columns=[
'Batting Average', 'Omega Ratio',
'Up Months', 'Down Months',
'Slugging Ratio',
'Up-Capture Russell',
'Down-Capture Russell'
],
market_index='Russell 3000',
MAR=0,
threshold=0,
order=1,
start_gap=start_gap)
### Daily maximum Drawdown for differnt portfolio
max_dd_df = m.time_drawdown(df_data,
'TeamCo Client Composite',
109,
0,
109,
start_gap=6)
### Maximum Drawdown for given time window
max_dd = m.max_drawdown(df_data,
'TeamCo Client Composite',
109,
0,
109,
start_gap=6)
### Rolling beta
rolling_beta_df = m.rolling_beta(df_data,
columns_name=columns_name,
window_length=36,
min_periods=36,
start_gap=6)
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)
### Daily maximum Drawdown for differnt portfolio
max_dd_df = m.time_drawdown(df_data, 'TeamCo Client Composite')
### Maximum Drawdown for given time window
max_dd = m.max_drawdown(df_data, 'TeamCo Client Composite')
### 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)
### Rolling sortino ratio
rolling_sortino_ratio_df = m.rolling_sortino_ratio(df_data, columns_name,
window_length,