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

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