def scat_plot(): dates = pd.date_range('2009-01-01', '2012-12-31') symbols = ['SPY', 'XOM', 'GLD'] df = get_data(symbols, dates) daily_returns = compute_daily_returns(df) daily_returns.plot(kind='scatter', x='SPY', y='XOM') beta_XOM, alpha_XOM = np.polyfit(daily_returns['SPY'], daily_returns['XOM'], 1) plt.plot(daily_returns['SPY'], beta_XOM * daily_returns['SPY'] + alpha_XOM, '-', color='r') plt.show() daily_returns.plot(kind='scatter', x='SPY', y='GLD') beta_GLD, alpha_GLD = np.polyfit(daily_returns['SPY'], daily_returns['GLD'], 1) plt.plot(daily_returns['SPY'], beta_GLD * daily_returns['SPY'] + alpha_GLD, '-', color='r') plt.show() print daily_returns.corr(method='pearson')
def scat_plot(): dates = pd.date_range('2009-01-01', '2012-12-31') symbols = ['SPY', 'XOM', 'GLD'] df = get_data(symbols, dates) daily_returns = compute_daily_returns(df) daily_returns.plot(kind='scatter', x='SPY', y='XOM') beta_XOM, alpha_XOM = np.polyfit(daily_returns['SPY'], daily_returns['XOM'], 1) plt.plot(daily_returns['SPY'], beta_XOM*daily_returns['SPY'] + alpha_XOM, '-', color='r') plt.show() daily_returns.plot(kind='scatter', x='SPY', y='GLD') beta_GLD, alpha_GLD = np.polyfit(daily_returns['SPY'], daily_returns['GLD'], 1) plt.plot(daily_returns['SPY'], beta_GLD*daily_returns['SPY'] + alpha_GLD, '-', color='r') plt.show() print daily_returns.corr(method='pearson')
def hist_plot(): dates = pd.date_range('2009-01-01', '2012-12-31') symbols = ['SPY', 'XOM'] df = get_data(symbols, dates) daily_returns = compute_daily_returns(df) daily_returns['SPY'].hist(bins=20, label='SPY') daily_returns['XOM'].hist(bins=20, label='XOM') #mean = daily_returns['SPY'].mean() #print 'mean = ', mean #std = daily_returns['SPY'].std() #print 'std = ', std #plt.axvline(mean, color='w', linestyle='dashed', linewidth=2) #plt.axvline(std, color='r', linestyle='dashed', linewidth=2) #plt.axvline(-std, color='r', linestyle='dashed', linewidth=2) plt.legend(loc='upper right') plt.show() print daily_returns.kurtosis()