Since statsmodels requires patsy, and statsmodels is now in
requirements.txt instead of requirements_dev.txt, patsy also needs to
be in requirements.txt instead of requirements_dev.txt.
Make the error messages for {DoBadThing}PostInit no longer reference "the
simulation", since the algorithm may not actually be running as a simulation.
Pandas website states statsmodels is needed for parts of pandas.stats.
Also, update statsmodels to 0.5.0.
Upgrade of version is to both keep up to date, and to improve
performance on lazy imports, as fixed by
https://github.com/statsmodels/statsmodels/issues/573
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
When zipline is imported it checks whether
it runs in the IPython notebook. If it does,
it registers a %%zipline magic that takes the
same arguments as the CLI with the addition of
a -o for specifying the output variable to store
the performance frame in.
The algo code in the cell is, as of yet, executed
in its own environment rather than that of the
IPython NB which is probably what we want.
Also adds cli option to save the perf dataframe
to a pickle file.
Also adds an IPython notebook buyapple example.
Add a CLI that reads in an algorithm, loads data,
run the algorithm, and output performance metrics.
The examples are adapted to the new zipline API and
analyses are split into separate files.
Also add config files that run the example
algorithms with preset settings.
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.
Add a test case in test_algorithms to verify that appropriate exceptions are
thrown if an algorithm makes a call to the order api with a stop/limit price
and a style.
Add `style` parameter to order_value, order_percent, order_target,
order_target_percent, and order_target_value methods. The style parameter is
forwarded to the underlying call to `order`.
existing `limit_price` and `stop_price` parameters. The goal of this change is
to refactor the existing ordering API to provide a cleaner interface for
defining more complex order types.
Adds a new module, zipline.finance.execution, which defines the ExecutionStyle
abstract base class, along with concrete MarketOrder, LimitOrder, StopOrder,
and StopLimitOrder subclasses.
Adds a new `style` keyword argument to the function signature of the `order`
API method, which accepts an instance of ExecutionStyle.
The existing limit_price and stop_price parameters are still supported at this
time, but are converted into the new ExecutionStyle objects before being passed
to Blotter.order.
Fixes an issue where very low limit prices were being rounded to 0.0 and
effectively resulting in market orders. Adds an explicit check to test for
this behavior.
Adds a test algorithm that tries to buy with very high limit prices/very low
stop prices and tries to sell with very low limit prices/very high stop prices.
TradingAlgorithm always uses set_algo_instance in pairs of
set_algo_instance(self) and set_algo_instance(None). Refactoring this to use a
context manager.
The check of existence of the null return key, and the drop of said
return on every single bar was adding unneeded CPU time when an
algorithm was run with minute emissions.
Instead, add the 0.0 return with an index of the trading day before
the start date.
The removal of the `null return` was mainly in place so that the
period calculation was not crashing on a non-date index value;
with the index as a date, the period return can also approximate
volatility (even though the that volatility has high noise-to-signal
strength because it uses only two values as an input.)
The factoring out of the Sharpe calculation changed behavior
so that both period and cumulative return nans when there is
no volatility; however before that change period returned 0.0.
This breaks existing consumers which expected a non-nan value
for period results.
Smooth out that change by checking the value after the sharpe
has been calculated and reset nan's to 0.0
The calculations that are expected to change are:
- cumulative.beta
- cumulative.alpha
- cumulative.information
- cumulative.sharpe
- period.sortino
* Explanation of how risk calculations are changing
** Risk Fixes for Both Period and Cumulative
*** Downside Risk
Use sample instead of population for standard deviation.
Add a rounding factor, so that if the two values are close for a given
dt, that they do not count as a downside value, which would throw off
the denominator of the standard deviation of the downside diffs.
*** Standard Deviation Type
Across the board the standard deviation has been standardized to using
a 'sample' calculation, whereas before cumulative risk was monstly using
'population'. Using `ddof=1` with `np.std` calculates as if the values
are a sample.
** Cumulative Risk Fixes
*** Beta
Use the daily algorithm returns and benchmarks instead of annualized
mean returns.
*** Volatility
Use sample instead of population with standard deviation.
The volatility is an input to other calculations so this change affects
Sharpe and Information ratio calculations.
*** Information Ratio
The benchmark returns input is changed from annualized benchmark returns
to the annualized mean returns.
*** Alpha
The benchmark returns input is changed from annualized benchmark returns
to the annualized mean returns.
** Period Risk Fixes
*** Sortino
Use the downside risk of the daily return vs. the mean algorithm returns
for the minimum acceptable return instead of the treasury return.
The above required adding the calculation of the mean algorithm returns
for period risk.
Also, use algorithm_period_returns and tresaury_period_return as the
cumulative Sortino does, instead of using algorithm returns for both
inputs into the Sortino calculation.
* Other Supporting Changes
** answer_key
Add new mappings for downside risk and Sortino as well as
re-address the index mappings because of changes to the answer key
spread sheet.
** test_risk_cumulative
Change the decimal precision to expect higher precision.
The calculations are now more aligned with the answer key, so we can
expect higher precision. In particular now that the standard deviation
type matches everywhere in both the Python implementation and the answer
sheet, the precision of the first value no longer has to be glossed over.
** test_events_through_risk
Change the results which are used as a canary for risk changes,
since we do expect Sharpe to change with this change..
Change the algorithm volatility test to use the same iterkv style
as the rest of the suite, as it was useful to be able to zero in
on the offending date when debugging changes to the risk module.
Jonathan Kamens