`budgetools`

budgetools is an App/project to demonstrate the utilities of budgeting, saving, and investing. The idea is to share the principles of budgeting and financial planning, as a means for assessing one's financial future and decision making.

How much to save/invest every month to reach a monetary amount in a given number of years
Inflation adjusted considerations for a given monetary amount i.e. purchasing power
Basic monthly budget considerations
Forecasting salary/income growth and cost of living (expenses due to inflation) growth i.e. living standards
Forecasting savings accumulation
Forecasting investing alongside savings accumulation
Forecasting net worth - savings (#5) and investment (#6)

Installation

The project/app is compatible with python 3.x (optimal results for 3.10).

It's first recommended to clone the repo

$ git clone https://github.com/Shuaib-8/budgetools.git

After cloning, within the root of the repo, you can install requirements in the following two ways

You can install the project/package directly using pip like so:

$ python -m pip install "git+https://github.com/Shuaib-8/finance-budget-app.git#egg=budgetools&subdirectory=budgetools"

You can install via conda environment (if installed) or create a virtual environment (venv) and install dependencies via requirements.txt

$ conda env create -f env.yml

Once environment is created run the following

$ conda activate finance-app

You can create a venv beforehand and install

$ python -m pip install -r requirements.txt

App Access

Finally, if you want to run the app, then perform the following
Move to the app location if not already

$ cd app
$ streamlit run 1_🤑_main_page.py

A Docker install is also possible (assuming you're in the root of this repo where the Dockerfile is located)

$ docker build -t streamlit_budgetools .

After the containerisation process is finished, the app can be accessed through

$ docker run -p 8501:8501 streamlit_budgetools

The app is then available to view online via:

http://0.0.0.0:8501

How to use

As this is also a native python project, it is structured like so

├── budgetools
│   ├── budgetools
│   │   ├── __init__.py
│   │   ├── budget.py # access budget utilities
│   │   ├── forecast.py # access forecast/simulations of utilities for savings/investing and overall net worth
│   │   ├── investment.py # access investment utilities to show avenues towards monetary target
│   │   └── main.py # access example workflow for these given functions
│   └── setup.py

The best place to start is with investment utilities to answer the following:

How much do I need to save monthly to reach a monetary target?

from budgetools.investment import monthly_amount_to_investment

# avg_ror - average annual rate of returns from a given stock index e.g. S&P 500
# years - number of years to invest for 
# desired_amount - monetary target 

monthly_amount_to_investment(
        avg_ror=0.07, years=25, desired_amount=1_000_000
)

> 1277.07

How much is the monetary target really worth in real terms by the end of the period?

from budgetools.investment import investment_inflation_adjustment

# avg_inflation - average inflation rate over a given period, which is usually shown by an economy's Consumer Price Index (CPI) rate

investment_inflation_adjustment(
        avg_inflation=0.03, years=25, desired_amount=1_000_000
)

> 477605.57

Afterwards, a basic budget is sensible to contemplate on a given future monetary goal:

How much do I have leftover at the end of a typical month based on income (after tax) and common expenses?

from budgetools.budget import BaseBudget

# salary - annual gross amount 
# tax_rate - tax rate in your domicile (if known)

base = BaseBudget(salary=60000, tax_rate=0.4)

base.rent = 1200 # monthly
base.food_daily = 10 # daily (below function call extends to monthly)
base.entertainment = 200 # monthly
base.emergency_expenses = 250 # monthly

base.monthly_savings()
> 1550

It's also to important to consider how salary and cost of living can be affected based on apparent trends and to see how one can be affected by given trends being extended in the future:

How does my salary growth prospects and cost of living (based on economic conditions) affect my income and expenses in the future (per month until end of period)?

from budgetools.forecast import SalaryExpensesForecasting

forecast_living = SalaryExpensesForecasting(years=15, salary=60000, tax_rate=0.3)
forecast_living.annual_salary_growth = 0.05

salary_forecast = forecast_living.monthly_salary_forecast()
salary_forecast 
# Returns a NumPy array that is equal to length of years e.g. 15 years = 180 months = 180 elements representing salary forecast
> array([3514.26, 3528.58, 3542.95, 3557.39, 3571.88, 3586.43, 3601.04,
       3615.72, 3630.45, 3645.24, 3660.09, 3675., ...])

forecast_living.rent = 1200
forecast_living.food_daily = 30
forecast_living.entertainment = 200
forecast_living.emergency_expenses = 250
forecast_living.annual_inflation = 0.025

expenses_forecast = forecast_living.monthly_expenses_forecast()

expenses_forecast
# Returns a NumPy array that is equal to length of years e.g. 15 years = 180 months = 180 elements representing expenses forecast
> array([2555.25, 2560.52, 2565.79, 2571.08, 2576.37, 2581.68, 2587.,
       2592.32, 2597.66, 2603.02, 2608.38, 2613.75, ...])

Finally, it's worth considering a sustainable solution alongside earning income to reach a monetary target (net worth) as indicated earlier. Savings in a standard savings account aggresively may not be feasible to reach a given goal quicker. However, introducing long-term investment strategies such as in a retirement account or in a broad/diversified stock market index such as the S&P 500 every so often can allow one to take advantage of compound growth, which could build up savings in a more efficient manner:

How does my final net worth and cumulative savings look like over a given period - standard savings and no investment?

from budgetools.forecast import NetWorthSimulation

forecast_net_worth = NetWorthSimulation(
    years=25, salary=60000, tax_rate=0.3, monthly_investment_pct=0.3
)

forecast_net_worth.rent = 1200
forecast_net_worth.food_daily = 30
forecast_net_worth.entertainment = 200
forecast_net_worth.emergency_expenses = 250
forecast_net_worth.annual_inflation = 0.025
forecast_net_worth.annual_salary_growth = 0.05

final_net_worth, cumulative_savings = forecast_net_worth.savings_forecast()

final_net_worth 
> 999094.69
cumulative_savings
# Returns a NumPy array that is equal to length of years e.g. 25 years = 300 months = 300 elements representing cumulative savings schedule
> array([  959.01,  1927.07,  2904.23,  3890.54,  4886.05,  5890.8,
        6904.84,  7928.24,  8961.03, 10003.25, 11054.96, 12116.21, ...])

Now if we factor in some of our savings towards investment (30% in this case), we can see how a savings/investment forecast schedule could look like:

How does my savings and investment schedule look like - corresponding with cumulative savings in this setup?

from budgetools.forecast import NetWorthSimulation

forecast_net_worth = NetWorthSimulation(
    years=25, salary=60000, tax_rate=0.3, monthly_investment_pct=0.3
)

forecast_net_worth.rent = 1200
forecast_net_worth.food_daily = 30
forecast_net_worth.entertainment = 200
forecast_net_worth.emergency_expenses = 250
forecast_net_worth.annual_inflation = 0.025
forecast_net_worth.annual_salary_growth = 0.05

(
    investment_deposit_forecast,
    savings_forecast_post_investment,
    cumulative_savings_new,
) = forecast_net_worth.monthly_income_investment()

investment_deposit_forecast 
# Returns a NumPy array that is equal to length of years e.g. 25 years = 300 months = 300 elements representing investment deposit forecast
> array([287.703, 290.418, 293.148, 295.893, 298.653, 301.425, 304.212,
       307.02 , 309.837, 312.666, 315.513, 318.375, ...])
savings_forecast_post_investment
# Returns a NumPy array that is equal to length of years e.g. 25 years = 300 months = 300 elements representing savings forecast while considering investing
> array([671.307, 677.642, 684.012, 690.417, 696.857, 703.325, 709.828,
       716.38 , 722.953, 729.554, 736.197, 742.875, ...])
cumulative_savings_new
# Returns a NumPy array that is equal to length of years e.g. 25 years = 300 months = 300 elements representing a modified cumulative savings schedule (wrt investing)
> array([671.307, 1348.949, 2032.961, 2723.378, 3420.235, 4123.56 ,
       4833.388, 5549.768, 6272.721, 7002.275, 7738.472, 8481.347, ...])

Now that it can be shown how savings/investment can look like for accumulating savings, finally we can show how net worth is impacted:

How does my net worth look like when considering savings alongside investment?

from budgetools.forecast import NetWorthSimulation

forecast_net_worth = NetWorthSimulation(
    years=25, salary=60000, tax_rate=0.3, monthly_investment_pct=0.3
)

forecast_net_worth.rent = 1200
forecast_net_worth.food_daily = 30
forecast_net_worth.entertainment = 200
forecast_net_worth.emergency_expenses = 250
forecast_net_worth.annual_inflation = 0.025
forecast_net_worth.annual_salary_growth = 0.05
forecast_net_worth.annual_investment_return = 0.07

(
    cumulated_savings_new,
    investment_portfolio,
    net_worth,
) = forecast_net_worth.net_worth_savings_investments()

investment_portfolio 
# Returns a NumPy array that is equal to length of years e.g. 25 years = 300 months = 300 elements representing an approximate investment portfolio growth schedule 
> array([287.703, 579.74771459, 876.17369246, 1177.0207059,
       1482.32875209, 1792.13505437, 2106.48004652, 2425.41039096,
       2748.96101393, 3077.17003917, 3410.08180595, 3747.73790427, ...])
net_worth 
# Returns a NumPy array that is equal to length of years e.g. 25 years = 300 months = 300 elements representing an approximate net worth (savings + investment) growth schedule 
> array([959.01, 1928.69671459, 2909.13469246, 3900.3987059,
        4902.56375209, 5915.69505437, 6939.86804652, 7975.17839096,
        9021.68201393, 10079.44503917, 11148.55380595, 12229.08490427, ...])
# Returns predicted net worth by the end of the period
final_net_worth = f'Final net worth: {net_worth[-1]:,.2f}'
print(final_net_worth)
> Final net worth: 1,299,150.94

Hence, within the same time frame, there's a gain to be made from long-term investing alongside saving.

Testing

To run and also test that everything is working within this project, it's also recommended to check tests.
From the top level/root of the directory, run the following command:

pytest

References

Special thanks to Dakota Wixom who developed the course Introduction to Financial Concepts in Python via DataCamp. The course has helped inspire and develop this project, where I have adapted the given material from the code/exercises within the course.

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