goalProb

Estimation of Goal Achieving Probabilities

Overview

• Using transition probabilities, it estimates the Expected Wealth paths and the probability of achieving the goals from a specific point in time (t0) to the goal achievement time (T).

  • For instance, it can estimate the probability that wealth of around $100 in January 2020 will exceed $250 by December 2022.

• All you need is asset return panel data and weights panel data

• check tutorial.ipynb

Example

• Portfolio: All Weather Portfolio (SPY, TLT, IEF, GLD, DBC)
• Assume an investor wants to know the probability that the wealth, which was 200 in January 2020, will be greater than 220 or 250 in December 2021
  • no additional cashflows excluding initial wealth
  • you can consider cashflows plans using model.add_cashflow() method.

Result

Start Date	End Date	Wealth(start)	Wealth(end)	Probability
2020-01-31	2021-12-31	200	220	0.5606
2020-01-31	2021-12-31	200	250	0.3607

Requirements

• Python >= 3.8
• numpy
• pandas
• scipy

reference

• Paper Link(original, multi)