Move the responsibility of wrapping the main simulation loop in the
ZiplineAPI context from the algorithm modules generator setup to the
main trade simulation loop, so that different methods of invoking loop
do not need to duplicate how the context is set.
To make it easier for internal implementation of handle_data to
transition off of calling the ZiplineAPI every bar, to only invoking the
context once per simulation.
Instead of using the pandas.Series datetime index for every single
vector, get the index at the beginning of the update loop based on the
dt and then use that index to set the values.
Also, since the dt lookup is no longer needed, store the values as numpy
arrays, which are more lightweight.
Locally, this patch cuts out about 60% of the time spent in the update
method.
- AssetFinder no longer accepts an unused trading_calendar.
- AssetFinder correctly accepts a DataFrame as input.
- Tests for AssetFinder no longer rely on a global trading environment.
Use explicit references to the performance tracker instead of the proxy
lookup.
Mainly a putative change, which surfaced when reasoning about places
where the position tracker and period need access to the last sale
price.
This is to fix the issue where 'GOOG_L's metadata lists its symbol as 'GOOG'. To resolve this, the 'file_name' is used as the canonical symbol, if it is given.
np.allclose creates a vector of size 1 if passed a scalar, which showed
up as a bottleneck in algorithms with a large number of
order_target_percent calls.
In d01e718373, we made a change for
compatibility with a change introduced in pip 7.0.0. Then the pip
maintainers went and reverted the change in pip 7.0.2, thereby
breaking our requirements file for the second time in ten days. This
change reverts the prior change so that we are once again compatible
with the current version of pip.
Also, to make it easier for people to avoid pip compatibility issues,
we're now specifying pip>=7.0.3 in etc/requirements.txt.
Test sources are now defined by the sim_params period_start and period_end, rather than by the period_start and a defined 'count' of bars. This allows us to consider the sim_params.period_end as the canonical definition of the end of a simulation.
Referencing context.account in handle_data() can block updates to account,
portfolio and performance metrics, which can cause unintended changes in
financial performance. Additionally, since context.account metrics are not
updating frequenctly enough (daily, rather than minutely) algos which base
decisions on these metrics can have undesired behavior.
To address this we do not base our decision to update performance on the
state of account_needs_update.
When we call get_account() from the market and minute close methods, we
set the performance_needs_update parameter to False since we just updated
performance.
_get_account() is renamed to _update_account() since the method does not
return account information.
Also remove redundant asset_needs_update = True lines.
Eddie Hebert