wassname/catalyst
1
0
Fork 0
mirror of https://github.com/wassname/catalyst.git synced 2026-08-01 12:20:21 +08:00
Code Issues Packages Projects Releases Wiki Activity

DOC: added portfolio_optimization to documented examples

Browse Source
This commit is contained in:
Victor Grau Serrat
2017-11-29 09:38:10 -07:00
parent 5db535918a
commit d0142f3bdd
21 changed files with 315 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files
_sources/example-algos.txt
+152
View File
@@ -31,6 +31,13 @@ Overview
`two-part video tutorial <videos.html#backtesting-a-strategy>`_ to show how
to get started in backtesting and live trading with Catalyst.
- :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
days of historical data and rebalance every 30 days. This code was used in
writting the following article:
`Markowitz Portfolio Optimization for Cryptocurrencies <https://blog.enigma.co/markowitz-portfolio-optimization-for-cryptocurrencies-in-catalyst-b23c38652556>`_.
.. _buy_btc_simple:
@@ -746,4 +753,149 @@ implemented after the video was recorded, which executes the orders at slighlty
different prices, but resulting in significant changes in performance of our
strategy.
.. _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 days of historical data and rebalance every 30 days. This code was used
in writting the following article:
`Markowitz Portfolio Optimization for Cryptocurrencies <https://blog.enigma.co/markowitz-portfolio-optimization-for-cryptocurrencies-in-catalyst-b23c38652556>`_.
.. code-block:: python
'''
You can run this code using the Python interpreter:
$ python portfolio_optimization.py
'''
from __future__ import division
import os
import pytz
import numpy as np
import pandas as pd
from scipy.optimize import minimize
import matplotlib.pyplot as plt
from datetime import datetime
from catalyst.api import record, symbol, symbols, order_target_percent
from catalyst.utils.run_algo import run_algorithm
np.set_printoptions(threshold='nan', suppress=True)
def initialize(context):
# Portfolio assets list
context.assets = symbols('btc_usdt', 'eth_usdt', 'ltc_usdt', 'dash_usdt',
'xmr_usdt')
context.nassets = len(context.assets)
# Set the time window that will be used to compute expected return
# and asset correlations
context.window = 180
# Set the number of days between each portfolio rebalancing
context.rebalance_period = 30
context.i = 0
def handle_data(context, data):
# Only rebalance at the beggining of the algorithm execution and
# every multiple of the rebalance period
if context.i == 0 or context.i%context.rebalance_period == 0:
n = context.window
prices = data.history(context.assets, fields='price',
bar_count=n+1, frequency='1d')
pr = np.asmatrix(prices)
t_prices = prices.iloc[1:n+1]
t_val = t_prices.values
tminus_prices = prices.iloc[0:n]
tminus_val = tminus_prices.values
# Compute daily returns (r)
r = np.asmatrix(t_val/tminus_val-1)
# Compute the expected returns of each asset with the average
# daily return for the selected time window
m = np.asmatrix(np.mean(r, axis=0))
# ###
stds = np.std(r, axis=0)
# Compute excess returns matrix (xr)
xr = r - m
# Matrix algebra to get variance-covariance matrix
cov_m = np.dot(np.transpose(xr),xr)/n
# Compute asset correlation matrix (informative only)
corr_m = cov_m/np.dot(np.transpose(stds),stds)
# Define portfolio optimization parameters
n_portfolios = 50000
results_array = np.zeros((3+context.nassets,n_portfolios))
for p in xrange(n_portfolios):
weights = np.random.random(context.nassets)
weights /= np.sum(weights)
w = np.asmatrix(weights)
p_r = np.sum(np.dot(w,np.transpose(m)))*365
p_std = np.sqrt(np.dot(np.dot(w,cov_m),np.transpose(w)))*np.sqrt(365)
#store results in results array
results_array[0,p] = p_r
results_array[1,p] = p_std
#store Sharpe Ratio (return / volatility) - risk free rate element
#excluded for simplicity
results_array[2,p] = results_array[0,p] / results_array[1,p]
i = 0
for iw in weights:
results_array[3+i,p] = weights[i]
i += 1
#convert results array to Pandas DataFrame
results_frame = pd.DataFrame(np.transpose(results_array),
columns=['r','stdev','sharpe']+context.assets)
#locate position of portfolio with highest Sharpe Ratio
max_sharpe_port = results_frame.iloc[results_frame['sharpe'].idxmax()]
#locate positon of portfolio with minimum standard deviation
min_vol_port = results_frame.iloc[results_frame['stdev'].idxmin()]
#order optimal weights for each asset
for asset in context.assets:
if data.can_trade(asset):
order_target_percent(asset, max_sharpe_port[asset])
#create scatter plot coloured by Sharpe Ratio
plt.scatter(results_frame.stdev,results_frame.r,c=results_frame.sharpe,cmap='RdYlGn')
plt.xlabel('Volatility')
plt.ylabel('Returns')
plt.colorbar()
#plot red star to highlight position of portfolio with highest Sharpe Ratio
plt.scatter(max_sharpe_port[1],max_sharpe_port[0],marker='o',color='b',s=200)
#plot green star to highlight position of minimum variance portfolio
plt.show()
print(max_sharpe_port)
record(pr=pr,r=r, m=m, stds=stds ,max_sharpe_port=max_sharpe_port, corr_m=corr_m)
context.i += 1
def analyze(context=None, results=None):
# Form DataFrame with selected data
data = results[['pr','r','m','stds','max_sharpe_port','corr_m','portfolio_value']]
# Save results in CSV file
filename = os.path.splitext(os.path.basename(__file__))[0]
data.to_csv(filename + '.csv')
# Bitcoin data is available from 2015-3-2. Dates vary for other tokens.
start = datetime(2017, 1, 1, 0, 0, 0, 0, pytz.utc)
end = datetime(2017, 8, 16, 0, 0, 0, 0, pytz.utc)
results = run_algorithm(initialize=initialize,
handle_data=handle_data,
analyze=analyze,
start=start,
end=end,
exchange_name='poloniex',
capital_base=100000, )
.. image:: https://cdn-images-1.medium.com/max/1600/0*EjjiKZHlYF3sn7yQ.
:align: center
appendix.html
+1
View File
@@ -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#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="utilities.html">Utilities</a><ul>
beginner-tutorial.html
+1
View File
@@ -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#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="utilities.html">Utilities</a><ul>
bundles.html
+1
View File
@@ -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#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="utilities.html">Utilities</a><ul>
development-guidelines.html
+1
View File
@@ -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#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="utilities.html">Utilities</a><ul>
example-algos.html
+145
View File
@@ -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="#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="utilities.html">Utilities</a><ul>
@@ -278,6 +279,12 @@ 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="#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
days of historical data and rebalance every 30 days. This code was used in
writting the following article:
<a class="reference external" href="https://blog.enigma.co/markowitz-portfolio-optimization-for-cryptocurrencies-in-catalyst-b23c38652556">Markowitz Portfolio Optimization for Cryptocurrencies</a>.</li>
</ul>
</div>
<div class="section" id="buy-btc-simple-algorithm">
@@ -956,6 +963,144 @@ 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="portfolio-optimization">
<span id="id1"></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
in writting the following article:
<a class="reference external" href="https://blog.enigma.co/markowitz-portfolio-optimization-for-cryptocurrencies-in-catalyst-b23c38652556">Markowitz Portfolio Optimization for Cryptocurrencies</a>.</p>
<div class="highlight-python"><div class="highlight"><pre><span class="sd">&#39;&#39;&#39;</span>
<span class="sd"> You can run this code using the Python interpreter:</span>
<span class="sd"> $ python portfolio_optimization.py</span>
<span class="sd">&#39;&#39;&#39;</span>
<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">division</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">pytz</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">scipy.optimize</span> <span class="kn">import</span> <span class="n">minimize</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">datetime</span> <span class="kn">import</span> <span class="n">datetime</span>
<span class="kn">from</span> <span class="nn">catalyst.api</span> <span class="kn">import</span> <span class="n">record</span><span class="p">,</span> <span class="n">symbol</span><span class="p">,</span> <span class="n">symbols</span><span class="p">,</span> <span class="n">order_target_percent</span>
<span class="kn">from</span> <span class="nn">catalyst.utils.run_algo</span> <span class="kn">import</span> <span class="n">run_algorithm</span>
<span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">threshold</span><span class="o">=</span><span class="s">&#39;nan&#39;</span><span class="p">,</span> <span class="n">suppress</span><span class="o">=</span><span class="bp">True</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="c"># Portfolio assets list</span>
<span class="n">context</span><span class="o">.</span><span class="n">assets</span> <span class="o">=</span> <span class="n">symbols</span><span class="p">(</span><span class="s">&#39;btc_usdt&#39;</span><span class="p">,</span> <span class="s">&#39;eth_usdt&#39;</span><span class="p">,</span> <span class="s">&#39;ltc_usdt&#39;</span><span class="p">,</span> <span class="s">&#39;dash_usdt&#39;</span><span class="p">,</span>
<span class="s">&#39;xmr_usdt&#39;</span><span class="p">)</span>
<span class="n">context</span><span class="o">.</span><span class="n">nassets</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">context</span><span class="o">.</span><span class="n">assets</span><span class="p">)</span>
<span class="c"># Set the time window that will be used to compute expected return</span>
<span class="c"># and asset correlations</span>
<span class="n">context</span><span class="o">.</span><span class="n">window</span> <span class="o">=</span> <span class="mi">180</span>
<span class="c"># Set the number of days between each portfolio rebalancing</span>
<span class="n">context</span><span class="o">.</span><span class="n">rebalance_period</span> <span class="o">=</span> <span class="mi">30</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="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"># Only rebalance at the beggining of the algorithm execution and</span>
<span class="c"># every multiple of the rebalance period</span>
<span class="k">if</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="ow">or</span> <span class="n">context</span><span class="o">.</span><span class="n">i</span><span class="o">%</span><span class="n">context</span><span class="o">.</span><span class="n">rebalance_period</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
<span class="n">n</span> <span class="o">=</span> <span class="n">context</span><span class="o">.</span><span class="n">window</span>
<span class="n">prices</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">assets</span><span class="p">,</span> <span class="n">fields</span><span class="o">=</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">n</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="n">frequency</span><span class="o">=</span><span class="s">&#39;1d&#39;</span><span class="p">)</span>
<span class="n">pr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asmatrix</span><span class="p">(</span><span class="n">prices</span><span class="p">)</span>
<span class="n">t_prices</span> <span class="o">=</span> <span class="n">prices</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="n">n</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span>
<span class="n">t_val</span> <span class="o">=</span> <span class="n">t_prices</span><span class="o">.</span><span class="n">values</span>
<span class="n">tminus_prices</span> <span class="o">=</span> <span class="n">prices</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="n">n</span><span class="p">]</span>
<span class="n">tminus_val</span> <span class="o">=</span> <span class="n">tminus_prices</span><span class="o">.</span><span class="n">values</span>
<span class="c"># Compute daily returns (r)</span>
<span class="n">r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asmatrix</span><span class="p">(</span><span class="n">t_val</span><span class="o">/</span><span class="n">tminus_val</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="c"># Compute the expected returns of each asset with the average</span>
<span class="c"># daily return for the selected time window</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asmatrix</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
<span class="c"># ###</span>
<span class="n">stds</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">r</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="c"># Compute excess returns matrix (xr)</span>
<span class="n">xr</span> <span class="o">=</span> <span class="n">r</span> <span class="o">-</span> <span class="n">m</span>
<span class="c"># Matrix algebra to get variance-covariance matrix</span>
<span class="n">cov_m</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">xr</span><span class="p">),</span><span class="n">xr</span><span class="p">)</span><span class="o">/</span><span class="n">n</span>
<span class="c"># Compute asset correlation matrix (informative only)</span>
<span class="n">corr_m</span> <span class="o">=</span> <span class="n">cov_m</span><span class="o">/</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">stds</span><span class="p">),</span><span class="n">stds</span><span class="p">)</span>
<span class="c"># Define portfolio optimization parameters</span>
<span class="n">n_portfolios</span> <span class="o">=</span> <span class="mi">50000</span>
<span class="n">results_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="mi">3</span><span class="o">+</span><span class="n">context</span><span class="o">.</span><span class="n">nassets</span><span class="p">,</span><span class="n">n_portfolios</span><span class="p">))</span>
<span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="nb">xrange</span><span class="p">(</span><span class="n">n_portfolios</span><span class="p">):</span>
<span class="n">weights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">random</span><span class="p">(</span><span class="n">context</span><span class="o">.</span><span class="n">nassets</span><span class="p">)</span>
<span class="n">weights</span> <span class="o">/=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
<span class="n">w</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asmatrix</span><span class="p">(</span><span class="n">weights</span><span class="p">)</span>
<span class="n">p_r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">w</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">m</span><span class="p">)))</span><span class="o">*</span><span class="mi">365</span>
<span class="n">p_std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">w</span><span class="p">,</span><span class="n">cov_m</span><span class="p">),</span><span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">w</span><span class="p">)))</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">365</span><span class="p">)</span>
<span class="c">#store results in results array</span>
<span class="n">results_array</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="n">p</span><span class="p">]</span> <span class="o">=</span> <span class="n">p_r</span>
<span class="n">results_array</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="n">p</span><span class="p">]</span> <span class="o">=</span> <span class="n">p_std</span>
<span class="c">#store Sharpe Ratio (return / volatility) - risk free rate element</span>
<span class="c">#excluded for simplicity</span>
<span class="n">results_array</span><span class="p">[</span><span class="mi">2</span><span class="p">,</span><span class="n">p</span><span class="p">]</span> <span class="o">=</span> <span class="n">results_array</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="n">p</span><span class="p">]</span> <span class="o">/</span> <span class="n">results_array</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span><span class="n">p</span><span class="p">]</span>
<span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">iw</span> <span class="ow">in</span> <span class="n">weights</span><span class="p">:</span>
<span class="n">results_array</span><span class="p">[</span><span class="mi">3</span><span class="o">+</span><span class="n">i</span><span class="p">,</span><span class="n">p</span><span class="p">]</span> <span class="o">=</span> <span class="n">weights</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="c">#convert results array to Pandas DataFrame</span>
<span class="n">results_frame</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">results_array</span><span class="p">),</span>
<span class="n">columns</span><span class="o">=</span><span class="p">[</span><span class="s">&#39;r&#39;</span><span class="p">,</span><span class="s">&#39;stdev&#39;</span><span class="p">,</span><span class="s">&#39;sharpe&#39;</span><span class="p">]</span><span class="o">+</span><span class="n">context</span><span class="o">.</span><span class="n">assets</span><span class="p">)</span>
<span class="c">#locate position of portfolio with highest Sharpe Ratio</span>
<span class="n">max_sharpe_port</span> <span class="o">=</span> <span class="n">results_frame</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">results_frame</span><span class="p">[</span><span class="s">&#39;sharpe&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">idxmax</span><span class="p">()]</span>
<span class="c">#locate positon of portfolio with minimum standard deviation</span>
<span class="n">min_vol_port</span> <span class="o">=</span> <span class="n">results_frame</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">results_frame</span><span class="p">[</span><span class="s">&#39;stdev&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">idxmin</span><span class="p">()]</span>
<span class="c">#order optimal weights for each asset</span>
<span class="k">for</span> <span class="n">asset</span> <span class="ow">in</span> <span class="n">context</span><span class="o">.</span><span class="n">assets</span><span class="p">:</span>
<span class="k">if</span> <span class="n">data</span><span class="o">.</span><span class="n">can_trade</span><span class="p">(</span><span class="n">asset</span><span class="p">):</span>
<span class="n">order_target_percent</span><span class="p">(</span><span class="n">asset</span><span class="p">,</span> <span class="n">max_sharpe_port</span><span class="p">[</span><span class="n">asset</span><span class="p">])</span>
<span class="c">#create scatter plot coloured by Sharpe Ratio</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">results_frame</span><span class="o">.</span><span class="n">stdev</span><span class="p">,</span><span class="n">results_frame</span><span class="o">.</span><span class="n">r</span><span class="p">,</span><span class="n">c</span><span class="o">=</span><span class="n">results_frame</span><span class="o">.</span><span class="n">sharpe</span><span class="p">,</span><span class="n">cmap</span><span class="o">=</span><span class="s">&#39;RdYlGn&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s">&#39;Volatility&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s">&#39;Returns&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">()</span>
<span class="c">#plot red star to highlight position of portfolio with highest Sharpe Ratio</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">max_sharpe_port</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">max_sharpe_port</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">marker</span><span class="o">=</span><span class="s">&#39;o&#39;</span><span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="s">&#39;b&#39;</span><span class="p">,</span><span class="n">s</span><span class="o">=</span><span class="mi">200</span><span class="p">)</span>
<span class="c">#plot green star to highlight position of minimum variance portfolio</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="k">print</span><span class="p">(</span><span class="n">max_sharpe_port</span><span class="p">)</span>
<span class="n">record</span><span class="p">(</span><span class="n">pr</span><span class="o">=</span><span class="n">pr</span><span class="p">,</span><span class="n">r</span><span class="o">=</span><span class="n">r</span><span class="p">,</span> <span class="n">m</span><span class="o">=</span><span class="n">m</span><span class="p">,</span> <span class="n">stds</span><span class="o">=</span><span class="n">stds</span> <span class="p">,</span><span class="n">max_sharpe_port</span><span class="o">=</span><span class="n">max_sharpe_port</span><span class="p">,</span> <span class="n">corr_m</span><span class="o">=</span><span class="n">corr_m</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="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="c"># Form DataFrame with selected data</span>
<span class="n">data</span> <span class="o">=</span> <span class="n">results</span><span class="p">[[</span><span class="s">&#39;pr&#39;</span><span class="p">,</span><span class="s">&#39;r&#39;</span><span class="p">,</span><span class="s">&#39;m&#39;</span><span class="p">,</span><span class="s">&#39;stds&#39;</span><span class="p">,</span><span class="s">&#39;max_sharpe_port&#39;</span><span class="p">,</span><span class="s">&#39;corr_m&#39;</span><span class="p">,</span><span class="s">&#39;portfolio_value&#39;</span><span class="p">]]</span>
<span class="c"># Save results in CSV file</span>
<span class="n">filename</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">__file__</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">data</span><span class="o">.</span><span class="n">to_csv</span><span class="p">(</span><span class="n">filename</span> <span class="o">+</span> <span class="s">&#39;.csv&#39;</span><span class="p">)</span>
<span class="c"># Bitcoin data is available from 2015-3-2. Dates vary for other tokens.</span>
<span class="n">start</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2017</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">pytz</span><span class="o">.</span><span class="n">utc</span><span class="p">)</span>
<span class="n">end</span> <span class="o">=</span> <span class="n">datetime</span><span class="p">(</span><span class="mi">2017</span><span class="p">,</span> <span class="mi">8</span><span class="p">,</span> <span class="mi">16</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="n">pytz</span><span class="o">.</span><span class="n">utc</span><span class="p">)</span>
<span class="n">results</span> <span class="o">=</span> <span class="n">run_algorithm</span><span class="p">(</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">start</span><span class="o">=</span><span class="n">start</span><span class="p">,</span>
<span class="n">end</span><span class="o">=</span><span class="n">end</span><span class="p">,</span>
<span class="n">exchange_name</span><span class="o">=</span><span class="s">&#39;poloniex&#39;</span><span class="p">,</span>
<span class="n">capital_base</span><span class="o">=</span><span class="mi">100000</span><span class="p">,</span> <span class="p">)</span>
</pre></div>
</div>
<img alt="https://cdn-images-1.medium.com/max/1600/0*EjjiKZHlYF3sn7yQ." class="align-center" src="https://cdn-images-1.medium.com/max/1600/0*EjjiKZHlYF3sn7yQ." />
</div>
</div>
genindex.html
+1
View File
@@ -126,6 +126,7 @@
<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#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="utilities.html">Utilities</a><ul>
index.html
+1
View File
@@ -126,6 +126,7 @@
<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#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="utilities.html">Utilities</a><ul>
install.html
+1
View File
@@ -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#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="utilities.html">Utilities</a><ul>
jupyter.html
+1
View File
@@ -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#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="utilities.html">Utilities</a><ul>
live-trading.html
+1
View File
@@ -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#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="utilities.html">Utilities</a><ul>
naming-convention.html
+1
View File
@@ -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#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="utilities.html">Utilities</a><ul>
objects.inv
BIN
View File
Binary file not shown.
release-process.html
+1
View File
@@ -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#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="utilities.html">Utilities</a><ul>
releases.html
+1
View File
@@ -126,6 +126,7 @@
<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#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="utilities.html">Utilities</a><ul>
resources.html
+1
View File
@@ -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#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="utilities.html">Utilities</a><ul>
search.html
+1
View File
@@ -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#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="utilities.html">Utilities</a><ul>
searchindex.js
+1 -1
View File
File diff suppressed because one or more lines are too long
utilities.html
+1
View File
@@ -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#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
<li class="toctree-l1 current"><a class="current reference internal" href="">Utilities</a><ul>
videos.html
+1
View File
@@ -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#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="utilities.html">Utilities</a><ul>
welcome.html
+1
View File
@@ -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#portfolio-optimization">Portfolio Optimization</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="utilities.html">Utilities</a><ul>