The write_data methods invokes the relevant AssetDBWriter subclass
to write data to the database. update_asset_finder is no longer
a relevant method since the AssetFinder is strictly a reader class.
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.
Removes support for handling dividends as part of the algorithm
simulation stream, replacing it with an API in `TradingAlgorithm` for
supplying dividends as a DataFrame.
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.
recent 2 for 1 stock split, where 1 class C share was distributed
for each share of class A held.
Now a dividend can specify a sid and ratio of stock that will be paid
to owners of the original security. If the ratio is 2.0, then for every
existing share, two shares will be paid.
Use date sorted sources instead, instead of sorting with second
argument of Event, etc. since the `heapq.merge` behavior is using
the second part of the tuple, thus requiring a richer set of comparison
methods, which would only be used in the test context.
Use `date_sorted_sources` instead, so that sorting is done on algo time
and source id.
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.
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.
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']
```
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.
- 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.
other details:
- also fixed grammatical errors in loader's status messages.
- converting the treasury curves to an ordered dict.
- moved to using a lambda for clarity as per @ehebert
- initializing calendar end dates to be midnight of current date in
- US/Eastern. Yahoo data isn't available until midnight eastern.
- added LSE reference rrules calendar (thanks to Edward Johns)
- added tests to verify LSE environment matches rrule calendar
- added a test to verify global environment behavior can be set.
- moved DailyReturn class to trading to eliminate circularity from
risk <-> trading.
- updated TradingEnvironment to be a context manager. This allows users
to run algorithms in individually isolated environments in one python
process. This is useful for managing multiple algorithms in a single
ipython notebook.
- added comments to explain behavior and useage of the global environment
Global state for the financial simulation environment is accessed through the
zipline.finance.trading module, which now contains a module variable:
environment.
Parameters are passed into an algorithm as a keyword argument, sim_params.
SimulationParameters creates a trading day index for the test period that
can be used to find trading days, calculate distance between trading days,
and other common operations. The sim params index is just selected from the
global state.
================
Details:
- adding delorean to the requirements.
- made index symbol a parameter for loading the benchmark data. changed
messagepack storage to be symbol specific.
- ported risk, performance, algorithm, transforms, batch transforms
and associated tests to use simulation parameters and global environment
- factory and sim factory use global state and sim params
- factory method parameter names now reflect the class expected
Algorithm returns and the risk calculations that depend on them now include
cash dividends. This commit does _not_ provide an API for user algorithms to
access dividends.
PerformanceTracker expects the dividend data to arrive as events, similar to
the way that Trades arrive. Dividends are expected to have adjusted payment
amounts that are inline with adjusted trades.
PerformanceTracker maintains state of all the unpaid dividends in the position
objects held in PerformancePeriod. Dividend objects contain all the relevant
dates (declared, ex, payment) as well as net and gross amounts. Dividends are
removed from the list as they are paid. Cash flow is not incremented until the
payment day. This creates the possibility of a dividend being owed but not
paid or realized before the end of a test. For example, a dividend with an
ex_date of today may have a pay date 2 weeks in the future. Right now the
algorithm does not receive any credit for unpaid dividends.
Tests cover buying/selling around the ex_date and payment_date, and checking
that the performance calculated is as expected.
The test factory was creating non-market days.
i.e. the date range spanned the weekend.
Using pandas' BDay frequency so that only business days are created.
This specific date range doesn't have holidays, so not accounting
for holidays in the factory.
Also, widens the range of the trading calendar to cover the test dates
generated by the factory which include 1990.
Previously the trading calendar began with 2002, meaning that holiday
and weekend adjustments with the data exercised by the factory did
not trigger when run with data in 1990.
This does increase the memory footprint of the tradingcalendar module.
However, only by a couple MB, so taking the hit there to enable
correct behavior.
Stewart Douglas