MAINT: Remove environment as an argument to benchmark source.
To allow the BenchmarkSource class to be more easily used in contexts other than
a TradingAlgorithm, remove the TradingEnvironment as an argument to the
benchmark source.
Instead:
- Pass a benchmark Asset, instead of a bencmark sid; so that the asset_finder
does not need to be passed to the benchmark source.
- Pass the pre-calculated benchmark_returns instead of an env,
which contains the benchmark_returns; a consumer can let the benchmark_returns
stay as the default of `None` when using an asset.
We may want to further refactor and make two different classes, instead of
relying on a combination of existence/non-existence of benchmark_asset and
benchmark_returns. That refactoring should be easier to do with this change.
The only downstream contex that was using batch_order_target_percent
already had all necessary prices, so calling batch_order_target_percent
was wasteful.
This allows us to remove the check for whether the provided dt had a
time of midnight, which was a flimsy way to infer if the data frequency
was 'daily'. Besides the explicit check being preferable, this method
was broken on the futures calendar, since midnight is a valid market
minute.
* ENH: Use regular rounding to calculate order amounts.
We previously tried to prevent accidental over-ordering by truncating
orders down unless they were within 1e-4 of the next higher integer.
Unfortunately, this makes it easy for a sell order to be one share short
of the desired position.
Using regular rounding treats both buys and sells in the same way.
* ENH keep non-rounding behavior consistent, but leave code structured to make easier to override
* DOC make round_order public and describe behavior in docstring
Capital base is included in the sim params, so we should define the
value there, or use the default.
This change also unifies the default capital base as 1e5, as was
previously defined in algorithm.py.
Any DataFrame that's had `.loc` or `.iloc `called on it participates in
a cycle, which means they're not immediately garbage collected when they
go out of scope. This matters for pipeline results because they consume
multiple megabytes per column, which means that a pipeline result with
many columns can hold take up over 100MB. By manually breaking
DataFrame cycles, we can ensure that we never hold multiple pipeline
results in memory at once.
Add the ability for an algorithm to request the current contract for a
future chain via `data.current`.
e.g.:
```
data.current(ContinuousFuture('CL', offset=0, roll='calendar'),
'contract')
```
In favor of a new method `set_restrictions` which takes a Restrictions
object. Calls to `set_do_not_order_list` should raise a deprecation
warning and create an equivalent Restrictions object, with which
`set_restrictions` will be called. For convenience, create a
RestrictionsSet from which the "restrictions" version of a security
list can be accessed
The SecurityList implements a non-exposed method
`current_securities(dt)` which SecurityListRestrictions calls to
determine if an asset is restricted. Deprecate the `__iter__` and
`__contains__` methods of security lists in favor of
`current_securities(dt)`
`future_chain` will be replaced by the as yet to be implemented method,
`data.current_chain`
Also removing `FutureChain` which will be replaced by another version
which only supports indexing and iteration.
- Fixes a warning on indexing with a float that ultimately came from
pd.Timedelta.total_seconds(). Adds ``timedelta_to_integral_seconds``
and ``timedelta_to_integral_minutes()`` functions and replaces various
usages of ``int(delta.total_seconds())`` with them.
- Fixes a warnings triggered in ``_create_daily_stats`` from
passing tz-aware datetimes to np.datetime64.
This reverts commit 5b1aa5ec55.
The paradigm is: we're calculating a new capital base for the
performance period. We are therefore using the total
portfolio_value, not just the cash, to calculate the
difference from the specified target as the algorithm
has meaningful holdings.
If subclasses have additional capital change information that
is required to correctly calculate the target values for
cash capital changes, it can now be provided via
"portfolio_value_adjustment".
No longer auto-updates its internal as-of date, instead requires an explicit
as-of date from the consumer.
Take a static list of contracts (instead of needing an assetfinder).
Instead of the as_of method, the user-facing API now lets you pass in an
offset, which is defined as an integral number of sessions.
Previously, on the dt of a capital change, we use the un-updated
prices to find the ending performance of the previous subperiod and
then got the new prices to determine the portfolio value used to
calculate the delta, without actually updating the performance
before applying the capital change. This logic is confusing and
unintuitive. Instead, save the ending performance as we do previously,
but have temp values for the starting current subperiod value.
Update those temp values after processing the capital change
Eddie Hebert