Become financially independent

Become financially independent#

4% and 25 times rules#

Annual Spending

Retirement Stash Needed

\(\$\)25,000

\(\$\)625,000

\(\$\)50,000

\(\$\)1,250,000

\(\$\)100,000

\(\$\)2,500,000

\(\$\)150,000

\(\$\)3,750,000
Mentimeter Question

https://www.menti.com/alohp2v13yqh

Hide code cell source 
import pandas as pd
import ipywidgets as widgets
import matplotlib.ticker as mtick
from IPython.display import YouTubeVideo, display
Hide code cell source 
#retirement functions

def calc_stash_compound_interest(w, i, r, t):
    return w + (i * (1+r) ** t + i * (1+r) * ((1+r) ** t - 1) / r)

def calc_principal(w, i, t):
    return w + (i * t)

def get_retirement_df(rate, age, max_age, salary, spending, net_worth):
    
    df = pd.DataFrame({'Age': pd.Series(range(age, max_age))})

    df['Yearly Income'] = df.apply(lambda x: salary, axis=1)
    df['Yearly Spending'] = df.apply(lambda x: spending, axis=1)
    df['Yearly Savings'] = df.apply(lambda x: salary-spending, axis=1)
    df['Total Stash'] = df.apply(lambda x: calc_stash_compound_interest(net_worth, salary-spending, rate*.01, x['Age']-age), axis=1)
    df['Principal'] = df.apply(lambda x: calc_principal(net_worth, salary-spending, x['Age']-age), axis=1)
    df['Total Stash'] = df['Total Stash'].astype('int64')
    df['Principal'] = df['Principal'].astype('int64')
    return df

style = {'description_width': 'initial'}

ma = 80
age = widgets.IntText(
    value=20,
    placeholder=20,
    description='Age you begin investing:',
    readout_format='d',
    style=style,
    disabled=False
)

rate = widgets.IntText(
    value=4,
    placeholder=4,
    description='Rate of return on investments:',
    readout_format='d',
    style=style,
    disabled=False
)

salary = widgets.IntText(
    value=80000,
    placeholder=80000,
    description='Yearly Salary: $',
    readout_format='d',
    style=style,
    disabled=False
)

spending = widgets.IntText(
    value=60000,
    placeholder=60000,
    description='Yearly Spending: $',
    readout_format='d',
    style=style,
    disabled=False
)

net_worth = widgets.IntText(
    value=20000,
    placeholder=20000,
    description='Current amount of assets or debt: $',
    readout_format='d',
    style=style,
    disabled=False
)

def r_output(r, a, sal, sp, w):
    #create and display dataframe
    
    def get_ret_age(df, ret_tgt):
        try:
            return df['Age'][df['Total Stash'] >= ret_tgt].iloc[0]
        except:
            return None
 
    def get_ret_num(df, ret_age):
        try:
            return df['Total Stash'][df['Age'] >= ret_age].iloc[0]
        except:
            return df['Total Stash'].max()
        
    def func(stash, retire_number):
        if stash > retire_number:
            return retire_number
        else:
            return stash

    df_plot = get_retirement_df(r, a, ma, sal, sp, w)
    ret_target = 25*sp
    ret_age = get_ret_age(df_plot, ret_target)
    ret_number = get_ret_num(df_plot, ret_age)
    df_plot['Retired Stash'] = df_plot.apply(lambda x: func(x['Total Stash'], ret_number), axis=1)

    if ret_age is None:
        print("Try reducing your spending or you won't have enough money to retire.")
    else:
        ax = df_plot.plot.line(x='Age', y=['Total Stash', 'Retired Stash', 'Principal'], figsize=(12,8), linewidth=3, grid=True)
        fmt = '${x:,.0f}'
        tick = mtick.StrMethodFormatter(fmt)
        ax.yaxis.set_major_formatter(tick)
        ymin, ymax = ax.get_ylim()
        xmin, xmax = ax.get_xlim()
        ax.scatter(ret_age, ret_number, s=700, color='yellow', zorder=10)
        ax.text(ret_age, ret_number+(ymax/100), s='   You may be able to retire at age ' + str(ret_age), fontsize='xx-large', fontweight='bold', color = 'purple', zorder=11)

    #display(df_plot)
 
    return("compare rate returns example")


Basic retirement planning tool#

Hide code cell source 
r_res = widgets.interactive(r_output, r=rate, a=age, sal=salary, sp=spending, w=net_worth)
widgets.VBox([r_res])


Takeaway#

  • Track your spending - budgeting

  • Payoff debts

  • Lower your spending

  • Build emergency fund

  • Invest for the future

    • Be nice to your future self

    • How soon can you retire?

    • The 4% rule

    • Invest early and often (more compound interest magic)



Citations#

Cumby, James & Degiacomi, Matteo & Erastova, Valentina & Guven, Jonna & Hobday, Claire & Mey, Antonia & Pollak, Hannah & Szabla, Rafał. (2023). Course Materials for an Introduction to Data-Driven Chemistry. Journal of Open Source Education. 6. 192. 10.21105/jose.00192.

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