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DOC: jupyter notebook in beginner tutorial

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Victor Grau Serrat
2017-10-20 14:51:35 -06:00
parent cd7e254e0c
commit d43d5821ef
3 changed files with 485 additions and 47 deletions
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_sources/beginner-tutorial.txt
+235 -15
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@@ -52,7 +52,7 @@ My first algorithm
~~~~~~~~~~~~~~~~~~
Lets take a look at a very simple algorithm from the ``examples``
directory, ``buy_btc.py``:
directory: `buy_btc_simple.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/buy_btc_simple.py>`_:
.. code-block:: python
@@ -225,16 +225,16 @@ Thus, to execute our algorithm from above and save the results to
.. code-block:: python
catalyst run -f buy_btc_simple.py -x bitfinex --start 2016-1-1 --end 2016-9-29 -o buy_simple_btc_out.pickle
catalyst run -f buy_btc_simple.py -x bitfinex --start 2016-1-1 --end 2017-9-30 -o buy_btc_simple_out.pickle
..
.. parsed-literal
.. parsed-literal::
.. 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
INFO: run_algo: running algo in backtest mode
INFO: exchange_algorithm: initialized trading algorithm in backtest mode
INFO: Performance: Simulated 639 trading days out of 639.
INFO: Performance: first open: 2016-01-01 00:00:00+00:00
INFO: Performance: last close: 2017-09-30 23:59:00+00:00
``run`` first calls the ``initialize()`` function, and then
@@ -255,7 +255,7 @@ slippage model that ``catalyst`` uses).
Let's take a quick look at the performance ``DataFrame``. For this, we
use ``pandas`` from inside the IPython Notebook and print the first ten
rows. Note that ``catalyst`` makes heavy usage of
rows. and print the first ten rows. Note that ``catalyst`` makes heavy usage of
`pandas <http://pandas.pydata.org/>`_, especially for data input and
outputting so it's worth spending some time to learn it.
@@ -265,17 +265,196 @@ outputting so it's worth spending some time to learn it.
perf = pd.read_pickle('buy_btc_simple_out.pickle') # read in perf DataFrame
perf.head()
.. raw:: html
<div style="max-height:1000px;max-width:1500px;overflow:auto;">
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></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>btc</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>2016-01-01 23:59:00+00:00</th>
<td>NaN</td>
<td>0.000000e+00</td>
<td>NaN</td>
<td>-0.010937</td>
<td>NaN</td>
<td>NaN</td>
<td>433.979999</td>
<td>0.000000</td>
<td>1.000000e+07</td>
<td>0.00</td>
<td>...</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>NaN</td>
<td>1.000000e+07</td>
<td>0.00</td>
<td>0.00</td>
<td>1</td>
<td>[]</td>
<td>0.0227</td>
</tr>
<tr>
<th>2016-01-02 23:59:00+00:00</th>
<td>0.000011</td>
<td>-9.536708e-07</td>
<td>-0.000170</td>
<td>-0.006480</td>
<td>0.173338</td>
<td>-0.000062</td>
<td>432.700000</td>
<td>-442.236708</td>
<td>9.999558e+06</td>
<td>432.70</td>
<td>...</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>-11.224972</td>
<td>1.000000e+07</td>
<td>0.00</td>
<td>0.00</td>
<td>2</td>
<td>[{u'order_id': u'7869f7828fa140328eb40477bb7de...</td>
<td>0.0227</td>
</tr>
<tr>
<th>2016-01-03 23:59:00+00:00</th>
<td>0.000011</td>
<td>-2.328842e-06</td>
<td>-0.000176</td>
<td>-0.026512</td>
<td>0.197857</td>
<td>0.000009</td>
<td>428.390000</td>
<td>-437.831716</td>
<td>9.999120e+06</td>
<td>856.78</td>
<td>...</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>-12.754262</td>
<td>9.999558e+06</td>
<td>432.70</td>
<td>432.70</td>
<td>3</td>
<td>[{u'order_id': u'be62ff77760c4599abaac43be9cc9...</td>
<td>0.0227</td>
</tr>
<tr>
<th>2016-01-04 23:59:00+00:00</th>
<td>0.000011</td>
<td>-2.380954e-06</td>
<td>-0.000139</td>
<td>-0.008640</td>
<td>0.269790</td>
<td>0.000020</td>
<td>432.900000</td>
<td>-442.441116</td>
<td>9.998677e+06</td>
<td>1298.70</td>
<td>...</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>-11.287205</td>
<td>9.999120e+06</td>
<td>856.78</td>
<td>856.78</td>
<td>4</td>
<td>[{u'order_id': u'd6dca79513214346a646079213526...</td>
<td>0.0224</td>
</tr>
<tr>
<th>2016-01-05 23:59:00+00:00</th>
<td>0.000011</td>
<td>-3.650729e-06</td>
<td>-0.000158</td>
<td>-0.021426</td>
<td>0.245989</td>
<td>0.000024</td>
<td>431.840000</td>
<td>-441.357754</td>
<td>9.998236e+06</td>
<td>1727.36</td>
<td>...</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>-12.333847</td>
<td>9.998677e+06</td>
<td>1298.70</td>
<td>1298.70</td>
<td>5</td>
<td>[{u'order_id': u'505275d6646a41f3856b22b16678d...</td>
<td>0.0225</td>
</tr>
</tbody>
</table>
</div>
|
There is a row for each trading day, starting on the first day of our
simulation Jan 1st, 2016. In the columns you can find various
information about the state of your algorithm. The very first column
information about the state of your algorithm. The column
``btc`` was placed there by the ``record()`` function mentioned earlier
and allows us to plot the price of bitcoin. For example, we could easily
examine now how our portfolio value changed over time compared to the
bitcoin price.
Our algorithm performance as assessed by the
``portfolio_value`` closely matches that of the bitcoin price. This
is not surprising as our algorithm only bought bitcoin every chance it got.
.. code-block:: python
%pylab inline
figsize(12, 12)
import matplotlib.pyplot as plt
ax1 = plt.subplot(211)
perf.portfolio_value.plot(ax=ax1)
ax1.set_ylabel('portfolio value')
ax2 = plt.subplot(212, sharex=ax1)
perf.btc.plot(ax=ax2)
ax2.set_ylabel('bitcoin price')
.. parsed-literal::
Populating the interactive namespace from numpy and matplotlib
.. parsed-literal::
<matplotlib.text.Text at 0x10eaeadd0>
.. image:: https://s3.amazonaws.com/enigmaco-docs/github.io/buy_btc_simple_graph.png
Our algorithm performance as assessed by the ``portfolio_value`` closely
matches that of the bitcoin price. This is not surprising as our algorithm
only bought bitcoin every chance it got.
Access to previous prices using ``history``
@@ -305,13 +484,14 @@ a function we use in the ``handle_data()`` section:
.. code-block:: python
from catalyst.api import order, record, symbol
%%catalyst --start 2016-1-1 --end 2017-9-30 -x bitfinex -o dma.pickle
from catalyst.api import order, record, symbol, order_target
def initialize(context):
context.i = 0
context.asset = symbol('btc_usd')
def handle_data(context, data):
def handle_data(context, data):
# Skip first 300 days to get full windows
context.i += 1
if context.i < 300:
@@ -336,6 +516,46 @@ a function we use in the ``handle_data()`` section:
short_mavg=short_mavg,
long_mavg=long_mavg)
def analyze(context, perf):
import matplotlib.pyplot as plt
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['btc'].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()
Here we are explicitly defining an ``analyze()`` function that gets
automatically called once the backtest is done.
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.
Conclusions
~~~~~~~~~~~
beginner-tutorial.html
+249 -31
View File
@@ -188,7 +188,7 @@ containing the current trading bar with open, high, low, and close
<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">buy_btc.py</span></code>:</p>
directory: <a class="reference external" href="https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/buy_btc_simple.py">buy_btc_simple.py</a>:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">catalyst.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>
@@ -332,7 +332,14 @@ to the <code class="docutils literal"><span class="pre">-c</span></code> option
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">buy_btc_simple_out.pickle</span></code> we would call <code class="docutils literal"><span class="pre">catalyst</span> <span class="pre">run</span></code> as follows:</p>
<div class="highlight-python"><div class="highlight"><pre><span class="n">catalyst</span> <span class="n">run</span> <span class="o">-</span><span class="n">f</span> <span class="n">buy_btc_simple</span><span class="o">.</span><span class="n">py</span> <span class="o">-</span><span class="n">x</span> <span class="n">bitfinex</span> <span class="o">--</span><span class="n">start</span> <span class="mi">2016</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">2016</span><span class="o">-</span><span class="mi">9</span><span class="o">-</span><span class="mi">29</span> <span class="o">-</span><span class="n">o</span> <span class="n">buy_simple_btc_out</span><span class="o">.</span><span class="n">pickle</span>
<div class="highlight-python"><div class="highlight"><pre><span class="n">catalyst</span> <span class="n">run</span> <span class="o">-</span><span class="n">f</span> <span class="n">buy_btc_simple</span><span class="o">.</span><span class="n">py</span> <span class="o">-</span><span class="n">x</span> <span class="n">bitfinex</span> <span class="o">--</span><span class="n">start</span> <span class="mi">2016</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">2017</span><span class="o">-</span><span class="mi">9</span><span class="o">-</span><span class="mi">30</span> <span class="o">-</span><span class="n">o</span> <span class="n">buy_btc_simple_out</span><span class="o">.</span><span class="n">pickle</span>
</pre></div>
</div>
<div class="highlight-python"><div class="highlight"><pre>INFO: run_algo: running algo in backtest mode
INFO: exchange_algorithm: initialized trading algorithm in backtest mode
INFO: Performance: Simulated 639 trading days out of 639.
INFO: Performance: first open: 2016-01-01 00:00:00+00:00
INFO: Performance: last close: 2017-09-30 23:59:00+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
@@ -349,7 +356,7 @@ asset price. (Note, that you can also change the commission and
slippage model that <code class="docutils literal"><span class="pre">catalyst</span></code> uses).</p>
<p>Let&#8217;s 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">catalyst</span></code> makes heavy usage of
rows. and print the first ten rows. Note that <code class="docutils literal"><span class="pre">catalyst</span></code> makes heavy usage of
<a class="reference external" href="http://pandas.pydata.org/">pandas</a>, 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>
@@ -357,16 +364,189 @@ outputting so it&#8217;s worth spending some time to learn it.</p>
<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>algo_volatility</th>
<th>algorithm_period_return</th>
<th>alpha</th>
<th>benchmark_period_return</th>
<th>benchmark_volatility</th>
<th>beta</th>
<th>btc</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>2016-01-01 23:59:00+00:00</th>
<td>NaN</td>
<td>0.000000e+00</td>
<td>NaN</td>
<td>-0.010937</td>
<td>NaN</td>
<td>NaN</td>
<td>433.979999</td>
<td>0.000000</td>
<td>1.000000e+07</td>
<td>0.00</td>
<td>...</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>NaN</td>
<td>1.000000e+07</td>
<td>0.00</td>
<td>0.00</td>
<td>1</td>
<td>[]</td>
<td>0.0227</td>
</tr>
<tr>
<th>2016-01-02 23:59:00+00:00</th>
<td>0.000011</td>
<td>-9.536708e-07</td>
<td>-0.000170</td>
<td>-0.006480</td>
<td>0.173338</td>
<td>-0.000062</td>
<td>432.700000</td>
<td>-442.236708</td>
<td>9.999558e+06</td>
<td>432.70</td>
<td>...</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>-11.224972</td>
<td>1.000000e+07</td>
<td>0.00</td>
<td>0.00</td>
<td>2</td>
<td>[{u'order_id': u'7869f7828fa140328eb40477bb7de...</td>
<td>0.0227</td>
</tr>
<tr>
<th>2016-01-03 23:59:00+00:00</th>
<td>0.000011</td>
<td>-2.328842e-06</td>
<td>-0.000176</td>
<td>-0.026512</td>
<td>0.197857</td>
<td>0.000009</td>
<td>428.390000</td>
<td>-437.831716</td>
<td>9.999120e+06</td>
<td>856.78</td>
<td>...</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>-12.754262</td>
<td>9.999558e+06</td>
<td>432.70</td>
<td>432.70</td>
<td>3</td>
<td>[{u'order_id': u'be62ff77760c4599abaac43be9cc9...</td>
<td>0.0227</td>
</tr>
<tr>
<th>2016-01-04 23:59:00+00:00</th>
<td>0.000011</td>
<td>-2.380954e-06</td>
<td>-0.000139</td>
<td>-0.008640</td>
<td>0.269790</td>
<td>0.000020</td>
<td>432.900000</td>
<td>-442.441116</td>
<td>9.998677e+06</td>
<td>1298.70</td>
<td>...</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>-11.287205</td>
<td>9.999120e+06</td>
<td>856.78</td>
<td>856.78</td>
<td>4</td>
<td>[{u'order_id': u'd6dca79513214346a646079213526...</td>
<td>0.0224</td>
</tr>
<tr>
<th>2016-01-05 23:59:00+00:00</th>
<td>0.000011</td>
<td>-3.650729e-06</td>
<td>-0.000158</td>
<td>-0.021426</td>
<td>0.245989</td>
<td>0.000024</td>
<td>431.840000</td>
<td>-441.357754</td>
<td>9.998236e+06</td>
<td>1727.36</td>
<td>...</td>
<td>0</td>
<td>0</td>
<td>0</td>
<td>-12.333847</td>
<td>9.998677e+06</td>
<td>1298.70</td>
<td>1298.70</td>
<td>5</td>
<td>[{u'order_id': u'505275d6646a41f3856b22b16678d...</td>
<td>0.0225</td>
</tr>
</tbody>
</table>
</div><div class="line-block">
<div class="line"><br /></div>
</div>
<p>There is a row for each trading day, starting on the first day of our
simulation Jan 1st, 2016. In the columns you can find various
information about the state of your algorithm. The very first column
information about the state of your algorithm. The column
<code class="docutils literal"><span class="pre">btc</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 bitcoin. For example, we could easily
examine now how our portfolio value changed over time compared to the
bitcoin price.</p>
<p>Our algorithm performance as assessed by the
<code class="docutils literal"><span class="pre">portfolio_value</span></code> closely matches that of the bitcoin price. This
is not surprising as our algorithm only bought bitcoin every chance it got.</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">btc</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;bitcoin 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 0x10eaeadd0&gt;
</pre></div>
</div>
<img alt="https://s3.amazonaws.com/enigmaco-docs/github.io/buy_btc_simple_graph.png" src="https://s3.amazonaws.com/enigmaco-docs/github.io/buy_btc_simple_graph.png" />
<p>Our algorithm performance as assessed by the <code class="docutils literal"><span class="pre">portfolio_value</span></code> closely
matches that of the bitcoin price. This is not surprising as our algorithm
only bought bitcoin every chance it got.</p>
</div>
</div>
<div class="section" id="access-to-previous-prices-using-history">
@@ -389,38 +569,76 @@ 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>). This is
a function we use in the <code class="docutils literal"><span class="pre">handle_data()</span></code> section:</p>
<div class="highlight-python"><div class="highlight"><pre> <span class="kn">from</span> <span class="nn">catalyst.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>
<div class="highlight-python"><div class="highlight"><pre><span class="o">%%</span><span class="n">catalyst</span> <span class="o">--</span><span class="n">start</span> <span class="mi">2016</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">2017</span><span class="o">-</span><span class="mi">9</span><span class="o">-</span><span class="mi">30</span> <span class="o">-</span><span class="n">x</span> <span class="n">bitfinex</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">catalyst.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="p">,</span> <span class="n">order_target</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;btc_usd&#39;</span><span class="p">)</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;btc_usd&#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"># 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"># 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"># 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">btc</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="c"># Save values for later inspection</span>
<span class="n">record</span><span class="p">(</span><span class="n">btc</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="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</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;btc&#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>
<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.</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.</p>
</div>
</div>
<div class="section" id="conclusions">
searchindex.js
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