Krdytkyu

from operator import mul

from functools import reduce

from random import gauss

from statistics import median

from typing import List



def avg_cagr(percents: List[int]) -> float:

        '''Given (successive) % annual growth rates, returns average Compound Annual Growth Rate'''

        amount = reduce(mul, [1+p/100 for p in percents])

        amount = amount if amount > 0 else 0 # at worst, complete amount can be lost but can't go negative

        return (amount**(1/len(percents)) - 1)*100



def normal_returns(mu: float, sigma: float, years: int = 20, simulations: int = 1000) -> float:

    '''Returns net CAGR assuming annual percentage returns follow a Normal distribution'''

    return median(avg_cagr([gauss(mu=mu, sigma=sigma) for _ in range(years)) for _ in range(simulations)])

from operator import mul

from functools import reduce

from random import gauss

from statistics import median

from typing import List



def avg_cagr(percents: List[int]) -> float:

        '''Given (successive) % annual growth rates, returns average Compound Annual Growth Rate'''

        amount = reduce(mul, [1+p/100 for p in percents])

        amount = amount if amount > 0 else 0 # at worst, complete amount can be lost but can't go negative

        return (amount**(1/len(percents)) - 1)*100



def normal_returns(mu: float, sigma: float, years: int = 20, simulations: int = 1000) -> float:

    '''Returns net CAGR assuming annual percentage returns follow a Normal distribution'''

    return median(avg_cagr([gauss(mu=mu, sigma=sigma) for _ in range(years)) for _ in range(simulations)])

from operator import mul

from functools import reduce

from random import gauss

from statistics import median

from typing import List



def avg_cagr(percents: List[int]) -> float:

        '''Given (successive) % annual growth rates, returns average Compound Annual Growth Rate'''

        amount = reduce(mul, [1+p/100 for p in percents])

        amount = amount if amount > 0 else 0 # at worst, complete amount can be lost but can't go negative

        return (amount**(1/len(percents)) - 1)*100



def normal_returns(mu: float, sigma: float, years: int = 20, simulations: int = 1000) -> float:

    '''Returns net CAGR assuming annual percentage returns follow a Normal distribution'''

    return median(avg_cagr([gauss(mu=mu, sigma=sigma) for _ in range(years)) for _ in range(simulations)])

from operator import mul

from functools import reduce

from random import gauss

from statistics import median

from typing import List



def avg_cagr(percents: List[int]) -> float:

        '''Given (successive) % annual growth rates, returns average Compound Annual Growth Rate'''

        amount = reduce(mul, [1+p/100 for p in percents])

        amount = amount if amount > 0 else 0 # at worst, complete amount can be lost but can't go negative

        return (amount**(1/len(percents)) - 1)*100



def normal_returns(mu: float, sigma: float, years: int = 20, simulations: int = 1000) -> float:

    '''Returns net CAGR assuming annual percentage returns follow a Normal distribution'''

    return median(avg_cagr([gauss(mu=mu, sigma=sigma) for _ in range(years)) for _ in range(simulations)])

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