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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