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DOC: PEP8 simple_universe.py & added to example_algos.html

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_sources/example-algos.txt
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@@ -31,6 +31,11 @@ Overview
`two-part video tutorial <videos.html#backtesting-a-strategy>`_ to show how
to get started in backtesting and live trading with Catalyst.
- :ref:`Simple Universe <simple_universe>`: This code provides the 'universe'
of available trading pairs on a given exchange on any given day. You can use
this code to dynamically select which currency pairs you want to trade each
day of your strategy. This example does not make any trades.
- :ref:`Portfolio Optimization <portfolio_optimization>`: Use this code to
execute a portfolio optimization model. This strategy will select the
portfolio with the maximum Sharpe Ratio. The parameters are set to use 180
@@ -753,6 +758,177 @@ implemented after the video was recorded, which executes the orders at slighlty
different prices, but resulting in significant changes in performance of our
strategy.
.. _simple_universe:
Simple Universe
~~~~~~~~~~~~~~~
Source code: `examples/simple_universe.py <https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/simple_universe.py>`_
This example aims to provide an easy way for users to learn how to
collect data from any given exchange and select a subset of the available
currency pairs for trading. You simply need to specify the exchange and
the market (base_currency) that you want to focus on. You will then see
how to create a universe of assets, and filter it based the market you
desire.
The example prints out the closing price of all the pairs for a given
market in a given exchange every 30 minutes. The example also contains
the OHLCV data with minute-resolution for the past seven days which
could be used to create indicators. Use this code as the backbone to
create your own trading strategy.
The lookback_date variable is used to ensure data for a coin existed on
the lookback period specified.
To run, execute the following two commands in a terminal (inside catalyst
environment). The first one retrieves all the pricing data needed for this
script to run (only needs to be run once), and the second one executes this
script with the parameters specified in the run_algorithm() call at the end
of the file:
.. code-block:: bash
catalyst ingest-exchange -x bitfinex -f minute
.. code-block:: bash
python simple_universe.py
Credits: This code was originally submitted by `Abner Ayala-Acevedo
<https://github.com/abnera>`_. Thank you!
.. code-block:: python
from datetime import timedelta
import numpy as np
import pandas as pd
from catalyst import run_algorithm
from catalyst.exchange.exchange_utils import get_exchange_symbols
from catalyst.api import (symbols, )
def initialize(context):
context.i = -1 # minute counter
context.exchange = context.exchanges.values()[0].name.lower()
context.base_currency = context.exchanges.values()[0].base_currency.lower()
def handle_data(context, data):
context.i += 1
lookback_days = 7 # 7 days
# current date & time in each iteration formatted into a string
now = data.current_dt
date, time = now.strftime('%Y-%m-%d %H:%M:%S').split(' ')
lookback_date = now - timedelta(days=lookback_days)
# keep only the date as a string, discard the time
lookback_date = lookback_date.strftime('%Y-%m-%d %H:%M:%S').split(' ')[0]
one_day_in_minutes = 1440 # 60 * 24 assumes data_frequency='minute'
# update universe everyday at midnight
if not context.i % one_day_in_minutes:
context.universe = universe(context, lookback_date, date)
# get data every 30 minutes
minutes = 30
# get lookback_days of history data: that is 'lookback' number of bins
lookback = one_day_in_minutes / minutes * lookback_days
if not context.i % minutes and context.universe:
# we iterate for every pair in the current universe
for coin in context.coins:
pair = str(coin.symbol)
# Get 30 minute interval OHLCV data. This is the standard data
# required for candlestick or indicators/signals. Return Pandas
# DataFrames. 30T means 30-minute re-sampling of one minute data.
# Adjust it to your desired time interval as needed.
opened = fill(data.history(coin, 'open',
bar_count=lookback, frequency='30T')).values
high = fill(data.history(coin, 'high',
bar_count=lookback, frequency='30T')).values
low = fill(data.history(coin, 'low',
bar_count=lookback, frequency='30T')).values
close = fill(data.history(coin, 'price',
bar_count=lookback, frequency='30T')).values
volume = fill(data.history(coin, 'volume',
bar_count=lookback, frequency='30T')).values
# close[-1] is the last value in the set, which is the equivalent
# to current price (as in the most recent value)
# displays the minute price for each pair every 30 minutes
print('{now}: {pair} -\tO:{o},\tH:{h},\tL:{c},\tC{c},\tV:{v}'.format(
now=now,
pair=pair,
o=opened[-1],
h=high[-1],
l=low[-1],
c=close[-1],
v=volume[-1],
))
# -------------------------------------------------------------
# --------------- Insert Your Strategy Here -------------------
# -------------------------------------------------------------
def analyze(context=None, results=None):
pass
# Get the universe for a given exchange and a given base_currency market
# Example: Poloniex BTC Market
def universe(context, lookback_date, current_date):
# get all the pairs for the given exchange
json_symbols = get_exchange_symbols(context.exchange)
# convert into a DataFrame for easier processing
df = pd.DataFrame.from_dict(json_symbols).transpose().astype(str)
df['base_currency'] = df.apply(lambda row: row.symbol.split('_')[1],axis=1)
df['market_currency'] = df.apply(lambda row: row.symbol.split('_')[0],axis=1)
# Filter all the pairs to get only the ones for a given base_currency
df = df[df['base_currency'] == context.base_currency]
# Filter all the pairs to ensure that pair existed in the current date range
df = df[df.start_date < lookback_date]
df = df[df.end_daily >= current_date]
context.coins = symbols(*df.symbol) # convert all the pairs to symbols
return df.symbol.tolist()
# Replace all NA, NAN or infinite values with its nearest value
def fill(series):
if isinstance(series, pd.Series):
return series.replace([np.inf, -np.inf], np.nan).ffill().bfill()
elif isinstance(series, np.ndarray):
return pd.Series(series).replace(
[np.inf, -np.inf], np.nan
).ffill().bfill().values
else:
return series
if __name__ == '__main__':
start_date = pd.to_datetime('2017-11-10', utc=True)
end_date = pd.to_datetime('2017-11-13', utc=True)
performance = run_algorithm(start=start_date, end=end_date,
capital_base=100.0, # amount of base_currency
initialize=initialize,
handle_data=handle_data,
analyze=analyze,
exchange_name='bitfinex',
data_frequency='minute',
base_currency='btc',
live=False,
live_graph=False,
algo_namespace='simple_universe')
.. _portfolio_optimization:
Portfolio Optimization
appendix.html
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@@ -125,6 +125,7 @@
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#buy-and-hodl-algorithm">Buy and Hodl Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#dual-moving-average-crossover">Dual Moving Average Crossover</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#mean-reversion-algorithm">Mean Reversion Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#simple-universe">Simple Universe</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
beginner-tutorial.html
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@@ -127,6 +127,7 @@
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#buy-and-hodl-algorithm">Buy and Hodl Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#dual-moving-average-crossover">Dual Moving Average Crossover</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#mean-reversion-algorithm">Mean Reversion Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#simple-universe">Simple Universe</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
bundles.html
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@@ -125,6 +125,7 @@
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#buy-and-hodl-algorithm">Buy and Hodl Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#dual-moving-average-crossover">Dual Moving Average Crossover</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#mean-reversion-algorithm">Mean Reversion Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#simple-universe">Simple Universe</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
development-guidelines.html
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@@ -127,6 +127,7 @@
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#buy-and-hodl-algorithm">Buy and Hodl Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#dual-moving-average-crossover">Dual Moving Average Crossover</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#mean-reversion-algorithm">Mean Reversion Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#simple-universe">Simple Universe</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
example-algos.html
+163 -1
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@@ -127,6 +127,7 @@
<li class="toctree-l2"><a class="reference internal" href="#buy-and-hodl-algorithm">Buy and Hodl Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="#dual-moving-average-crossover">Dual Moving Average Crossover</a></li>
<li class="toctree-l2"><a class="reference internal" href="#mean-reversion-algorithm">Mean Reversion Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="#simple-universe">Simple Universe</a></li>
<li class="toctree-l2"><a class="reference internal" href="#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
@@ -279,6 +280,10 @@ in the <code class="docutils literal"><span class="pre">analyze()</span></code>
strategy that is used in our
<a class="reference external" href="videos.html#backtesting-a-strategy">two-part video tutorial</a> to show how
to get started in backtesting and live trading with Catalyst.</li>
<li><a class="reference internal" href="#simple-universe"><span>Simple Universe</span></a>: This code provides the &#8216;universe&#8217;
of available trading pairs on a given exchange on any given day. You can use
this code to dynamically select which currency pairs you want to trade each
day of your strategy. This example does not make any trades.</li>
<li><a class="reference internal" href="#portfolio-optimization"><span>Portfolio Optimization</span></a>: Use this code to
execute a portfolio optimization model. This strategy will select the
portfolio with the maximum Sharpe Ratio. The parameters are set to use 180
@@ -963,8 +968,165 @@ implemented after the video was recorded, which executes the orders at slighlty
different prices, but resulting in significant changes in performance of our
strategy.</p>
</div>
<div class="section" id="simple-universe">
<span id="id1"></span><h2>Simple Universe<a class="headerlink" href="#simple-universe" title="Permalink to this headline"></a></h2>
<p>Source code: <a class="reference external" href="https://github.com/enigmampc/catalyst/blob/master/catalyst/examples/simple_universe.py">examples/simple_universe.py</a></p>
<p>This example aims to provide an easy way for users to learn how to
collect data from any given exchange and select a subset of the available
currency pairs for trading. You simply need to specify the exchange and
the market (base_currency) that you want to focus on. You will then see
how to create a universe of assets, and filter it based the market you
desire.</p>
<p>The example prints out the closing price of all the pairs for a given
market in a given exchange every 30 minutes. The example also contains
the OHLCV data with minute-resolution for the past seven days which
could be used to create indicators. Use this code as the backbone to
create your own trading strategy.</p>
<p>The lookback_date variable is used to ensure data for a coin existed on
the lookback period specified.</p>
<p>To run, execute the following two commands in a terminal (inside catalyst
environment). The first one retrieves all the pricing data needed for this
script to run (only needs to be run once), and the second one executes this
script with the parameters specified in the run_algorithm() call at the end
of the file:</p>
<div class="highlight-bash"><div class="highlight"><pre>catalyst ingest-exchange -x bitfinex -f minute
</pre></div>
</div>
<div class="highlight-bash"><div class="highlight"><pre>python simple_universe.py
</pre></div>
</div>
<p>Credits: This code was originally submitted by <a class="reference external" href="https://github.com/abnera">Abner Ayala-Acevedo</a>. Thank you!</p>
<div class="highlight-python"><div class="highlight"><pre><span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">timedelta</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="kn">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">catalyst</span> <span class="kn">import</span> <span class="n">run_algorithm</span>
<span class="kn">from</span> <span class="nn">catalyst.exchange.exchange_utils</span> <span class="kn">import</span> <span class="n">get_exchange_symbols</span>
<span class="kn">from</span> <span class="nn">catalyst.api</span> <span class="kn">import</span> <span class="p">(</span><span class="n">symbols</span><span class="p">,</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="o">-</span><span class="mi">1</span> <span class="c"># minute counter</span>
<span class="n">context</span><span class="o">.</span><span class="n">exchange</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">exchanges</span><span class="o">.</span><span class="n">values</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">name</span><span class="o">.</span><span class="n">lower</span><span class="p">()</span>
<span class="n">context</span><span class="o">.</span><span class="n">base_currency</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">exchanges</span><span class="o">.</span><span class="n">values</span><span class="p">()[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">base_currency</span><span class="o">.</span><span class="n">lower</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="n">context</span><span class="o">.</span><span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">lookback_days</span> <span class="o">=</span> <span class="mi">7</span> <span class="c"># 7 days</span>
<span class="c"># current date &amp; time in each iteration formatted into a string</span>
<span class="n">now</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">current_dt</span>
<span class="n">date</span><span class="p">,</span> <span class="n">time</span> <span class="o">=</span> <span class="n">now</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s">&#39;%Y-%m-</span><span class="si">%d</span><span class="s"> %H:%M:%S&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">&#39; &#39;</span><span class="p">)</span>
<span class="n">lookback_date</span> <span class="o">=</span> <span class="n">now</span> <span class="o">-</span> <span class="n">timedelta</span><span class="p">(</span><span class="n">days</span><span class="o">=</span><span class="n">lookback_days</span><span class="p">)</span>
<span class="c"># keep only the date as a string, discard the time</span>
<span class="n">lookback_date</span> <span class="o">=</span> <span class="n">lookback_date</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s">&#39;%Y-%m-</span><span class="si">%d</span><span class="s"> %H:%M:%S&#39;</span><span class="p">)</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">&#39; &#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">one_day_in_minutes</span> <span class="o">=</span> <span class="mi">1440</span> <span class="c"># 60 * 24 assumes data_frequency=&#39;minute&#39;</span>
<span class="c"># update universe everyday at midnight</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">context</span><span class="o">.</span><span class="n">i</span> <span class="o">%</span> <span class="n">one_day_in_minutes</span><span class="p">:</span>
<span class="n">context</span><span class="o">.</span><span class="n">universe</span> <span class="o">=</span> <span class="n">universe</span><span class="p">(</span><span class="n">context</span><span class="p">,</span> <span class="n">lookback_date</span><span class="p">,</span> <span class="n">date</span><span class="p">)</span>
<span class="c"># get data every 30 minutes</span>
<span class="n">minutes</span> <span class="o">=</span> <span class="mi">30</span>
<span class="c"># get lookback_days of history data: that is &#39;lookback&#39; number of bins</span>
<span class="n">lookback</span> <span class="o">=</span> <span class="n">one_day_in_minutes</span> <span class="o">/</span> <span class="n">minutes</span> <span class="o">*</span> <span class="n">lookback_days</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">context</span><span class="o">.</span><span class="n">i</span> <span class="o">%</span> <span class="n">minutes</span> <span class="ow">and</span> <span class="n">context</span><span class="o">.</span><span class="n">universe</span><span class="p">:</span>
<span class="c"># we iterate for every pair in the current universe</span>
<span class="k">for</span> <span class="n">coin</span> <span class="ow">in</span> <span class="n">context</span><span class="o">.</span><span class="n">coins</span><span class="p">:</span>
<span class="n">pair</span> <span class="o">=</span> <span class="nb">str</span><span class="p">(</span><span class="n">coin</span><span class="o">.</span><span class="n">symbol</span><span class="p">)</span>
<span class="c"># Get 30 minute interval OHLCV data. This is the standard data</span>
<span class="c"># required for candlestick or indicators/signals. Return Pandas</span>
<span class="c"># DataFrames. 30T means 30-minute re-sampling of one minute data.</span>
<span class="c"># Adjust it to your desired time interval as needed.</span>
<span class="n">opened</span> <span class="o">=</span> <span class="n">fill</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">history</span><span class="p">(</span><span class="n">coin</span><span class="p">,</span> <span class="s">&#39;open&#39;</span><span class="p">,</span>
<span class="n">bar_count</span><span class="o">=</span><span class="n">lookback</span><span class="p">,</span> <span class="n">frequency</span><span class="o">=</span><span class="s">&#39;30T&#39;</span><span class="p">))</span><span class="o">.</span><span class="n">values</span>
<span class="n">high</span> <span class="o">=</span> <span class="n">fill</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">history</span><span class="p">(</span><span class="n">coin</span><span class="p">,</span> <span class="s">&#39;high&#39;</span><span class="p">,</span>
<span class="n">bar_count</span><span class="o">=</span><span class="n">lookback</span><span class="p">,</span> <span class="n">frequency</span><span class="o">=</span><span class="s">&#39;30T&#39;</span><span class="p">))</span><span class="o">.</span><span class="n">values</span>
<span class="n">low</span> <span class="o">=</span> <span class="n">fill</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">history</span><span class="p">(</span><span class="n">coin</span><span class="p">,</span> <span class="s">&#39;low&#39;</span><span class="p">,</span>
<span class="n">bar_count</span><span class="o">=</span><span class="n">lookback</span><span class="p">,</span> <span class="n">frequency</span><span class="o">=</span><span class="s">&#39;30T&#39;</span><span class="p">))</span><span class="o">.</span><span class="n">values</span>
<span class="n">close</span> <span class="o">=</span> <span class="n">fill</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">history</span><span class="p">(</span><span class="n">coin</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="n">lookback</span><span class="p">,</span> <span class="n">frequency</span><span class="o">=</span><span class="s">&#39;30T&#39;</span><span class="p">))</span><span class="o">.</span><span class="n">values</span>
<span class="n">volume</span> <span class="o">=</span> <span class="n">fill</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">history</span><span class="p">(</span><span class="n">coin</span><span class="p">,</span> <span class="s">&#39;volume&#39;</span><span class="p">,</span>
<span class="n">bar_count</span><span class="o">=</span><span class="n">lookback</span><span class="p">,</span> <span class="n">frequency</span><span class="o">=</span><span class="s">&#39;30T&#39;</span><span class="p">))</span><span class="o">.</span><span class="n">values</span>
<span class="c"># close[-1] is the last value in the set, which is the equivalent</span>
<span class="c"># to current price (as in the most recent value)</span>
<span class="c"># displays the minute price for each pair every 30 minutes</span>
<span class="k">print</span><span class="p">(</span><span class="s">&#39;{now}: {pair} -</span><span class="se">\t</span><span class="s">O:{o},</span><span class="se">\t</span><span class="s">H:{h},</span><span class="se">\t</span><span class="s">L:{c},</span><span class="se">\t</span><span class="s">C{c},</span><span class="se">\t</span><span class="s">V:{v}&#39;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span>
<span class="n">now</span><span class="o">=</span><span class="n">now</span><span class="p">,</span>
<span class="n">pair</span><span class="o">=</span><span class="n">pair</span><span class="p">,</span>
<span class="n">o</span><span class="o">=</span><span class="n">opened</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="n">h</span><span class="o">=</span><span class="n">high</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="n">l</span><span class="o">=</span><span class="n">low</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="n">c</span><span class="o">=</span><span class="n">close</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="n">v</span><span class="o">=</span><span class="n">volume</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span>
<span class="p">))</span>
<span class="c"># -------------------------------------------------------------</span>
<span class="c"># --------------- Insert Your Strategy Here -------------------</span>
<span class="c"># -------------------------------------------------------------</span>
<span class="k">def</span> <span class="nf">analyze</span><span class="p">(</span><span class="n">context</span><span class="o">=</span><span class="bp">None</span><span class="p">,</span> <span class="n">results</span><span class="o">=</span><span class="bp">None</span><span class="p">):</span>
<span class="k">pass</span>
<span class="c"># Get the universe for a given exchange and a given base_currency market</span>
<span class="c"># Example: Poloniex BTC Market</span>
<span class="k">def</span> <span class="nf">universe</span><span class="p">(</span><span class="n">context</span><span class="p">,</span> <span class="n">lookback_date</span><span class="p">,</span> <span class="n">current_date</span><span class="p">):</span>
<span class="c"># get all the pairs for the given exchange</span>
<span class="n">json_symbols</span> <span class="o">=</span> <span class="n">get_exchange_symbols</span><span class="p">(</span><span class="n">context</span><span class="o">.</span><span class="n">exchange</span><span class="p">)</span>
<span class="c"># convert into a DataFrame for easier processing</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="o">.</span><span class="n">from_dict</span><span class="p">(</span><span class="n">json_symbols</span><span class="p">)</span><span class="o">.</span><span class="n">transpose</span><span class="p">()</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="nb">str</span><span class="p">)</span>
<span class="n">df</span><span class="p">[</span><span class="s">&#39;base_currency&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">row</span><span class="p">:</span> <span class="n">row</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">&#39;_&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">df</span><span class="p">[</span><span class="s">&#39;market_currency&#39;</span><span class="p">]</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="k">lambda</span> <span class="n">row</span><span class="p">:</span> <span class="n">row</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s">&#39;_&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">],</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="c"># Filter all the pairs to get only the ones for a given base_currency</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="p">[</span><span class="s">&#39;base_currency&#39;</span><span class="p">]</span> <span class="o">==</span> <span class="n">context</span><span class="o">.</span><span class="n">base_currency</span><span class="p">]</span>
<span class="c"># Filter all the pairs to ensure that pair existed in the current date range</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">start_date</span> <span class="o">&lt;</span> <span class="n">lookback_date</span><span class="p">]</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">end_daily</span> <span class="o">&gt;=</span> <span class="n">current_date</span><span class="p">]</span>
<span class="n">context</span><span class="o">.</span><span class="n">coins</span> <span class="o">=</span> <span class="n">symbols</span><span class="p">(</span><span class="o">*</span><span class="n">df</span><span class="o">.</span><span class="n">symbol</span><span class="p">)</span> <span class="c"># convert all the pairs to symbols</span>
<span class="k">return</span> <span class="n">df</span><span class="o">.</span><span class="n">symbol</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
<span class="c"># Replace all NA, NAN or infinite values with its nearest value</span>
<span class="k">def</span> <span class="nf">fill</span><span class="p">(</span><span class="n">series</span><span class="p">):</span>
<span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">series</span><span class="p">,</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">):</span>
<span class="k">return</span> <span class="n">series</span><span class="o">.</span><span class="n">replace</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span><span class="o">.</span><span class="n">ffill</span><span class="p">()</span><span class="o">.</span><span class="n">bfill</span><span class="p">()</span>
<span class="k">elif</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">series</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">):</span>
<span class="k">return</span> <span class="n">pd</span><span class="o">.</span><span class="n">Series</span><span class="p">(</span><span class="n">series</span><span class="p">)</span><span class="o">.</span><span class="n">replace</span><span class="p">(</span>
<span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">,</span> <span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
<span class="p">)</span><span class="o">.</span><span class="n">ffill</span><span class="p">()</span><span class="o">.</span><span class="n">bfill</span><span class="p">()</span><span class="o">.</span><span class="n">values</span>
<span class="k">else</span><span class="p">:</span>
<span class="k">return</span> <span class="n">series</span>
<span class="k">if</span> <span class="n">__name__</span> <span class="o">==</span> <span class="s">&#39;__main__&#39;</span><span class="p">:</span>
<span class="n">start_date</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="s">&#39;2017-11-10&#39;</span><span class="p">,</span> <span class="n">utc</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">end_date</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">to_datetime</span><span class="p">(</span><span class="s">&#39;2017-11-13&#39;</span><span class="p">,</span> <span class="n">utc</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
<span class="n">performance</span> <span class="o">=</span> <span class="n">run_algorithm</span><span class="p">(</span><span class="n">start</span><span class="o">=</span><span class="n">start_date</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="n">end_date</span><span class="p">,</span>
<span class="n">capital_base</span><span class="o">=</span><span class="mf">100.0</span><span class="p">,</span> <span class="c"># amount of base_currency</span>
<span class="n">initialize</span><span class="o">=</span><span class="n">initialize</span><span class="p">,</span>
<span class="n">handle_data</span><span class="o">=</span><span class="n">handle_data</span><span class="p">,</span>
<span class="n">analyze</span><span class="o">=</span><span class="n">analyze</span><span class="p">,</span>
<span class="n">exchange_name</span><span class="o">=</span><span class="s">&#39;bitfinex&#39;</span><span class="p">,</span>
<span class="n">data_frequency</span><span class="o">=</span><span class="s">&#39;minute&#39;</span><span class="p">,</span>
<span class="n">base_currency</span><span class="o">=</span><span class="s">&#39;btc&#39;</span><span class="p">,</span>
<span class="n">live</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
<span class="n">live_graph</span><span class="o">=</span><span class="bp">False</span><span class="p">,</span>
<span class="n">algo_namespace</span><span class="o">=</span><span class="s">&#39;simple_universe&#39;</span><span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="portfolio-optimization">
<span id="id1"></span><h2>Portfolio Optimization<a class="headerlink" href="#portfolio-optimization" title="Permalink to this headline"></a></h2>
<span id="id2"></span><h2>Portfolio Optimization<a class="headerlink" href="#portfolio-optimization" title="Permalink to this headline"></a></h2>
<p>Use this code to execute a portfolio optimization model. This strategy will
select the portfolio with the maximum Sharpe Ratio. The parameters are set to
use 180 days of historical data and rebalance every 30 days. This code was used
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<li class="toctree-l2"><a class="reference internal" href="example-algos.html#buy-and-hodl-algorithm">Buy and Hodl Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#dual-moving-average-crossover">Dual Moving Average Crossover</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#mean-reversion-algorithm">Mean Reversion Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#simple-universe">Simple Universe</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
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<li class="toctree-l2"><a class="reference internal" href="example-algos.html#buy-and-hodl-algorithm">Buy and Hodl Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#dual-moving-average-crossover">Dual Moving Average Crossover</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#mean-reversion-algorithm">Mean Reversion Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#simple-universe">Simple Universe</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
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<li class="toctree-l2"><a class="reference internal" href="example-algos.html#buy-and-hodl-algorithm">Buy and Hodl Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#dual-moving-average-crossover">Dual Moving Average Crossover</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#mean-reversion-algorithm">Mean Reversion Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#simple-universe">Simple Universe</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
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<li class="toctree-l2"><a class="reference internal" href="example-algos.html#buy-and-hodl-algorithm">Buy and Hodl Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#dual-moving-average-crossover">Dual Moving Average Crossover</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#mean-reversion-algorithm">Mean Reversion Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#simple-universe">Simple Universe</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
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<li class="toctree-l2"><a class="reference internal" href="example-algos.html#buy-and-hodl-algorithm">Buy and Hodl Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#dual-moving-average-crossover">Dual Moving Average Crossover</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#mean-reversion-algorithm">Mean Reversion Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#simple-universe">Simple Universe</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
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<li class="toctree-l2"><a class="reference internal" href="example-algos.html#buy-and-hodl-algorithm">Buy and Hodl Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#dual-moving-average-crossover">Dual Moving Average Crossover</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#mean-reversion-algorithm">Mean Reversion Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#simple-universe">Simple Universe</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
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<li class="toctree-l2"><a class="reference internal" href="example-algos.html#buy-and-hodl-algorithm">Buy and Hodl Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#dual-moving-average-crossover">Dual Moving Average Crossover</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#mean-reversion-algorithm">Mean Reversion Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#simple-universe">Simple Universe</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
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<li class="toctree-l2"><a class="reference internal" href="example-algos.html#buy-and-hodl-algorithm">Buy and Hodl Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#dual-moving-average-crossover">Dual Moving Average Crossover</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#mean-reversion-algorithm">Mean Reversion Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#simple-universe">Simple Universe</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
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<li class="toctree-l2"><a class="reference internal" href="example-algos.html#buy-and-hodl-algorithm">Buy and Hodl Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#dual-moving-average-crossover">Dual Moving Average Crossover</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#mean-reversion-algorithm">Mean Reversion Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#simple-universe">Simple Universe</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
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<li class="toctree-l2"><a class="reference internal" href="example-algos.html#buy-and-hodl-algorithm">Buy and Hodl Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#dual-moving-average-crossover">Dual Moving Average Crossover</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#mean-reversion-algorithm">Mean Reversion Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#simple-universe">Simple Universe</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
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<li class="toctree-l2"><a class="reference internal" href="example-algos.html#buy-and-hodl-algorithm">Buy and Hodl Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#dual-moving-average-crossover">Dual Moving Average Crossover</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#mean-reversion-algorithm">Mean Reversion Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#simple-universe">Simple Universe</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
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<li class="toctree-l2"><a class="reference internal" href="example-algos.html#buy-and-hodl-algorithm">Buy and Hodl Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#dual-moving-average-crossover">Dual Moving Average Crossover</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#mean-reversion-algorithm">Mean Reversion Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#simple-universe">Simple Universe</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
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<li class="toctree-l2"><a class="reference internal" href="example-algos.html#buy-and-hodl-algorithm">Buy and Hodl Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#dual-moving-average-crossover">Dual Moving Average Crossover</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#mean-reversion-algorithm">Mean Reversion Algorithm</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#simple-universe">Simple Universe</a></li>
<li class="toctree-l2"><a class="reference internal" href="example-algos.html#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>