Efficient Frontier with Quandl (Part 2)

Author: PyDataBlog

Tutorials/Efficient Frontier with Sharpe Ratio.py

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+# import needed modules
+import quandl
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+# get adjusted closing prices of 5 selected companies with Quandl
+quandl.ApiConfig.api_key = 'zcfJ6696mcZScjzsyeta'
+selected = ['CNP', 'F', 'WMT', 'GE', 'TSLA']
+data = quandl.get_table('WIKI/PRICES', ticker = selected,
+                        qopts = { 'columns': ['date', 'ticker', 'adj_close'] },
+                        date = { 'gte': '2014-1-1', 'lte': '2016-12-31' }, paginate=True)
+
+# reorganise data pulled by setting date as index with
+# columns of tickers and their corresponding adjusted prices
+clean = data.set_index('date')
+table = clean.pivot(columns='ticker')
+
+# calculate daily and annual returns of the stocks
+returns_daily = table.pct_change()
+returns_annual = returns_daily.mean() * 250
+
+# get daily and covariance of returns of the stock
+cov_daily = returns_daily.cov()
+cov_annual = cov_daily * 250
+
+# empty lists to store returns, volatility and weights of imiginary portfolios
+port_returns = []
+port_volatility = []
+sharpe_ratio = []
+stock_weights = []
+
+# set the number of combinations for imaginary portfolios
+num_assets = len(selected)
+num_portfolios = 50000
+
+#set random seed for reproduction's sake
+np.random.seed(101)
+
+# populate the empty lists with each portfolios returns,risk and weights
+for single_portfolio in range(num_portfolios):
+    weights = np.random.random(num_assets)
+    weights /= np.sum(weights)
+    returns = np.dot(weights, returns_annual)
+    volatility = np.sqrt(np.dot(weights.T, np.dot(cov_annual, weights)))
+    sharpe = returns / volatility
+    sharpe_ratio.append(sharpe)
+    port_returns.append(returns)
+    port_volatility.append(volatility)
+    stock_weights.append(weights)
+
+# a dictionary for Returns and Risk values of each portfolio
+portfolio = {'Returns': port_returns,
+             'Volatility': port_volatility,
+             'Sharpe Ratio': sharpe_ratio}
+
+# extend original dictionary to accomodate each ticker and weight in the portfolio
+for counter,symbol in enumerate(selected):
+    portfolio[symbol+' Weight'] = [Weight[counter] for Weight in stock_weights]
+
+# make a nice dataframe of the extended dictionary
+df = pd.DataFrame(portfolio)
+
+# get better labels for desired arrangement of columns
+column_order = ['Returns', 'Volatility', 'Sharpe Ratio'] + [stock+' Weight' for stock in selected]
+
+# reorder dataframe columns
+df = df[column_order]
+
+# plot frontier, max sharpe & min Volatility values with a scatterplot
+plt.style.use('seaborn-dark')
+df.plot.scatter(x='Volatility', y='Returns', c='Sharpe Ratio',
+                cmap='RdYlGn', edgecolors='black', figsize=(10, 8), grid=True)
+plt.xlabel('Volatility (Std. Deviation)')
+plt.ylabel('Expected Returns')
+plt.title('Efficient Frontier')
+plt.show()