`period_end` can be outside the range of data for which we have dates.
`last_close` properly gets pulled back to the last date for which we
actually have data.
We should consider whether or not we need to be storing period_end at
all.
This patch lays the groundwork for a compute engine designed to
facilitate construction of factor-based universe screening and portfolio
allocation. It contains:
A new module, `zipline.modelling`, containing entities that can be used
to express computations as dependency graphs. Each node in such a graph
is an instance of the base `Term` class, defined in
`zipline.modelling.term`. Dependency graphs are executed by instances
of `FFCEngine`, defined in `zipline.modelling.engine`.
A new module, `zipline.data.ffc`, containing loaders and dataset
definitions for inputs to the modelling API.
New `TradingAlgorithm` api methods: `add_factor`, and `add_filter`.
These methods can only be called from `initialize`, and are used to
inform the algorithm that each day it should compute the given terms.
Computed factor results are made available through a new attribute of
the `data` object in `before_trading_start` and `handle_data`. Computed
filter results control which assets are available in the factor matrix
on each day.
The datetime attribute of TradingAlgorithm was initially None, so calling get_datetime in initialize was causing an unhandled exception. This commit addresses that issue by calling on_dt_changed when initializing the TradingAlgorithm, to force all datetimes to be in line with the period_start.
This commit modifies the DataFrameSource and DataPanelSource to accept only Int64Indexes on the incoming data and moves the burden of mapping user identifiers to TradingAlgorithm.run().
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.
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.
Remove pieces that are no longer used now that the simple transforms are
wrappers around history via the SIDData object.
Move window length related pieces into batch_transform, since the rest
of the utils module is no longer used.
Currently, `order_percent()` and `order_target_percent()` both operate as a percentage of `self.portfolio.portfolio_value`. This PR lets them operate as percentages of other important MVs.
(also adds `context.get_market_value()`, which enables this functionality)
For example:
```python
order_percent('AAPL', 0.5)
order_percent('AAPL', 0.5, percent_of='cash')
order_target_percent('MSFT', 0.1, percent_of='shorts')
tech_stocks = ('AAPL', 'MSFT', 'GOOGL')
tech_filter = lambda p: p.sid in tech_stocks
for stock in tech_stocks:
order_target_percent(stock, 1/3, percent_of_fn=tech_filter)
```
Makes get_datetime agnostic as to whether its input is a datetime or a
Timestamp. (astimezone is an alias to tz_convert when the input is a
Timestamp).
script.
Adds an option kwarg to TradingAlgorithm named 'algo_filename' that
represents the file where the algoscript came from (if any). The
run_algo.py script will pass this argument with the value passed to the
'-f' flag. The default name is '<string>' to represent that the script
is coming from a string in python and not a file. This matches the
behavior of exec and the python convention for compiling code objects.
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.
- NotHalfDay only worked at midnight
- week_(start|end) were actually month_(start|end)
- Removes check_args from api.
- Default offset of 30mins for market_(open|close)
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.
Scott Sanderson