Fixes an issue that caused a crash if a user assigned a new column into a
returned history DataFrame. This occurred because we re-use DataFrames between
history() calls, so the new column caused an index-size mismatch on the next
attempted calculation.
Ideally we would fix this by in-place dropping the columns, but that isn't
supported in pandas 0.12.0, so instead we just return a complete copy of the
frame. We should re-evaluate this implementation when we're on a more modern
pandas version.
There were sevaral places you could supply sim_params
in TradingAlgorithm (__init__, run). This got confusing
as its not clear who updated what and which one was the
correct one to use at each time.
Then there were to ways to define data_frequency, one in
__init__() and one in the sim_params which also added code
complexity.
This refactor makes it explicit that sim_params are to be
passed to __init__() only. Moreover, data_frequency is
only stored in sim_params. For backwards compatibility,
it can still be supplied separately but will link to
the one in sim_params.
For example, you could create new sim params via:
sim_params = create_simulation_parameters(data_frequency='minute')
algo = MyAlgo(sim_params)
algo.run(data)
In addition, perf_tracker only gets initialized in one place:
_create_generator() which should also make the various ways
of running an algorithm more deterministic.
This also fixes a bug with SimulationParameters where
you could not change the period_start. Unfortunately, the
current implementation still requieres an implicit call to
update the internal variables.
Replace usage of .ix in TradingEnvironment with .loc when we know that we're
using an index key.
DataFrame.ix can be used with either integer or key-based indices, and as such
it incurs an overhead for figuring out which you meant.
The bug occurred because there is a special case in the initial window setup
code for handling the case where only a length-1 history is used for a given
frequency. Previously, the code was incorrectly calculating the period end
using a hard-coded expression for the end of the day (the correct behavior for
a length-1 '1d' history), and then using the frequency object to calculate the
period start for the window. In the case of length 1 '1m' data, this resulted
in an initial window whose start and end was the last minute of the day rather
than the first minute of the day. For non-price fields, this error doesn't
matter, because the window is only used for rolling digests (which doesn't
happen when there's only a length-1 history), and for the forward-filling logic
(which only happens on price fields). For a length-1 '1m' price, however, the
incorrect window causes us to attempt to forward-fill an empty panel, resulting
in an IndexError when we do an iloc[0] on a length-0 axis.
There quite some bugs in certain corner cases. Dropping of obsolete
axes was not working correctly, roll over could cause obsolete axes
to not drop. The tests are much more stringent now as well.
Adds a new argument, `freq_filter`, to `HistoryContainer.update_digest_panels`,
which can be used to supply a predicate determining which digest panels are
updated by the call. This is useful for supporting initialization/backfilling
of multi-frequency panels in subclasses.
The internal system used to build and get burn-in on packages was
accidentally using scipy 0.12.0, despite the change in the requirements
file to 0.13.2
Setting back to using scipy 0.12.0 until 0.13.2 is more tested with
internal systems, with the goal of upgrading to >= 0.14.0 as soon as
possible.
Truncate non-integer order amounts in `TradingAlgorithm.order` instead of
`Blotter.order`. This fixes an issue where non-integer orders coming out of
order_value can spuriously trigger a `LongOnly` trading guard.
Example:
sid.price == 2.0
order_value(sid, 5) -> order(sid, 2.5) -> truncated to order(sid, 2.0)
order_value(sid, -5) -> order(sid, -2.5) -> LongOnlyViolation b/c 2.0 - 2.5 < 0
The IPython magic still created an output file because
the output argument was only removed after the pipeline
was run. This fix simply removes the argument before
the call to run_pipline() when running the IPython magic.
Updates `HistoryContainer.roll` to handle cases where no data is present for
the period being rolled.
We now only forward-fill the `price` field when `ffill` is specified.
Overhauls `HistoryContainer` in prep for support of more than one frequency.
Major changes:
- Methods/variables referring to "day" have been renamed/generalized.
- `current_day_panel` became `buffer_panel`, which is now a `RollingPanel`
- `prior_day_panel` became a dictionary mapping `Frequency` objects to
"digest panels", which are instances of `RollingPanel`.
- Hard-coded daily rollover replaced with a notion of a "current window" for
each unique frequency managed by the panel.
- When the end of the current window is reached for a given frequency, we
compute an aggregate bar (code refers to this as a "digest"), which is
appended to a panel associated with that frequency.
- Window rollover dates are managed by a pair of dictionaries,
`cur_window_starts` and `cur_window_closes`. The `Frequency` class is
responsible for computing window bounds based on the open/close of the
previous window.
- Semantic change to the `open_price` field: `open_price` now always
contains the price of the first trade occurring in the given window.
Previously it contained the price of the first minute in the window,
returning NaN it the security happened not to trade in the first minute.
Adds a suite of new functions for querying data from the trading calendar.
These include:
`previous_trading_day`
`minutes_for_days_in_range` (minutely version of `days_in_range`)
`previous_open_and_close` (inverse of `next_open_and_close`)
`next_market_minute`
`previous_market_minute`
`open_close_window` (get a range of opens/closes with slicing semantics)
`market_minute_window` (get a range of minutes with slicing semantics)
Also refactors `test_finance` to move `TradingEnvironment` tests into their own
TestCase.
Adds a classmethod, `instance` on `TradingEnvironment` that returns
`zipline.finance.trading.environment`, instantiating it if necessary.
This makes it possible to initialize the default environment instance in a
less-roundabout way than creating a `SimulationParameters` object.
Refactored the target order methods to separate their logic from the
other order methods.
This makes order_target the only target method that calls order()
directly.
order_target_value is passed to order_target instead of order_value.
order_target_percent is passed to order_target_value rather than
order_value.
This simplified the code and decouples the logic of target orders from
the other order methods. This allows the target order methods to be
developed independently from order_value and order_percent.
For Emacs users, set a .dir-locals.el file to define behavior during
formatting operations like `fill-paragraph`
Set:
- Double spaces after sentences. (Which is the default.)
- Fill column to 79 characters. (To match pep8 script.)
- Docstrings to conform to Django formatting, since that is currently
most alike to the existing docstrings.
Many algorithms that use the new order methods like order_target()
will legitimately try to order 0 shares many times. The printed
warning at every turn is quite annoying and too verbose. We do not
display it on Quantopian either so I'm removing it here as well.
Instead of hard-coding the package versions in the Travis config,
grep the version number out of the requirements_dev.txt file,
so that the versions do not drift.
Upgrade pep8 1.4.6 -> 1.5.7
Upgrade pyflakes 0.7.3 -> 0.8.1
Also, tweak some line indentations which now show up as errors,
because of the fixes/changes to visual indent detection between
pep8 versions.
Delaney Granizo-Mackenzie