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<div class="section" id="zipline-beginner-tutorial">
<h1>Zipline Beginner Tutorial<a class="headerlink" href="#zipline-beginner-tutorial" title="Permalink to this headline"></a></h1>
<div class="section" id="basics">
<h2>Basics<a class="headerlink" href="#basics" title="Permalink to this headline"></a></h2>
<p>Zipline is an open-source algorithmic trading simulator written in
Python.</p>
<p>The source can be found at: <a class="reference external" href="https://github.com/quantopian/zipline">https://github.com/quantopian/zipline</a></p>
<p>Some benefits include:</p>
<ul class="simple">
<li>Realistic: slippage, transaction costs, order delays.</li>
<li>Stream-based: Process each event individually, avoids look-ahead
bias.</li>
<li>Batteries included: Common transforms (moving average) as well as
common risk calculations (Sharpe).</li>
<li>Developed and continuously updated by
<a class="reference external" href="https://www.quantopian.com">Quantopian</a> which provides an
easy-to-use web-interface to Zipline, 10 years of minute-resolution
historical US stock data, and live-trading capabilities. This
tutorial is directed at users wishing to use Zipline without using
Quantopian. If you instead want to get started on Quantopian, see
<a class="reference external" href="https://www.quantopian.com/faq#get-started">here</a>.</li>
</ul>
<p>This tutorial assumes that you have zipline correctly installed, see the
<a class="reference external" href="https://github.com/quantopian/zipline#installation">installation
instructions</a> if
you haven&#8217;t set up zipline yet.</p>
<p>Every <code class="docutils literal"><span class="pre">zipline</span></code> algorithm consists of two functions you have to
define:</p>
<ul class="simple">
<li><code class="docutils literal"><span class="pre">initialize(context)</span></code></li>
<li><code class="docutils literal"><span class="pre">handle_data(context,</span> <span class="pre">data)</span></code></li>
</ul>
<p>Before the start of the algorithm, <code class="docutils literal"><span class="pre">zipline</span></code> calls the
<code class="docutils literal"><span class="pre">initialize()</span></code> function and passes in a <code class="docutils literal"><span class="pre">context</span></code> variable.
<code class="docutils literal"><span class="pre">context</span></code> is a persistent namespace for you to store variables you
need to access from one algorithm iteration to the next.</p>
<p>After the algorithm has been initialized, <code class="docutils literal"><span class="pre">zipline</span></code> calls the
<code class="docutils literal"><span class="pre">handle_data()</span></code> function once for each event. At every call, it passes
the same <code class="docutils literal"><span class="pre">context</span></code> variable and an event-frame called <code class="docutils literal"><span class="pre">data</span></code>
containing the current trading bar with open, high, low, and close
(OHLC) prices as well as volume for each stock in your universe. For
more information on these functions, see the <a class="reference external" href="https://www.quantopian.com/help#api-toplevel">relevant part of the
Quantopian docs</a>.</p>
</div>
<div class="section" id="my-first-algorithm">
<h2>My first algorithm<a class="headerlink" href="#my-first-algorithm" title="Permalink to this headline"></a></h2>
<p>Lets take a look at a very simple algorithm from the <code class="docutils literal"><span class="pre">examples</span></code>
directory, <code class="docutils literal"><span class="pre">buyapple.py</span></code>:</p>
<div class="highlight-python"><div class="highlight"><pre>from zipline.examples import buyapple
buyapple??
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">zipline.api</span> <span class="kn">import</span> <span class="n">order</span><span class="p">,</span> <span class="n">record</span><span class="p">,</span> <span class="n">symbol</span>
<span class="k">def</span> <span class="nf">initialize</span><span class="p">(</span><span class="n">context</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">def</span> <span class="nf">handle_data</span><span class="p">(</span><span class="n">context</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
<span class="n">order</span><span class="p">(</span><span class="n">symbol</span><span class="p">(</span><span class="s">&#39;AAPL&#39;</span><span class="p">),</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">record</span><span class="p">(</span><span class="n">AAPL</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">current</span><span class="p">(</span><span class="n">symbol</span><span class="p">(</span><span class="s">&#39;AAPL&#39;</span><span class="p">),</span> <span class="s">&#39;price&#39;</span><span class="p">))</span>
</pre></div>
</div>
<p>As you can see, we first have to import some functions we would like to
use. All functions commonly used in your algorithm can be found in
<code class="docutils literal"><span class="pre">zipline.api</span></code>. Here we are using <code class="xref py py-func docutils literal"><span class="pre">order()</span></code> which takes two
arguments: a security object, and a number specifying how many stocks you would
like to order (if negative, <code class="xref py py-func docutils literal"><span class="pre">order()</span></code> will sell/short
stocks). In this case we want to order 10 shares of Apple at each iteration. For
more documentation on <code class="docutils literal"><span class="pre">order()</span></code>, see the <a class="reference external" href="https://www.quantopian.com/help#api-order">Quantopian docs</a>.</p>
<p>Finally, the <code class="xref py py-func docutils literal"><span class="pre">record()</span></code> function allows you to save the value
of a variable at each iteration. You provide it with a name for the variable
together with the variable itself: <code class="docutils literal"><span class="pre">varname=var</span></code>. After the algorithm
finished running you will have access to each variable value you tracked
with <code class="xref py py-func docutils literal"><span class="pre">record()</span></code> under the name you provided (we will see this
further below). You also see how we can access the current price data of the
AAPL stock in the <code class="docutils literal"><span class="pre">data</span></code> event frame (for more information see
<a class="reference external" href="https://www.quantopian.com/help#api-event-properties">here</a>.</p>
</div>
<div class="section" id="running-the-algorithm">
<h2>Running the algorithm<a class="headerlink" href="#running-the-algorithm" title="Permalink to this headline"></a></h2>
<p>To now test this algorithm on financial data, <code class="docutils literal"><span class="pre">zipline</span></code> provides three
interfaces: A command-line interface, <code class="docutils literal"><span class="pre">IPython</span> <span class="pre">Notebook</span></code> magic, and
<code class="xref py py-func docutils literal"><span class="pre">run_algorithm()</span></code>.</p>
<div class="section" id="ingesting-data">
<h3>Ingesting Data<a class="headerlink" href="#ingesting-data" title="Permalink to this headline"></a></h3>
<p>If you haven&#8217;t ingested the data, run:</p>
<div class="highlight-bash"><div class="highlight"><pre><span class="nv">$ </span>zipline ingest <span class="o">[</span>-b &lt;bundle&gt;<span class="o">]</span>
</pre></div>
</div>
<p>where <code class="docutils literal"><span class="pre">&lt;bundle&gt;</span></code> is the name of the bundle to ingest, defaulting to
<a class="reference internal" href="bundles.html#quantopian-quandl-mirror"><span>quantopian-quandl</span></a>.</p>
<p>you can check out the <a class="reference internal" href="bundles.html#ingesting-data"><span>ingesting data</span></a> section for
more detail.</p>
</div>
<div class="section" id="command-line-interface">
<h3>Command line interface<a class="headerlink" href="#command-line-interface" title="Permalink to this headline"></a></h3>
<p>After you installed zipline you should be able to execute the following
from your command line (e.g. <code class="docutils literal"><span class="pre">cmd.exe</span></code> on Windows, or the Terminal app
on OSX):</p>
<div class="highlight-bash"><div class="highlight"><pre><span class="nv">$ </span>zipline run --help
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre>Usage: zipline run [OPTIONS]
Run a backtest for the given algorithm.
Options:
-f, --algofile FILENAME The file that contains the algorithm to run.
-t, --algotext TEXT The algorithm script to run.
-D, --define TEXT Define a name to be bound in the namespace
before executing the algotext. For example
&#39;-Dname=value&#39;. The value may be any python
expression. These are evaluated in order so
they may refer to previously defined names.
--data-frequency [minute|daily]
The data frequency of the simulation.
[default: daily]
--capital-base FLOAT The starting capital for the simulation.
[default: 10000000.0]
-b, --bundle BUNDLE-NAME The data bundle to use for the simulation.
[default: quantopian-quandl]
--bundle-timestamp TIMESTAMP The date to lookup data on or before.
[default: &lt;current-time&gt;]
-s, --start DATE The start date of the simulation.
-e, --end DATE The end date of the simulation.
-o, --output FILENAME The location to write the perf data. If this
is &#39;-&#39; the perf will be written to stdout.
[default: -]
--print-algo / --no-print-algo Print the algorithm to stdout.
--help Show this message and exit.
</pre></div>
</div>
<p>As you can see there are a couple of flags that specify where to find your
algorithm (<code class="docutils literal"><span class="pre">-f</span></code>) as well as parameters specifying which data to use,
defaulting to the <a class="reference internal" href="bundles.html#quantopian-quandl-mirror"><span>Quantopian Quandl WIKI Mirror</span></a>. There are also arguments for
the date range to run the algorithm over (<code class="docutils literal"><span class="pre">--start</span></code> and <code class="docutils literal"><span class="pre">--end</span></code>). Finally,
you&#8217;ll want to save the performance metrics of your algorithm so that you can
analyze how it performed. This is done via the <code class="docutils literal"><span class="pre">--output</span></code> flag and will cause
it to write the performance <code class="docutils literal"><span class="pre">DataFrame</span></code> in the pickle Python file format.
Note that you can also define a configuration file with these parameters that
you can then conveniently pass to the <code class="docutils literal"><span class="pre">-c</span></code> option so that you don&#8217;t have to
supply the command line args all the time (see the .conf files in the examples
directory).</p>
<p>Thus, to execute our algorithm from above and save the results to
<code class="docutils literal"><span class="pre">buyapple_out.pickle</span></code> we would call <code class="docutils literal"><span class="pre">zipline</span> <span class="pre">run</span></code> as follows:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="n">zipline</span> <span class="n">run</span> <span class="o">-</span><span class="n">f</span> <span class="o">../../</span><span class="n">zipline</span><span class="o">/</span><span class="n">examples</span><span class="o">/</span><span class="n">buyapple</span><span class="o">.</span><span class="n">py</span> <span class="o">--</span><span class="n">start</span> <span class="mi">2000</span><span class="o">-</span><span class="mi">1</span><span class="o">-</span><span class="mi">1</span> <span class="o">--</span><span class="n">end</span> <span class="mi">2014</span><span class="o">-</span><span class="mi">1</span><span class="o">-</span><span class="mi">1</span> <span class="o">-</span><span class="n">o</span> <span class="n">buyapple_out</span><span class="o">.</span><span class="n">pickle</span>
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre>AAPL
[2015-11-04 22:45:32.820166] INFO: Performance: Simulated 3521 trading days out of 3521.
[2015-11-04 22:45:32.820314] INFO: Performance: first open: 2000-01-03 14:31:00+00:00
[2015-11-04 22:45:32.820401] INFO: Performance: last close: 2013-12-31 21:00:00+00:00
</pre></div>
</div>
<p><code class="docutils literal"><span class="pre">run</span></code> first calls the <code class="docutils literal"><span class="pre">initialize()</span></code> function, and then
streams the historical stock price day-by-day through <code class="docutils literal"><span class="pre">handle_data()</span></code>.
After each call to <code class="docutils literal"><span class="pre">handle_data()</span></code> we instruct <code class="docutils literal"><span class="pre">zipline</span></code> to order 10
stocks of AAPL. After the call of the <code class="docutils literal"><span class="pre">order()</span></code> function, <code class="docutils literal"><span class="pre">zipline</span></code>
enters the ordered stock and amount in the order book. After the
<code class="docutils literal"><span class="pre">handle_data()</span></code> function has finished, <code class="docutils literal"><span class="pre">zipline</span></code> looks for any open
orders and tries to fill them. If the trading volume is high enough for
this stock, the order is executed after adding the commission and
applying the slippage model which models the influence of your order on
the stock price, so your algorithm will be charged more than just the
stock price * 10. (Note, that you can also change the commission and
slippage model that <code class="docutils literal"><span class="pre">zipline</span></code> uses, see the <a class="reference external" href="https://www.quantopian.com/help#ide-slippage">Quantopian
docs</a> for more
information).</p>
<p>Lets take a quick look at the performance <code class="docutils literal"><span class="pre">DataFrame</span></code>. For this, we
use <code class="docutils literal"><span class="pre">pandas</span></code> from inside the IPython Notebook and print the first ten
rows. Note that <code class="docutils literal"><span class="pre">zipline</span></code> makes heavy usage of <code class="docutils literal"><span class="pre">pandas</span></code>, especially
for data input and outputting so it&#8217;s worth spending some time to learn
it.</p>
<div class="highlight-python"><div class="highlight"><pre><span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
<span class="n">perf</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_pickle</span><span class="p">(</span><span class="s">&#39;buyapple_out.pickle&#39;</span><span class="p">)</span> <span class="c"># read in perf DataFrame</span>
<span class="n">perf</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
</div>
<div style="max-height:1000px;max-width:1500px;overflow:auto;">
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>AAPL</th>
<th>algo_volatility</th>
<th>algorithm_period_return</th>
<th>alpha</th>
<th>benchmark_period_return</th>
<th>benchmark_volatility</th>
<th>beta</th>
<th>capital_used</th>
<th>ending_cash</th>
<th>ending_exposure</th>
<th>...</th>
<th>short_exposure</th>
<th>short_value</th>
<th>shorts_count</th>
<th>sortino</th>
<th>starting_cash</th>
<th>starting_exposure</th>
<th>starting_value</th>
<th>trading_days</th>
<th>transactions</th>
<th>treasury_period_return</th>
</tr>
</thead>
<tbody>
<tr>
<th>2000-01-03 21:00:00</th>
<td>3.738314</td>
<td>0.000000e+00</td>
<td>0.000000e+00</td>
<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'order_id': u'513357725cb64a539e3dd02b47da7...</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'order_id': u'd7d4ad03cfec4d578c0d817dc3829...</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'order_id': u'1fbf5e9bfd7c4d9cb2e8383e1085e...</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'order_id': u'9ea6b142ff09466b9113331a37437...</td>
<td>0.0652</td>
</tr>
</tbody>
</table>
<p>5 rows × 39 columns</p>
</div><p>As you can see, there is a row for each trading day, starting on the
first business day of 2000. In the columns you can find various
information about the state of your algorithm. The very first column
<code class="docutils literal"><span class="pre">AAPL</span></code> was placed there by the <code class="docutils literal"><span class="pre">record()</span></code> function mentioned earlier
and allows us to plot the price of apple. For example, we could easily
examine now how our portfolio value changed over time compared to the
AAPL stock price.</p>
<div class="highlight-python"><div class="highlight"><pre><span class="o">%</span><span class="n">pylab</span> <span class="n">inline</span>
<span class="n">figsize</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span> <span class="mi">12</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">211</span><span class="p">)</span>
<span class="n">perf</span><span class="o">.</span><span class="n">portfolio_value</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s">&#39;portfolio value&#39;</span><span class="p">)</span>
<span class="n">ax2</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">212</span><span class="p">,</span> <span class="n">sharex</span><span class="o">=</span><span class="n">ax1</span><span class="p">)</span>
<span class="n">perf</span><span class="o">.</span><span class="n">AAPL</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s">&#39;AAPL stock price&#39;</span><span class="p">)</span>
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre>Populating the interactive namespace from numpy and matplotlib
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre>&lt;matplotlib.text.Text at 0x7ff5c6147f90&gt;
</pre></div>
</div>
<img alt="_images/tutorial_11_2.png" src="_images/tutorial_11_2.png" />
<p>As you can see, our algorithm performance as assessed by the
<code class="docutils literal"><span class="pre">portfolio_value</span></code> closely matches that of the AAPL stock price. This
is not surprising as our algorithm only bought AAPL every chance it got.</p>
</div>
</div>
<div class="section" id="ipython-notebook">
<h2>IPython Notebook<a class="headerlink" href="#ipython-notebook" title="Permalink to this headline"></a></h2>
<p>The <a class="reference external" href="http://ipython.org/notebook.html">IPython Notebook</a> is a very
powerful browser-based interface to a Python interpreter (this tutorial
was written in it). As it is already the de-facto interface for most
quantitative researchers <code class="docutils literal"><span class="pre">zipline</span></code> provides an easy way to run your
algorithm inside the Notebook without requiring you to use the CLI.</p>
<p>To use it you have to write your algorithm in a cell and let <code class="docutils literal"><span class="pre">zipline</span></code>
know that it is supposed to run this algorithm. This is done via the
<code class="docutils literal"><span class="pre">%%zipline</span></code> IPython magic command that is available after you
<code class="docutils literal"><span class="pre">import</span> <span class="pre">zipline</span></code> from within the IPython Notebook. This magic takes
the same arguments as the command line interface described above. Thus
to run the algorithm from above with the same parameters we just have to
execute the following cell after importing <code class="docutils literal"><span class="pre">zipline</span></code> to register the
magic.</p>
<div class="highlight-python"><div class="highlight"><pre><span class="o">%</span><span class="n">load_ext</span> <span class="n">zipline</span>
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre><span class="o">%%</span><span class="n">zipline</span> <span class="o">--</span><span class="n">start</span> <span class="mi">2000</span><span class="o">-</span><span class="mi">1</span><span class="o">-</span><span class="mi">1</span> <span class="o">--</span><span class="n">end</span> <span class="mi">2014</span><span class="o">-</span><span class="mi">1</span><span class="o">-</span><span class="mi">1</span>
<span class="kn">from</span> <span class="nn">zipline.api</span> <span class="kn">import</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">order</span><span class="p">,</span> <span class="n">record</span>
<span class="k">def</span> <span class="nf">initialize</span><span class="p">(</span><span class="n">context</span><span class="p">):</span>
<span class="k">pass</span>
<span class="k">def</span> <span class="nf">handle_data</span><span class="p">(</span><span class="n">context</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
<span class="n">order</span><span class="p">(</span><span class="n">symbol</span><span class="p">(</span><span class="s">&#39;AAPL&#39;</span><span class="p">),</span> <span class="mi">10</span><span class="p">)</span>
<span class="n">record</span><span class="p">(</span><span class="n">AAPL</span><span class="o">=</span><span class="n">data</span><span class="p">[</span><span class="n">symbol</span><span class="p">(</span><span class="s">&#39;AAPL&#39;</span><span class="p">)]</span><span class="o">.</span><span class="n">price</span><span class="p">)</span>
</pre></div>
</div>
<p>Note that we did not have to specify an input file as above since the
magic will use the contents of the cell and look for your algorithm
functions there. Also, instead of defining an output file we are
specifying a variable name with <code class="docutils literal"><span class="pre">-o</span></code> that will be created in the name
space and contain the performance <code class="docutils literal"><span class="pre">DataFrame</span></code> we looked at above.</p>
<div class="highlight-python"><div class="highlight"><pre><span class="n">_</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
</div>
<div style="max-height:1000px;max-width:1500px;overflow:auto;">
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>AAPL</th>
<th>algo_volatility</th>
<th>algorithm_period_return</th>
<th>alpha</th>
<th>benchmark_period_return</th>
<th>benchmark_volatility</th>
<th>beta</th>
<th>capital_used</th>
<th>ending_cash</th>
<th>ending_exposure</th>
<th>...</th>
<th>short_exposure</th>
<th>short_value</th>
<th>shorts_count</th>
<th>sortino</th>
<th>starting_cash</th>
<th>starting_exposure</th>
<th>starting_value</th>
<th>trading_days</th>
<th>transactions</th>
<th>treasury_period_return</th>
</tr>
</thead>
<tbody>
<tr>
<th>2000-01-03 21:00:00</th>
<td>3.738314</td>
<td>0.000000e+00</td>
<td>0.000000e+00</td>
<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></div>
<div class="section" id="access-to-previous-prices-using-history">
<h2>Access to previous prices using <code class="docutils literal"><span class="pre">history</span></code><a class="headerlink" href="#access-to-previous-prices-using-history" title="Permalink to this headline"></a></h2>
<div class="section" id="working-example-dual-moving-average-cross-over">
<h3>Working example: Dual Moving Average Cross-Over<a class="headerlink" href="#working-example-dual-moving-average-cross-over" title="Permalink to this headline"></a></h3>
<p>The Dual Moving Average (DMA) is a classic momentum strategy. It&#8217;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) &#8211; 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.</p>
<p>As we need to have access to previous prices to implement this strategy
we need a new concept: History</p>
<p><code class="docutils literal"><span class="pre">data.history()</span></code> 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 <code class="docutils literal"><span class="pre">'1d'</span></code> for <code class="docutils literal"><span class="pre">'1m'</span></code>
but note that you need to have minute-level data for using <code class="docutils literal"><span class="pre">1m</span></code>). For
a more detailed description <code class="docutils literal"><span class="pre">history()</span></code>&#8216;s features, see the
<a class="reference external" href="https://www.quantopian.com/help#ide-history">Quantopian docs</a>.
Let&#8217;s look at the strategy which should make this clear:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="o">%%</span><span class="n">zipline</span> <span class="o">--</span><span class="n">start</span> <span class="mi">2000</span><span class="o">-</span><span class="mi">1</span><span class="o">-</span><span class="mi">1</span> <span class="o">--</span><span class="n">end</span> <span class="mi">2012</span><span class="o">-</span><span class="mi">1</span><span class="o">-</span><span class="mi">1</span> <span class="o">-</span><span class="n">o</span> <span class="n">dma</span><span class="o">.</span><span class="n">pickle</span>
<span class="kn">from</span> <span class="nn">zipline.api</span> <span class="kn">import</span> <span class="n">order_target</span><span class="p">,</span> <span class="n">record</span><span class="p">,</span> <span class="n">symbol</span>
<span class="k">def</span> <span class="nf">initialize</span><span class="p">(</span><span class="n">context</span><span class="p">):</span>
<span class="n">context</span><span class="o">.</span><span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">context</span><span class="o">.</span><span class="n">asset</span> <span class="o">=</span> <span class="n">symbol</span><span class="p">(</span><span class="s">&#39;AAPL&#39;</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">handle_data</span><span class="p">(</span><span class="n">context</span><span class="p">,</span> <span class="n">data</span><span class="p">):</span>
<span class="c"># Skip first 300 days to get full windows</span>
<span class="n">context</span><span class="o">.</span><span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="k">if</span> <span class="n">context</span><span class="o">.</span><span class="n">i</span> <span class="o">&lt;</span> <span class="mi">300</span><span class="p">:</span>
<span class="k">return</span>
<span class="c"># Compute averages</span>
<span class="c"># data.history() has to be called with the same params</span>
<span class="c"># from above and returns a pandas dataframe.</span>
<span class="n">short_mavg</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">history</span><span class="p">(</span><span class="n">context</span><span class="o">.</span><span class="n">asset</span><span class="p">,</span> <span class="s">&#39;price&#39;</span><span class="p">,</span> <span class="n">bar_count</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">frequency</span><span class="o">=</span><span class="s">&quot;1d&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="n">long_mavg</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">history</span><span class="p">(</span><span class="n">context</span><span class="o">.</span><span class="n">asset</span><span class="p">,</span> <span class="s">&#39;price&#39;</span><span class="p">,</span> <span class="n">bar_count</span><span class="o">=</span><span class="mi">300</span><span class="p">,</span> <span class="n">frequency</span><span class="o">=</span><span class="s">&quot;1d&quot;</span><span class="p">)</span><span class="o">.</span><span class="n">mean</span><span class="p">()</span>
<span class="c"># Trading logic</span>
<span class="k">if</span> <span class="n">short_mavg</span> <span class="o">&gt;</span> <span class="n">long_mavg</span><span class="p">:</span>
<span class="c"># order_target orders as many shares as needed to</span>
<span class="c"># achieve the desired number of shares.</span>
<span class="n">order_target</span><span class="p">(</span><span class="n">context</span><span class="o">.</span><span class="n">asset</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
<span class="k">elif</span> <span class="n">short_mavg</span> <span class="o">&lt;</span> <span class="n">long_mavg</span><span class="p">:</span>
<span class="n">order_target</span><span class="p">(</span><span class="n">context</span><span class="o">.</span><span class="n">asset</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="c"># Save values for later inspection</span>
<span class="n">record</span><span class="p">(</span><span class="n">AAPL</span><span class="o">=</span><span class="n">data</span><span class="o">.</span><span class="n">current</span><span class="p">(</span><span class="n">context</span><span class="o">.</span><span class="n">asset</span><span class="p">,</span> <span class="s">&#39;price&#39;</span><span class="p">),</span>
<span class="n">short_mavg</span><span class="o">=</span><span class="n">short_mavg</span><span class="p">,</span>
<span class="n">long_mavg</span><span class="o">=</span><span class="n">long_mavg</span><span class="p">)</span>
<span class="k">def</span> <span class="nf">analyze</span><span class="p">(</span><span class="n">context</span><span class="p">,</span> <span class="n">perf</span><span class="p">):</span>
<span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">ax1</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">211</span><span class="p">)</span>
<span class="n">perf</span><span class="o">.</span><span class="n">portfolio_value</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax1</span><span class="p">)</span>
<span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s">&#39;portfolio value in $&#39;</span><span class="p">)</span>
<span class="n">ax2</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">212</span><span class="p">)</span>
<span class="n">perf</span><span class="p">[</span><span class="s">&#39;AAPL&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">)</span>
<span class="n">perf</span><span class="p">[[</span><span class="s">&#39;short_mavg&#39;</span><span class="p">,</span> <span class="s">&#39;long_mavg&#39;</span><span class="p">]]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">ax</span><span class="o">=</span><span class="n">ax2</span><span class="p">)</span>
<span class="n">perf_trans</span> <span class="o">=</span> <span class="n">perf</span><span class="o">.</span><span class="n">ix</span><span class="p">[[</span><span class="n">t</span> <span class="o">!=</span> <span class="p">[]</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">perf</span><span class="o">.</span><span class="n">transactions</span><span class="p">]]</span>
<span class="n">buys</span> <span class="o">=</span> <span class="n">perf_trans</span><span class="o">.</span><span class="n">ix</span><span class="p">[[</span><span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s">&#39;amount&#39;</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">perf_trans</span><span class="o">.</span><span class="n">transactions</span><span class="p">]]</span>
<span class="n">sells</span> <span class="o">=</span> <span class="n">perf_trans</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span>
<span class="p">[</span><span class="n">t</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="s">&#39;amount&#39;</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">0</span> <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="n">perf_trans</span><span class="o">.</span><span class="n">transactions</span><span class="p">]]</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">buys</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">perf</span><span class="o">.</span><span class="n">short_mavg</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">buys</span><span class="o">.</span><span class="n">index</span><span class="p">],</span>
<span class="s">&#39;^&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">&#39;m&#39;</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">sells</span><span class="o">.</span><span class="n">index</span><span class="p">,</span> <span class="n">perf</span><span class="o">.</span><span class="n">short_mavg</span><span class="o">.</span><span class="n">ix</span><span class="p">[</span><span class="n">sells</span><span class="o">.</span><span class="n">index</span><span class="p">],</span>
<span class="s">&#39;v&#39;</span><span class="p">,</span> <span class="n">markersize</span><span class="o">=</span><span class="mi">10</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s">&#39;k&#39;</span><span class="p">)</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s">&#39;price in $&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</pre></div>
</div>
<img alt="_images/tutorial_22_1.png" src="_images/tutorial_22_1.png" />
<p>Here we are explicitly defining an <code class="docutils literal"><span class="pre">analyze()</span></code> function that gets
automatically called once the backtest is done (this is not possible on
Quantopian currently).</p>
<p>Although it might not be directly apparent, the power of <code class="docutils literal"><span class="pre">history()</span></code>
(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
<a class="reference external" href="http://scikit-learn.org/stable/">scikit-learn</a> which tries to
predict future market movements based on past prices (note, that most of
the <code class="docutils literal"><span class="pre">scikit-learn</span></code> functions require <code class="docutils literal"><span class="pre">numpy.ndarray</span></code>s rather than
<code class="docutils literal"><span class="pre">pandas.DataFrame</span></code>s, so you can simply pass the underlying
<code class="docutils literal"><span class="pre">ndarray</span></code> of a <code class="docutils literal"><span class="pre">DataFrame</span></code> via <code class="docutils literal"><span class="pre">.values</span></code>).</p>
<p>We also used the <code class="docutils literal"><span class="pre">order_target()</span></code> function above. This and other
functions like it can make order management and portfolio rebalancing
much easier. See the <a class="reference external" href="https://www.quantopian.com/help#api-order-methods">Quantopian documentation on order
functions</a> fore
more details.</p>
</div>
</div>
<div class="section" id="conclusions">
<h2>Conclusions<a class="headerlink" href="#conclusions" title="Permalink to this headline"></a></h2>
<p>We hope that this tutorial gave you a little insight into the
architecture, API, and features of <code class="docutils literal"><span class="pre">zipline</span></code>. For next steps, check
out some of the
<a class="reference external" href="https://github.com/quantopian/zipline/tree/master/zipline/examples">examples</a>.</p>
<p>Feel free to ask questions on <a class="reference external" href="https://groups.google.com/forum/#!forum/zipline">our mailing
list</a>, report
problems on our <a class="reference external" href="https://github.com/quantopian/zipline/issues?state=open">GitHub issue
tracker</a>,
<a class="reference external" href="https://github.com/quantopian/zipline/wiki/Contribution-Requests">get
involved</a>,
and <a class="reference external" href="https://quantopian.com">checkout Quantopian</a>.</p>
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