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()