Time Series Analytics

1. Semi-log graph of equity curve
2. Exposure over time
3. Half year rolling return, annualized
   • Useful to see what annualized would look like in a shorter time firm
   • AnnualizedReturn = (EndValue/StartValue)^YearDays/ActualDays-1
   • Python:
     • def ann_ret(ts):
       • return np.power((ts[-1]/ts[0]), (year_length/len(ts)))-1
4. Maximum Drawdown
   • Python:
     • def dd(ts):
       • return np.min(ts/np.maximum.accumulate(ts)) – 1
5. Recalculate time series vs benchmarks with same starting point
   • Python:
     • def rebased(ts):
       • return ts / ts[0]

• Key python reminders:
  • Remove all N/A calculations:
    • df.dropna(inplace=True)
  • Reset figures when needed:
    • fig.clear(True)
  • Start returns at 0:
    • df.iloc[0].[loc[‘underlying_return’] = 0

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