index_name_3, market_index, target_year, condition='Non-positive') ### Summary table Summary_table_df = r.summary_table(df_data, index_name, summary_columns, market_index, MAR) ### 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,
rolling_beta_df = m.rolling_beta(df_data, columns_name=columns_name, window_length=36, min_periods=36, start_gap=6) ### Rolling annulized return rolling_annual_return_df = m.rolling_annulized_return( df_data, columns_name=columns_name, window_length=36, min_periods=36, start_gap=6) ### Cummulative return cum_return_df = m.cumulative_return(df_data, columns_name=columns_name, window_length=36, min_periods=36, start_gap=6) ### Rolling sortino ratio rolling_sortino_ratio_df = m.rolling_sortino_ratio( df_data, columns_name=columns_name, window_length=36, min_periods=36, start_gap=6, MAR=0, threshold=0, order=2) ### Rolling omega ratio rolling_omega_ratio_df = m.rolling_omega_ratio(df_data, columns_name=columns_name,
Omega_df = r.omega_ratio_table(df_data, index_name, MAR, target_year) ### Correlation table Corr_df = r.corr_table(df_data, index_name_3, market_index, target_year, condition = None) ### Positive Correlation table Corr_df_p = r.corr_table(df_data, index_name_3, market_index, target_year, condition='Positive') ### Positive Correlation table Corr_df_np = r.corr_table(df_data, index_name_3, market_index, target_year, condition='Non-positive') ### Summary table Summary_table_df = r.summary_table(df_data,index_name, summary_columns, market_index, MAR) ### 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()