mirror of
https://github.com/saymrwulf/zipline.git
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This data bundle will use the quantopian mirror of the quandl WIKI data instead of downloading from quandl directly. This dramatically improves the speed because we do not pay the rate limiting for quandl and we can send the data in the format zipline expects.
722 lines
25 KiB
ReStructuredText
722 lines
25 KiB
ReStructuredText
Zipline beginner tutorial
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-------------------------
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Basics
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~~~~~~
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Zipline is an open-source algorithmic trading simulator written in
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Python.
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The source can be found at: https://github.com/quantopian/zipline
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Some benefits include:
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- Realistic: slippage, transaction costs, order delays.
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- Stream-based: Process each event individually, avoids look-ahead
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bias.
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- Batteries included: Common transforms (moving average) as well as
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common risk calculations (Sharpe).
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- Developed and continuously updated by
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`Quantopian <https://www.quantopian.com>`__ which provides an
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easy-to-use web-interface to Zipline, 10 years of minute-resolution
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historical US stock data, and live-trading capabilities. This
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tutorial is directed at users wishing to use Zipline without using
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Quantopian. If you instead want to get started on Quantopian, see
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`here <https://www.quantopian.com/faq#get-started>`__.
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This tutorial assumes that you have zipline correctly installed, see the
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`installation
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instructions <https://github.com/quantopian/zipline#installation>`__ if
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you haven't set up zipline yet.
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Every ``zipline`` algorithm consists of two functions you have to
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define: \* ``initialize(context)`` \* ``handle_data(context, data)``
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Before the start of the algorithm, ``zipline`` calls the
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``initialize()`` function and passes in a ``context`` variable.
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``context`` is a persistent namespace for you to store variables you
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need to access from one algorithm iteration to the next.
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After the algorithm has been initialized, ``zipline`` calls the
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``handle_data()`` function once for each event. At every call, it passes
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the same ``context`` variable and an event-frame called ``data``
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containing the current trading bar with open, high, low, and close
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(OHLC) prices as well as volume for each stock in your universe. For
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more information on these functions, see the `relevant part of the
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Quantopian docs <https://www.quantopian.com/help#api-toplevel>`__.
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My first algorithm
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~~~~~~~~~~~~~~~~~~
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Lets take a look at a very simple algorithm from the ``examples``
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directory, ``buyapple.py``:
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.. code-block:: python
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from zipline.examples import buyapple
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buyapple??
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.. code-block:: python
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from zipline.api import order, record, symbol
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def initialize(context):
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pass
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def handle_data(context, data):
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order(symbol('AAPL'), 10)
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record(AAPL=data.current(symbol('AAPL'), 'price'))
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As you can see, we first have to import some functions we would like to
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use. All functions commonly used in your algorithm can be found in
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``zipline.api``. Here we are using :func:`~zipline.api.order()` which takes two
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arguments: a security object, and a number specifying how many stocks you would
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like to order (if negative, :func:`~zipline.api.order()` will sell/short
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stocks). In this case we want to order 10 shares of Apple at each iteration. For
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more documentation on ``order()``, see the `Quantopian docs
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<https://www.quantopian.com/help#api-order>`__.
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Finally, the :func:`~zipline.api.record` function allows you to save the value
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of a variable at each iteration. You provide it with a name for the variable
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together with the variable itself: ``varname=var``. After the algorithm
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finished running you will have access to each variable value you tracked
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with :func:`~zipline.api.record` under the name you provided (we will see this
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further below). You also see how we can access the current price data of the
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AAPL stock in the ``data`` event frame (for more information see
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`here <https://www.quantopian.com/help#api-event-properties>`__.
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Running the algorithm
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~~~~~~~~~~~~~~~~~~~~~
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To now test this algorithm on financial data, ``zipline`` provides three
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interfaces: A command-line interface, ``IPython Notebook`` magic, and
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:func:`~zipline.run_algorithm`.
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Command line interface
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^^^^^^^^^^^^^^^^^^^^^^
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After you installed zipline you should be able to execute the following
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from your command line (e.g. ``cmd.exe`` on Windows, or the Terminal app
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on OSX):
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.. code-block:: bash
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$ python -m zipline run --help
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.. parsed-literal::
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Usage: __main__.py run [OPTIONS]
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Run a backtest for the given algorithm.
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Options:
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-f, --algofile FILENAME The file that contains the algorithm to run.
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-t, --algotext TEXT The algorithm script to run.
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-D, --define TEXT Define a name to be bound in the namespace
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before executing the algotext. For example
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'-Dname=value'. The value may be any python
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expression. These are evaluated in order so
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they may refer to previously defined names.
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--data-frequency [minute|daily]
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The data frequency of the simulation.
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[default: daily]
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--capital-base FLOAT The starting capital for the simulation.
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[default: 10000000.0]
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-b, --bundle BUNDLE-NAME The data bundle to use for the simulation.
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[default: quantopian-quandl]
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--bundle-timestamp TIMESTAMP The date to lookup data on or before.
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[default: <current-time>]
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-s, --start DATE The start date of the simulation.
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-e, --end DATE The end date of the simulation.
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-o, --output FILENAME The location to write the perf data. If this
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is '-' the perf will be written to stdout.
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[default: -]
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--print-algo / --no-print-algo Print the algorithm to stdout.
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--help Show this message and exit.
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As you can see there are a couple of flags that specify where to find your
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algorithm (``-f``) as well as parameters specifying which data to use,
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defaulting to the :ref:`quantopian-quandl-mirror`. There are also arguments for
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the date range to run the algorithm over (``--start`` and ``--end``). Finally,
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you'll want to save the performance metrics of your algorithm so that you can
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analyze how it performed. This is done via the ``--output`` flag and will cause
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it to write the performance ``DataFrame`` in the pickle Python file format.
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Note that you can also define a configuration file with these parameters that
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you can then conveniently pass to the ``-c`` option so that you don't have to
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supply the command line args all the time (see the .conf files in the examples
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directory).
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Thus, to execute our algorithm from above and save the results to
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``buyapple_out.pickle`` we would call ``python -m zipline run`` as follows:
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.. code-block:: python
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python -m zipline run -f ../../zipline/examples/buyapple.py --start 2000-1-1 --end 2014-1-1 --symbols AAPL -o buyapple_out.pickle
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.. parsed-literal::
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AAPL
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[2015-11-04 22:45:32.820166] INFO: Performance: Simulated 3521 trading days out of 3521.
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[2015-11-04 22:45:32.820314] INFO: Performance: first open: 2000-01-03 14:31:00+00:00
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[2015-11-04 22:45:32.820401] INFO: Performance: last close: 2013-12-31 21:00:00+00:00
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``run`` first calls the ``initialize()`` function, and then
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streams the historical stock price day-by-day through ``handle_data()``.
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After each call to ``handle_data()`` we instruct ``zipline`` to order 10
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stocks of AAPL. After the call of the ``order()`` function, ``zipline``
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enters the ordered stock and amount in the order book. After the
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``handle_data()`` function has finished, ``zipline`` looks for any open
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orders and tries to fill them. If the trading volume is high enough for
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this stock, the order is executed after adding the commission and
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applying the slippage model which models the influence of your order on
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the stock price, so your algorithm will be charged more than just the
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stock price \* 10. (Note, that you can also change the commission and
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slippage model that ``zipline`` uses, see the `Quantopian
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docs <https://www.quantopian.com/help#ide-slippage>`__ for more
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information).
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Lets take a quick look at the performance ``DataFrame``. For this, we
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use ``pandas`` from inside the IPython Notebook and print the first ten
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rows. Note that ``zipline`` makes heavy usage of ``pandas``, especially
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for data input and outputting so it's worth spending some time to learn
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it.
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.. code-block:: python
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import pandas as pd
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perf = pd.read_pickle('buyapple_out.pickle') # read in perf DataFrame
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perf.head()
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.. raw:: html
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<div style="max-height:1000px;max-width:1500px;overflow:auto;">
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<table border="1" class="dataframe">
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<thead>
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<tr style="text-align: right;">
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<th></th>
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<th>AAPL</th>
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<th>algo_volatility</th>
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<th>algorithm_period_return</th>
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<th>alpha</th>
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<th>benchmark_period_return</th>
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<th>benchmark_volatility</th>
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<th>beta</th>
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<th>capital_used</th>
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<th>ending_cash</th>
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<th>ending_exposure</th>
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<th>...</th>
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<th>short_exposure</th>
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<th>short_value</th>
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<th>shorts_count</th>
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<th>sortino</th>
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<th>starting_cash</th>
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<th>starting_exposure</th>
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<th>starting_value</th>
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<th>trading_days</th>
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<th>transactions</th>
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<th>treasury_period_return</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th>2000-01-03 21:00:00</th>
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<td>3.738314</td>
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<td>0.000000e+00</td>
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<td>0.000000e+00</td>
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<td>-0.065800</td>
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<td>-0.009549</td>
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<td>0.000000</td>
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<td>0.000000</td>
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<td>0.00000</td>
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<td>10000000.00000</td>
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<td>0.00000</td>
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<td>...</td>
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<td>0</td>
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<td>0</td>
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<td>0</td>
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<td>0.000000</td>
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<td>10000000.00000</td>
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<td>0.00000</td>
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<td>0.00000</td>
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<td>1</td>
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<td>[]</td>
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<td>0.0658</td>
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</tr>
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<tr>
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<th>2000-01-04 21:00:00</th>
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<td>3.423135</td>
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<td>3.367492e-07</td>
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<td>-3.000000e-08</td>
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<td>-0.064897</td>
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<td>-0.047528</td>
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<td>0.323229</td>
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<td>0.000001</td>
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<td>-34.53135</td>
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<td>9999965.46865</td>
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<td>34.23135</td>
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<td>...</td>
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<td>0</td>
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<td>0</td>
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<td>0</td>
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<td>0.000000</td>
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<td>10000000.00000</td>
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<td>0.00000</td>
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<td>0.00000</td>
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<td>2</td>
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<td>[{u'order_id': u'513357725cb64a539e3dd02b47da7...</td>
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<td>0.0649</td>
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</tr>
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<tr>
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<th>2000-01-05 21:00:00</th>
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<td>3.473229</td>
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<td>4.001918e-07</td>
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<td>-9.906000e-09</td>
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<td>-0.066196</td>
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<td>-0.045697</td>
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<td>0.329321</td>
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<td>0.000001</td>
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<td>-35.03229</td>
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<td>9999930.43636</td>
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<td>69.46458</td>
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<td>...</td>
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<td>0</td>
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<td>0</td>
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<td>0</td>
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<td>0.000000</td>
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<td>9999965.46865</td>
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<td>34.23135</td>
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<td>34.23135</td>
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<td>3</td>
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<td>[{u'order_id': u'd7d4ad03cfec4d578c0d817dc3829...</td>
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<td>0.0662</td>
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</tr>
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<tr>
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<th>2000-01-06 21:00:00</th>
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<td>3.172661</td>
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<td>4.993979e-06</td>
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<td>-6.410420e-07</td>
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<td>-0.065758</td>
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<td>-0.044785</td>
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<td>0.298325</td>
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<td>-0.000006</td>
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<td>-32.02661</td>
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<td>9999898.40975</td>
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<td>95.17983</td>
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<td>...</td>
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<td>0</td>
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<td>0</td>
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<td>0</td>
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<td>-12731.780516</td>
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<td>9999930.43636</td>
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<td>69.46458</td>
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<td>69.46458</td>
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<td>4</td>
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<td>[{u'order_id': u'1fbf5e9bfd7c4d9cb2e8383e1085e...</td>
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<td>0.0657</td>
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</tr>
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<tr>
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<th>2000-01-07 21:00:00</th>
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<td>3.322945</td>
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<td>5.977002e-06</td>
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<td>-2.201900e-07</td>
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<td>-0.065206</td>
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<td>-0.018908</td>
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<td>0.375301</td>
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<td>0.000005</td>
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<td>-33.52945</td>
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<td>9999864.88030</td>
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<td>132.91780</td>
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<td>...</td>
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<td>0</td>
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<td>0</td>
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<td>0</td>
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<td>-12629.274583</td>
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<td>9999898.40975</td>
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<td>95.17983</td>
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<td>95.17983</td>
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<td>5</td>
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<td>[{u'order_id': u'9ea6b142ff09466b9113331a37437...</td>
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<td>0.0652</td>
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</tr>
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</tbody>
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</table>
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<p>5 rows × 39 columns</p>
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</div>
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As you can see, there is a row for each trading day, starting on the
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first business day of 2000. In the columns you can find various
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information about the state of your algorithm. The very first column
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``AAPL`` was placed there by the ``record()`` function mentioned earlier
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and allows us to plot the price of apple. For example, we could easily
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examine now how our portfolio value changed over time compared to the
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AAPL stock price.
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.. code-block:: python
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%pylab inline
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figsize(12, 12)
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import matplotlib.pyplot as plt
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ax1 = plt.subplot(211)
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perf.portfolio_value.plot(ax=ax1)
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ax1.set_ylabel('portfolio value')
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ax2 = plt.subplot(212, sharex=ax1)
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perf.AAPL.plot(ax=ax2)
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ax2.set_ylabel('AAPL stock price')
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.. parsed-literal::
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Populating the interactive namespace from numpy and matplotlib
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.. parsed-literal::
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<matplotlib.text.Text at 0x7ff5c6147f90>
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.. image:: tutorial_files/tutorial_11_2.png
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As you can see, our algorithm performance as assessed by the
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``portfolio_value`` closely matches that of the AAPL stock price. This
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is not surprising as our algorithm only bought AAPL every chance it got.
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IPython Notebook
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~~~~~~~~~~~~~~~~
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The `IPython Notebook <http://ipython.org/notebook.html>`__ is a very
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powerful browser-based interface to a Python interpreter (this tutorial
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was written in it). As it is already the de-facto interface for most
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quantitative researchers ``zipline`` provides an easy way to run your
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algorithm inside the Notebook without requiring you to use the CLI.
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To use it you have to write your algorithm in a cell and let ``zipline``
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know that it is supposed to run this algorithm. This is done via the
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``%%zipline`` IPython magic command that is available after you
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``import zipline`` from within the IPython Notebook. This magic takes
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the same arguments as the command line interface described above. Thus
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to run the algorithm from above with the same parameters we just have to
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execute the following cell after importing ``zipline`` to register the
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magic.
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.. code-block:: python
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%load_ext zipline
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.. code-block:: python
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%%zipline --start 2000-1-1 --end 2014-1-1 --symbols AAPL
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from zipline.api import symbol, order, record
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def initialize(context):
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pass
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def handle_data(context, data):
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order(symbol('AAPL'), 10)
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record(AAPL=data[symbol('AAPL')].price)
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Note that we did not have to specify an input file as above since the
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magic will use the contents of the cell and look for your algorithm
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functions there. Also, instead of defining an output file we are
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specifying a variable name with ``-o`` that will be created in the name
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space and contain the performance ``DataFrame`` we looked at above.
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.. code-block:: python
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_.head()
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.. raw:: html
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<div style="max-height:1000px;max-width:1500px;overflow:auto;">
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<table border="1" class="dataframe">
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<thead>
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<tr style="text-align: right;">
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<th></th>
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<th>AAPL</th>
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<th>algo_volatility</th>
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<th>algorithm_period_return</th>
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<th>alpha</th>
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<th>benchmark_period_return</th>
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<th>benchmark_volatility</th>
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<th>beta</th>
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<th>capital_used</th>
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<th>ending_cash</th>
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<th>ending_exposure</th>
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<th>...</th>
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<th>short_exposure</th>
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<th>short_value</th>
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<th>shorts_count</th>
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<th>sortino</th>
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<th>starting_cash</th>
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<th>starting_exposure</th>
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<th>starting_value</th>
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<th>trading_days</th>
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<th>transactions</th>
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<th>treasury_period_return</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th>2000-01-03 21:00:00</th>
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<td>3.738314</td>
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<td>0.000000e+00</td>
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<td>0.000000e+00</td>
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<td>-0.065800</td>
|
||
<td>-0.009549</td>
|
||
<td>0.000000</td>
|
||
<td>0.000000</td>
|
||
<td>0.00000</td>
|
||
<td>10000000.00000</td>
|
||
<td>0.00000</td>
|
||
<td>...</td>
|
||
<td>0</td>
|
||
<td>0</td>
|
||
<td>0</td>
|
||
<td>0.000000</td>
|
||
<td>10000000.00000</td>
|
||
<td>0.00000</td>
|
||
<td>0.00000</td>
|
||
<td>1</td>
|
||
<td>[]</td>
|
||
<td>0.0658</td>
|
||
</tr>
|
||
<tr>
|
||
<th>2000-01-04 21:00:00</th>
|
||
<td>3.423135</td>
|
||
<td>3.367492e-07</td>
|
||
<td>-3.000000e-08</td>
|
||
<td>-0.064897</td>
|
||
<td>-0.047528</td>
|
||
<td>0.323229</td>
|
||
<td>0.000001</td>
|
||
<td>-34.53135</td>
|
||
<td>9999965.46865</td>
|
||
<td>34.23135</td>
|
||
<td>...</td>
|
||
<td>0</td>
|
||
<td>0</td>
|
||
<td>0</td>
|
||
<td>0.000000</td>
|
||
<td>10000000.00000</td>
|
||
<td>0.00000</td>
|
||
<td>0.00000</td>
|
||
<td>2</td>
|
||
<td>[{u'commission': 0.3, u'amount': 10, u'sid': 0...</td>
|
||
<td>0.0649</td>
|
||
</tr>
|
||
<tr>
|
||
<th>2000-01-05 21:00:00</th>
|
||
<td>3.473229</td>
|
||
<td>4.001918e-07</td>
|
||
<td>-9.906000e-09</td>
|
||
<td>-0.066196</td>
|
||
<td>-0.045697</td>
|
||
<td>0.329321</td>
|
||
<td>0.000001</td>
|
||
<td>-35.03229</td>
|
||
<td>9999930.43636</td>
|
||
<td>69.46458</td>
|
||
<td>...</td>
|
||
<td>0</td>
|
||
<td>0</td>
|
||
<td>0</td>
|
||
<td>0.000000</td>
|
||
<td>9999965.46865</td>
|
||
<td>34.23135</td>
|
||
<td>34.23135</td>
|
||
<td>3</td>
|
||
<td>[{u'commission': 0.3, u'amount': 10, u'sid': 0...</td>
|
||
<td>0.0662</td>
|
||
</tr>
|
||
<tr>
|
||
<th>2000-01-06 21:00:00</th>
|
||
<td>3.172661</td>
|
||
<td>4.993979e-06</td>
|
||
<td>-6.410420e-07</td>
|
||
<td>-0.065758</td>
|
||
<td>-0.044785</td>
|
||
<td>0.298325</td>
|
||
<td>-0.000006</td>
|
||
<td>-32.02661</td>
|
||
<td>9999898.40975</td>
|
||
<td>95.17983</td>
|
||
<td>...</td>
|
||
<td>0</td>
|
||
<td>0</td>
|
||
<td>0</td>
|
||
<td>-12731.780516</td>
|
||
<td>9999930.43636</td>
|
||
<td>69.46458</td>
|
||
<td>69.46458</td>
|
||
<td>4</td>
|
||
<td>[{u'commission': 0.3, u'amount': 10, u'sid': 0...</td>
|
||
<td>0.0657</td>
|
||
</tr>
|
||
<tr>
|
||
<th>2000-01-07 21:00:00</th>
|
||
<td>3.322945</td>
|
||
<td>5.977002e-06</td>
|
||
<td>-2.201900e-07</td>
|
||
<td>-0.065206</td>
|
||
<td>-0.018908</td>
|
||
<td>0.375301</td>
|
||
<td>0.000005</td>
|
||
<td>-33.52945</td>
|
||
<td>9999864.88030</td>
|
||
<td>132.91780</td>
|
||
<td>...</td>
|
||
<td>0</td>
|
||
<td>0</td>
|
||
<td>0</td>
|
||
<td>-12629.274583</td>
|
||
<td>9999898.40975</td>
|
||
<td>95.17983</td>
|
||
<td>95.17983</td>
|
||
<td>5</td>
|
||
<td>[{u'commission': 0.3, u'amount': 10, u'sid': 0...</td>
|
||
<td>0.0652</td>
|
||
</tr>
|
||
</tbody>
|
||
</table>
|
||
<p>5 rows × 39 columns</p>
|
||
</div>
|
||
|
||
|
||
Access to previous prices using ``history``
|
||
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
|
||
|
||
Working example: Dual Moving Average Cross-Over
|
||
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
|
||
|
||
The Dual Moving Average (DMA) is a classic momentum strategy. It's
|
||
probably not used by any serious trader anymore but is still very
|
||
instructive. The basic idea is that we compute two rolling or moving
|
||
averages (mavg) -- one with a longer window that is supposed to capture
|
||
long-term trends and one shorter window that is supposed to capture
|
||
short-term trends. Once the short-mavg crosses the long-mavg from below
|
||
we assume that the stock price has upwards momentum and long the stock.
|
||
If the short-mavg crosses from above we exit the positions as we assume
|
||
the stock to go down further.
|
||
|
||
As we need to have access to previous prices to implement this strategy
|
||
we need a new concept: History
|
||
|
||
``history()`` is a convenience function that keeps a rolling window of
|
||
data for you. The first argument is the number of bars you want to
|
||
collect, the second argument is the unit (either ``'1d'`` for ``'1m'``
|
||
but note that you need to have minute-level data for using ``1m``). For
|
||
a more detailed description ``history()``'s features, see the
|
||
`Quantopian docs <https://www.quantopian.com/help#ide-history>`__. While
|
||
you can directly use the ``history()`` function on Quantopian, in
|
||
``zipline`` you have to register each history container you want to use
|
||
with ``add_history()`` and pass it the same arguments as the history
|
||
function below. Lets look at the strategy which should make this clear:
|
||
|
||
.. code-block:: python
|
||
|
||
%%zipline --start 2000-1-1 --end 2014-1-1 -o perf_dma
|
||
|
||
|
||
from zipline.api import order_target, record, symbol, history, add_history
|
||
import numpy as np
|
||
|
||
def initialize(context):
|
||
# Register 2 histories that track daily prices,
|
||
# one with a 100 window and one with a 300 day window
|
||
add_history(100, '1d', 'price')
|
||
add_history(300, '1d', 'price')
|
||
|
||
context.i = 0
|
||
|
||
|
||
def handle_data(context, data):
|
||
# Skip first 300 days to get full windows
|
||
context.i += 1
|
||
if context.i < 300:
|
||
return
|
||
|
||
# Compute averages
|
||
# history() has to be called with the same params
|
||
# from above and returns a pandas dataframe.
|
||
short_mavg = history(100, '1d', 'price').mean()
|
||
long_mavg = history(300, '1d', 'price').mean()
|
||
|
||
# Trading logic
|
||
if short_mavg[0] > long_mavg[0]:
|
||
# order_target orders as many shares as needed to
|
||
# achieve the desired number of shares.
|
||
order_target(symbol('AAPL'), 100)
|
||
elif short_mavg[0] < long_mavg[0]:
|
||
order_target(symbol('AAPL'), 0)
|
||
|
||
# Save values for later inspection
|
||
record(AAPL=data[symbol('AAPL')].price,
|
||
short_mavg=short_mavg[0],
|
||
long_mavg=long_mavg[0])
|
||
|
||
|
||
def analyze(context, perf):
|
||
fig = plt.figure()
|
||
ax1 = fig.add_subplot(211)
|
||
perf.portfolio_value.plot(ax=ax1)
|
||
ax1.set_ylabel('portfolio value in $')
|
||
|
||
ax2 = fig.add_subplot(212)
|
||
perf['AAPL'].plot(ax=ax2)
|
||
perf[['short_mavg', 'long_mavg']].plot(ax=ax2)
|
||
|
||
perf_trans = perf.ix[[t != [] for t in perf.transactions]]
|
||
buys = perf_trans.ix[[t[0]['amount'] > 0 for t in perf_trans.transactions]]
|
||
sells = perf_trans.ix[
|
||
[t[0]['amount'] < 0 for t in perf_trans.transactions]]
|
||
ax2.plot(buys.index, perf.short_mavg.ix[buys.index],
|
||
'^', markersize=10, color='m')
|
||
ax2.plot(sells.index, perf.short_mavg.ix[sells.index],
|
||
'v', markersize=10, color='k')
|
||
ax2.set_ylabel('price in $')
|
||
plt.legend(loc=0)
|
||
plt.show()
|
||
|
||
.. image:: tutorial_files/tutorial_22_1.png
|
||
|
||
Here we are explicitly defining an ``analyze()`` function that gets
|
||
automatically called once the backtest is done (this is not possible on
|
||
Quantopian currently).
|
||
|
||
Although it might not be directly apparent, the power of ``history()``
|
||
(pun intended) can not be under-estimated as most algorithms make use of
|
||
prior market developments in one form or another. You could easily
|
||
devise a strategy that trains a classifier with
|
||
`scikit-learn <http://scikit-learn.org/stable/>`__ which tries to
|
||
predict future market movements based on past prices (note, that most of
|
||
the ``scikit-learn`` functions require ``numpy.ndarray``\ s rather than
|
||
``pandas.DataFrame``\ s, so you can simply pass the underlying
|
||
``ndarray`` of a ``DataFrame`` via ``.values``).
|
||
|
||
We also used the ``order_target()`` function above. This and other
|
||
functions like it can make order management and portfolio rebalancing
|
||
much easier. See the `Quantopian documentation on order
|
||
functions <https://www.quantopian.com/help#api-order-methods>`__ fore
|
||
more details.
|
||
|
||
Conclusions
|
||
~~~~~~~~~~~
|
||
|
||
We hope that this tutorial gave you a little insight into the
|
||
architecture, API, and features of ``zipline``. For next steps, check
|
||
out some of the
|
||
`examples <https://github.com/quantopian/zipline/tree/master/zipline/examples>`__.
|
||
|
||
Feel free to ask questions on `our mailing
|
||
list <https://groups.google.com/forum/#!forum/zipline>`__, report
|
||
problems on our `GitHub issue
|
||
tracker <https://github.com/quantopian/zipline/issues?state=open>`__,
|
||
`get
|
||
involved <https://github.com/quantopian/zipline/wiki/Contribution-Requests>`__,
|
||
and `checkout Quantopian <https://quantopian.com>`__.
|