maween / pypme

Assisting utility functions for calculation of Private Equity Public Market Equivalent (PME) measures using Python. Natively compatible with Preqin data format.

PyPME

Assisting utility functions for calculation of Private Equity PME measures using Python. Natively compatible with Preqin data format.

Example code:

for idx, fund in cf_funds.iterrows():
  fund_cf = cf_data[cf_data['Fund ID'] == fund['Fund ID']].copy()
  cf_funds.loc[idx,'local_KSPME'] = pypme.KS_PME(fund_cf["Transaction Date"],fund_cf["Transaction Amount"],fund_cf["Transaction Category"],local_index.iloc[:,0],local_index.iloc[:,1])
  cf_funds.loc[idx,'DirectAlpha'] = pypme.Direct_Alpha_PME(fund_cf["Transaction Date"],fund_cf["Transaction Amount"],fund_cf["Transaction Category"],local_index.iloc[:,0],local_index.iloc[:,1])
  cf_funds.loc[idx,'PME+'] = pypme.PME_PLUS(fund_cf["Transaction Date"],fund_cf["Transaction Amount"],fund_cf["Transaction Category"],local_index.iloc[:,0],local_index.iloc[:,1])                      

Where cf_data is the cashflow data imported from Preqin using pd.read_csv('preqin_cf_file.csv'), and local_index is a 2-column DataFrame in the format [Date|Index_Value].

The code above iterates over all unique funds in the file, and assigns the metric on a per-fund basis, using local_index as the basis for discount factor.

The library automatically finds the closest index dates for a given cash flow, and thus day-for-day index data is not required for the code to run properly (albeit, better resolution does obviously increase accuracy).