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

Example code:

foridx,fundincf_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])

Wherecf_data is the cashflow data imported from Preqin usingpd.read_csv('preqin_cf_file.csv'), andlocal_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, usinglocal_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).