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]
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,
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)
### 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()
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