The code that was consuming noop_environment now uses a
real trading environment.
As more behavior relies on an accurate trading calendar, maintaining
the noop environment was a constraint that was more overhead than it
is worth.
Passing the exchange time timestamp to is_market_hours was ending
up with odd behavior due to conversion back to UTC when checking
the is_trading_day boolean.
Use the early closes to populate a DataFrame which includes
the open and close minute for each day.
To be used by the environment instead of calculating each value
mid-backtest.
Remove the lists of DailyReturn objects in favor of using pd.Series
to store the return values.
Should make it easier to inspect the values when stepping through,
make the windowing of data to a certain range more facile by using,
and have some performance increases due to removing object creation
and member access.
Instead of using all calendar days between start and end in test
sources, use the trading calendar for test sources.
Needed for an incoming refactoring of market open and close,
where the opens and closes are indexed by market days.
Most of the functions in date_utils can be done via pandas.
The other functions are no longer used for loading, etc. so remove
the date_utils module to reduce the total surface area of Zipline core.
Instead of using a pandas Series of with dictionaries as the
values treasury curves, use a DataFrame which more naturally fits
the data type of a having a timeseries with mulitple values.
Should allow easier slicing/manipulation of the treasury curves,
e.g. getting 10 year curves would now be:
```
treasury_curves['10year']
```
For consistency, datetimes returned by the trading calendar should
always show HHMMSS of midnight UTC. Not only is this useful for
consistency, but it also allows us to check if a particular date() is
in an array of these datetimes, because they will hash to the same
thing. For example:
early_closes = get_early_closes()
... later ...
if current_bar_datetime.date() in early_closes:
... today closes early ...
If if the datetimes returned by the trading calendar functions don't
have 00:00:00 for HHMMSS, then the "in" check above will fail because
the date and the datetimes in early_closes won't hash to the same
thing.
Fix warnings when compiling the docs.
Removes the documentation of the default types, which already gets
included automatically and was wrong because not kept in sync with the
function signature.
Changed, the formatting to the Sphinx formatting.
This looks much better in the compiled documents, but does make the
source a bit harder to read.
Before the change to the RollingPanel, window_length
specified the number of days that should be in a window.
The previous commit broke this if data was minute resolution.
By passing bar='minute' to the batch_transform we internally
multiply the window_length by 60*6.5 to have a full day.
Also adds a (still rudamentary) test for batch_transform
with minute data.
The check() function in zipline.utils.test_utils was only comparing
lists up to the length of the shortest list. This fix uses
izip_longest instead of izip so it compares up to the length of the
longest list, which among other things means that it will now
correctly report when one list is empty and the other is not.
- moved Order and Blotter to zipline.finance.blotter
- moved order method from AlgoSimulator to Blotter
- eliminated the set_order method in algorithm
- moved blotter to the algorithm
The use of np.allclose introduced a severe performance penalty,
caused by the creation of two `np.array`s for each check.
Instead create and use a similar check which maintains tolerance
to floating point rounding, but operates only on scalars.
- Add transaction and order types
- Move TransactionSimulator from trading.py to tradesimulation.py
(only used by other members of the tradesimulation module)
- Make Transaction an independent event, like dividend
- Add Blotter class.
- Flatten the transaction events to be independent of trade bar events
- Make orders into events that reach performance (need to add
handling)
- Issue IDs to orders and tracking each transaction's order id.
- Make volume share slippage fill orders independently, rather than
aggregating them into a single transaction.
- Perf tracker holds orders, serializes them with transactions.
- Order state defined and maintained by order class.
- Minutely emission of orders based on last_modified date.
The start and end of the simulation parameters should be 'normalized'
i.e. midnight timestamped.
However, the algorithm tests were using the timestamp of the
first and last trade, which were in market times,
i.e. 9:30 AM and 4:00 PM EST.
Fix passing the sim_params that is used to create the trade_history,
instead of having the sim_params inferred from the source.
(Also may want to consider fixing the logic that infers the date
range from the sources provided.)
Also, add a `num_days` option to `factory.create_simulation_parameters`
so that the a date range that covers the desired number of days is covered.
Since the default sim_params were covering a year, while the test only
supplies 4 values, causing an alignment issue with the record test,
since a years worth of results were returned, but there were only 4 events.
Set the default end date to current date, so that trading on
'fresh' data is the default case.
Set the default begin date at 1/1/1990, since that is when the
treasury benchmark data is first available.
In the test factory creation of returns, the date creation was using
a timedelta of one day instead of incrementing by trading days.
Working towards changing risk module behavior which would leverage
the trading day map, but tests fail because non-trading days are
created.
Remove `factory.create_returns`, moving uses of that function to us
`factory.create_returns_from_period`, since the number of days input
for `create_returns` was more difficult to use when specifying ranges
over arbirtray dates.
Eddie Hebert