as it is a backwards compat. Without sorting the orders are filled by
order date. With sorting the orders are moved to back of queue after
partil fills. If all orders are fully filled, there is no
deviation. Also there is no portfolio difference as this is about
assigning fills to equivalent orders.
For use in a function that wraps de-serialization, to call instead of
creating the OrderedDicts from a module outside of the object.
So that the other module does not need to the internals of this object,
also to ensure that the cythonized OrderedDict is used, when available.
This need should be superseded with serialization versioning.
When calculate_positions_value used np.dot, the return type was a
np.float64. Which allows the use of 0.0 in division to not raise an
exception.
Fix by expliciting creating an np.float64 with 0 value.
Add a set_positions method so that the serialization process can rebuild
from just the positions, since the last_sale and amounts are derivable
from those values.
Also, use the private naming convention for last sale price and amount
members, so that those members are ignored by the serialization process.
based OrderedDict. Series/ndarray can only be sped up so much because
they weren't designed for fast iterative mutations.
This also cut down on the # of intermediate Series being generated
during perf stat generation. Things like s[s > 0] will create a new
Series for s > 0.
Moved cython to requirements.txt and added cyordereddict
Alleviates bottleneck caused re-indexing into a pd.Series during a tight
loop, by keeping track of the index value into the underlying `.values`
in a lookup table.
Based on suggestion from @dalejung
Risk calculations are robust to nans, except for
beta which calls numpy with the complete list of
algorithm_returns. If nans are present the result
of covar will be nan.
This is fixed by filtering out nans in
algorithm_returns.
Instead of checking the positions indexes every time either
`_position_amounts` or `_position_last_sale_prices` is updated, check
and grow the individual Series on each update.
This gain with this patch is by reducing the following bottlenecks:
- Checking both vectors when only one is updated.
- Using try/except to trigger the growth, instead of incurring the cost
of checking the Index contains on every update.
In testing this change results in about a 33% speedup of the
`update_last_sale` algorithm when run with a buy and hold algorithm with
160 equities, resulting in a 20% speedup overall.
Previously, all specs had to be pre-allocated by using the 'add_history'
function. This is now no longer required and instead serves as a hint to
the HistoryContainer to pre-allocate the space for the given spec.
History can grow by increasing the length for a frequency, adding a
frequency, or adding a field. It can grow with any combination of
these.
HistoryContainer now is aware of the data_frequency of the algorithm,
and no longer uses the daily_at_midnight flag; instead, this is the
default behavior.
schedule_function takes a date rule, a time rule, and a function and
will call the function, passing context and data only when the two rules
fire. This allows for code that is conditional to the datetime of the
algo.
This is implemented internally with `Event` objects which are pairings
of `EventRule`s and callbacks.
handle_data becomes a special event with a rule that always fires. This
makes the logic for handling events more complete and compact.
This commit adds support for arbitrary objects in addition to NaN
and infinity values. The object well be returned in string format
as part of the error message.
Previously order was not checking for nan values sent as
limit or stop prices. It will now raise a runtime exception
in the event that an attempt to order with a nan price is made.
Previously the last sale price was not correctly being set on
positions when the transaction arrived before the trade event.
The last sale price was defaulted to zero and never updated. This resulted
in one holding stocks that were bough >>0 and now had value 0 from
the perspective of returns. The returns would display correctly again
when the next trade of that security happened. For most securities trading is
frequent enough that there's no issue, but for some illiquid ones it took
hours to fix itself.
Updated test_perf_tracking:TestPerformanceTracker.test_minute_tracker
This test was based on assuming that last_sale_price was zero,
allowing the sharpe ratio to be calculated. The sharpe ratio can no longer
be calculated for this specific tested scenario and the test has been changed
accordingly.
Delaney Granizo-Mackenzie