cumsize from
the clears records on every read.
- `.add_clear` no longer does bisec insorting since all sorting is
done on position properties *reads*.
- move the PPU (price per unit) calculator to a new `.accounting.calcs`
as well as add in the `iter_by_dt()` clearing transaction sorted
iterator.
- also make some fixes to this to handle both lists of `Transaction`
as well as `dict`s as before.
- start rename of `PpTable` -> `Account` and make a note about adding
a `.balances` table.
- always `float()` the transaction size/price values since it seems if
they get processed as `tomlkit.Integer` there's some suuper weird
double negative on read-then-write to the clears table?
- something like `cumsize = -1` -> `cumsize = --1` !?!?
- make `load_pps_from_ledger()` work again but now includes some very
very first draft `polars` df processing from a transaction ledger.
- use this from the `accounting.cli.disect` subcmd which is also in
*super early draft* mode ;)
- obviously as mentioned in the `Position` section, add the new `.calcs`
module with a `.ppu()` calculator func B)
No point having duplicate data when we already stash the `expiry` on the
mkt info type and can just read it (and cast to `datetime` obj).
Further this fixes a regression caused by converting `._clears` to
a list by adding a `._events: dict[str, Transaction]` which prevents
double entering transactions based on checking the events table for the
existing id.. Further add a sanity check that all events are popped
(for now) after serializing the clearing table for the toml account
file.
In the longer run, ideally we don't have the separate sequences ._clears
and ._events by choosing a better data structure (sorted unique set of
mkt events) maybe a specially used `polars.DataFrame` (which we kind
need eventually anyway)?
When you look at usage we don't end up really needing clear entries to
be keyed by their `Transaction.tid`, instead it's much more important to
ensure the time sorted order of trade-clearing transactions such that
position properties such as the size and ppu are calculated correctly.
Thus, this instead simplified the `.clears` table to a list of clear
dict entries making a bunch of things simpler:
- object form `Position._clears` compared to the offline TOML schema
(saved in account files) is now data-structure-symmetrical.
- `Position.add_clear()` now uses `bisect.insort()` to
datetime-field-sort-insert into the *list* which saves having to worry
about sorting on every sequence *read*.
Further deats:
- adjust `.accounting._ledger.iter_by_dt()` to expect an input `list`.
- change `Position.iter_clears()` to iterate only the clearing entry
dicts without yielding a key/tid; no more tuples.
- drop `Position.to_dict()` since parent `Struct` already implements it.
Like you'd think:
- `load_ledger()` -> ._ledger
- `load_accounrt()` -> ._pos
Also fixup the old `load_pps_from_ledger()` and expose it from a new
`.accounting.cli.disect` cli cmd for trying to figure out why pp calcs
are totally mucked on stupid ib..
We might as well start standardizing on `brokerd` init such that it can
be used more generally in client code (such as the `.accounting.cli`
stuff).
Deats of `broker_init()` impl:
- loads appropriate py pkg module,
- reads any declared `__enable_modules__: listr[str]` which will be
passed to `tractor.ActorNursery.start_actor(enabled_modules=<this>)`
- loads the `.brokers._daemon._setup_persistent_brokerd
As expected the `accounting.cli` tools can now import directly from this
new location and use the common daemon fixture definition.
For crypto derivatives (at least futes), yes they are margined, but
generally not around a single unit of vlm (like equities or commodities
futes) so don't pre-set the order mode allocator to use a #unit limit,
$limit is fine.
Tyler Goodlet