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.
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.
The function that handles a market close for daily frequency changed from
`handle_market_close` to `handle_market_close_daily`.
The function that is called at on the closing minute each day when running
minutely changed from `handle_intraday_close` to
`handle_intraday_market_close`.
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.
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
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.
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.
Make the portfolio property on TradingAlgorithm call `updated_portfolio`
internally. This prevents needless recomputation of the portfolio between
calls to `handle_data`, and also prevents issues where the portfolio object
could be unexpectedly modified in place in the body of a `handle_data` call.
Noteworthy finding in the course of investigating this bug:
If you modify a Python dictionary while iterating over it, the language will
only throw an exception if the size of the dictionary changes between loop
iterations; this means that you can do:
```
x = {1:1, 2:2, 3:3}
for k in x:
old_val = x[k]
del x[k]
x[f(k)] = old_val
print k
```
and you'll only get an error if f(k) is already a key in the dictionary.
This can lead to bizarre/nondeterministic behavior in the key iterator.
Adds four new methods to the Zipline API that can be used as circuit-breakers
to interrupt the execution of an algorithm. The API methods are:
`set_max_position_size`
`set_max_order_size`
`set_max_order_count`
`set_long_only`
Internally, these methods are implemented by each registering a TradingControl
callback object with the TradingAlgorithm. During
TradingAlgorithm.__validate_order_params (and thus before any side-effects of
the order call occur), each callback's `validate` method is called with
information about the order to be placed and the algorithm's current state,
raising an exception if the callback detects that an error condition has been breached.
TradingEnvironment class uses env_trading_calendar for trading days,
but the default trading calendar for open_and_close data, which causes
errors later, because of misalignment of trading days.
The issue can be resolved by using env_trading_calendar for
open_and_closes as well
Adds the exchange property the interface for ExecutionStyle and adds an
exchange parameter to the interface of all the existing ExceutionStyles.
Subclasses wishing to support the ability to specify an exchange should set the
_exchange attribute in __init__.
Stop and limit prices both trigger when a price crosses some threshold, but
they trigger in "opposite directions". For example, on a buy, a limit price is
triggered when a price falls below a specified value, whereas a stop price
triggers when the price exceeds a specified value.
Our current stop/limit price rounding logic is asymmetric, preferring to "round
to improve" the specified price. This change makes it so that we interpret
"improvement" in opposite directions for stop vs limit prices.
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.
