* 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
TradingAlgorithm.run didn't support Panel minute bar data, and assumed
all Panel data was daily.
To rectify this, adding PanelMinuteBarReader class.
TradingAlgorithm.run decides whether to use it or PanelDailyBarReader
by assuming data is daily if and only if the time of day of every
Timestamp is identical.
AlgorithmSimulator will no longer check for capital changes.
Instead, TradingAlgorithm find and calculate the changes, and
PerformanceTracker will apply the changes
dmichalowicz