window_length, min_periods) ### Rolling sortino ratio rolling_sortino_ratio_df = m.rolling_sortino_ratio(df_data, columns_name, window_length, min_periods, MAR, threshold) ### Rolling omega ratio rolling_omega_ratio_df = m.rolling_omega_ratio(df_data, columns_name, window_length, min_periods, MAR) ### Rolling sharp ratio rolling_sharpe_ratio_df = m.rolling_sharpe_ratio(df_data, columns_name, window_length, min_periods, benchmark) ### Rolling alpha rolling_alpha_df = m.rolling_alpha(df_data, columns_name, window_length, min_periods) ### Rolling correlation rolling_corr_df = m.rolling_corr(df_data, columns_name, market_index, window_length_corr, min_periods_corr) ### Calculate the correlation with other fund's mean return # Modify the market data, replace it with mean of other fund's return df_data_corr = df_data.copy() df_data_corr['Russell 3000'] = df_data_other.mean( axis=1) # replace with population mean final_cum_return = cum_return_df_other.iloc[-1, :-1] quantile = pd.qcut(final_cum_return.T, 4, labels=['Q1', 'Q2', 'Q3', 'Q4']) Q1 = quantile[quantile.values == 'Q1'].index Q2 = quantile[quantile.values == 'Q2'].index Q3 = quantile[quantile.values == 'Q3'].index
columns_name=columns_name, window_length=36, min_periods=36, start_gap=6, MAR=0) ### Rolling sharp ratio rolling_sharpe_ratio_df = m.rolling_sharpe_ratio(df_data, columns_name=columns_name, window_length=36, min_periods=36, start_gap=6, benchmark=0.02) ### Rolling alpha rolling_alpha_df = m.rolling_alpha(df_data, columns_name=columns_name, window_length=36, min_periods=36, start_gap=6) ### Rolling correlation rolling_corr_df = m.rolling_corr(df_data, columns_name=columns_name, target_benchmark='Russell 3000', window_length=36, min_periods=36, start_gap=6) ### Draw Down dd_df = 100 * m.draw_down(df_data, columns_name, start_gap) ### Graph for result with PdfPages('Rolling Ratio Figure.pdf') as pdf: plt.style.use('fivethirtyeight')
### Rolling beta rolling_beta_df = m.rolling_beta(df_data, columns_name, window_length, min_periods) ### Rolling annulized return rolling_annual_return_df = m.rolling_annulized_return(df_data, columns_name, window_length, min_periods) ### Cummulative return cum_return_df = m.cumulative_return(df_data, columns_name, window_length, min_periods) cum_return_df_other = m.cumulative_return(df_data_other, columns_name_other, window_length, min_periods) ### Rolling sortino ratio rolling_sortino_ratio_df = m.rolling_sortino_ratio(df_data, columns_name, window_length, min_periods, MAR, threshold) ### Rolling omega ratio rolling_omega_ratio_df = m.rolling_omega_ratio(df_data, columns_name, window_length, min_periods, MAR) ### Rolling sharp ratio rolling_sharpe_ratio_df = m.rolling_sharpe_ratio(df_data, columns_name, window_length, min_periods, benchmark) ### Rolling alpha rolling_alpha_df = m.rolling_alpha(df_data, columns_name, window_length, min_periods) ### Rolling correlation rolling_corr_df = m.rolling_corr(df_data, columns_name, market_index, window_length_corr, min_periods_corr) ### Calculate the correlation with other fund's mean return # Modify the market data, replace it with mean of other fund's return df_data_corr = df_data.copy() df_data_corr['Russell 3000'] = df_data_other.mean(axis=1) # replace with population mean final_cum_return = cum_return_df_other.iloc[-1,:-1] quantile = pd.qcut(final_cum_return.T,4, labels=['Q1','Q2','Q3','Q4']) Q1 = quantile[quantile.values == 'Q1'].index Q2 = quantile[quantile.values == 'Q2'].index Q3 = quantile[quantile.values == 'Q3'].index Q4 = quantile[quantile.values == 'Q4'].index df_data_Q1 = df_data.copy()