As of logbook 0.10.0, logbook no longer installs a default handler,
which means that if the application doesn't install one, log messages
disappear into the ether.
Therefore, all of our scripts with `__main__` endpoints need to push a
`logbook.StderrHandler` if they're not already pushing some other
handler.
This commit modifies the DataFrameSource and DataPanelSource to accept only Int64Indexes on the incoming data and moves the burden of mapping user identifiers to TradingAlgorithm.run().
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.
This is a step towards the goal of uniting Quantopian scripts
and zipline.
To make the syntax of zipline identical to Quantopian
we break out the API methods (like order) and turn them into
functions. To access the algo object we add a thread local reference
to the current algorithm that is accessed in the API functions.
TradingAlgorithm now takes either a string or two functions
(initialize and handle_data) that it executes.
Use api method decorator for methods available in algoscript.
Ported appropriate algorithm tests from internal code.
So that the 1-Month Sharpe ratio has a curve to use during calculation,
use data from 2002, since the Treasury returns 1 Month data starting
in July, 2001.
- Use `print()` function for all print calls
- Fix strip and format calls that were on the outside of the
print function for some reason.
(Which were breaking in Python 3 because of print returning None.)
- Remove commented out print calls.
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.
To support mulitple sids the TALib transforms now return a dict,
instead of a float. Accordingly, the TALib example script now needs
to index into the transform result.
The new example is almost identical to the dual_moving_average one.
However, instead of our in-house mavg transform it uses the new
talib exponential moving average (EMA).
Fix crash due to 'delay' was no longer supported.
But removing SlippageModel override, since current configs
should be functionally equivalent to FixedSlippage.
Uses a method called 'record' that provides a key value,
instead of providing keys to extract from context.
The variables are stored internally to the algorithm in a dictionary,
and not just stared as a property of the algorithm.
Main intent behind this change is to make the API more user friendly,
since the previous recorded_variables relies on the value to be set
in the algorithms context/self, the hope is that only having to use
the `record` method means less moving pieces and a more understandable
API.
i.e., instead of:
```
def initialize(self):
recorded_variables('foo', bar')
def handle_data(self, data):
self.foo = 1
self.bar = 2
```
The API is now:
```
def initialize(self):
pass
def handle_data(self, data):
self.record(foo=1, bar=2)
```
Stewart Douglas