Welcome to fin_libs’s documentation!¶
Fin Libs is a Python library that exposes tools for financial analytics. This includes functions to calculate: * simple and compound interest * compound annual growth rate * weighted average * linear least squares * earning per share * net income
How to install fin_libs¶
This library can be installed by running pip install fin_libs given that you already have Python and pip installed.
Library usage¶
Example usage of each library module is included in the documentation of that module.
compound_annual_growth_rate¶
Library module to compute compund annual growth rate
Typical usage example:
rate = calculate_compound_annual_growth_rate(71, 100, 4)
- fin_libs.compound_annual_growth_rate.calculate_compound_annual_growth_rate(first, last, years)¶
Calculate compound annual growth rate
- Parameters:
first (float) – starting value
last (float) – ending value
years (int) – number of years
- Returns:
calculated compound annual growth rate
- Return type:
float
- fin_libs.compound_annual_growth_rate.print_calculate_compound_annual_growth_rate(first, last, years)¶
Print compound annaul growth rate
- Parameters:
first (float) – starting value
last (float) – ending value
years (int) – number of years
- Returns:
None
dividends¶
Library module to compute dividend information
Typical usage example:
aapl_dr = calculate_dividend_rate(“AAPL”)
- fin_libs.dividends.calculate_dividend_rate(ticker)¶
Calculate dividend rate
- Parameters:
ticker (str) – name of the ticker
- Returns:
dividend rate
- Return type:
float
- fin_libs.dividends.calculate_dividend_yield(ticker)¶
Calculate dividend yield
- Parameters:
ticker (str) – name of the ticker
- Returns:
dividend yield
- Return type:
float
eps¶
Library module to compute earnings per share
Typical usage example:
aapl_eps = calculate_eps(“AAPL”)
- fin_libs.eps.calculate_eps(ticker)¶
Calculate Earnings per Share
- Parameters:
ticker (str) – the ticker for which we should calculate the EPS
- Returns:
the EPS
- Return type:
float
income¶
Library module to compute net income
Typical usage example:
net_income = calculate_net_income(“earnings.csv”, “transactions”)
- fin_libs.income.calculate_net_income(csv_file_path, col_name)¶
Calculate net income
- Parameters:
csv_file_path (str) – expect csv_file_path to have column with positive and negative values for money in & out
col_name (str) – name of the column
- Returns:
the net income
- Return type:
float
interest.compound¶
Library package to calculate compound interest
Typical usage example:
compound_interest = calculate_compound_interest(100, 5, 2)
- fin_libs.interest.compound.calculate_compound_interest(principal, rate, time)¶
Calculate compound interest
- Parameters:
principal (float) – principal amount
rate (float) – interest rate
time (int) – time
- Returns:
compound interest
- Return type:
float
interest.simple¶
Library package to calculate simple interest
Typical usage example:
simple_interest = calculate_simple_interest(100, 5, 2)
- fin_libs.interest.simple.calculate_simple_interest(principal, rate, time)¶
Calculate simple interest
- Parameters:
principal (float) – principal amount
rate (float) – interest rate
time (int) – time
- Returns:
simple interest
- Return type:
float
linear_least_squares¶
Library package to compute linear least squares regression
Typical usage example:
x, y, y_pred = do_linear_least_squares_regression(“data.csv”) plot_linear_least_squares_regression(x, y, y_pred, “red”)
- fin_libs.linear_least_squares.do_linear_least_squares_regression(csv_file_path)¶
Does linear least squares regression
- Parameters:
csv_file_path (string) – file path where data is located
- Returns:
x coefficient int: y coefficient int: y predictor coefficient
- Return type:
int
- fin_libs.linear_least_squares.plot_linear_least_squares_regression(x, y, y_pred, color='yellow')¶
Plots chart of linear least squares regression
- Parameters:
x (int) – x coefficient
y (int) – y coefficient
y_pred (int) – y predictor coefficient
color (string, optional) – color
- Returns:
None
price¶
Module to calculate stock price ratios
Typical usage example:
apple_pe = calculate_price_to_earning(“AAPL”) apple_pbv = calculate_price_to_book_value(“AAPL”)
- fin_libs.price.calculate_price_to_book_value(ticker)¶
Calculate Price to Book Value Ratio (P/BV)
- Parameters:
ticker (str) – the ticker for which we should calculate the ratio
- Returns:
the ratio
- Return type:
float
- fin_libs.price.calculate_price_to_earning(ticker)¶
Calculate Price to Earnings Ratio (P/E)
- Parameters:
ticker (str) – the ticker for which we should calculate the ratio
- Returns:
the ratio
- Return type:
float
weighted_average¶
Library package to compute the weighted average
Typical usage example:
weighted_avg = compute_weighted_average(“data.csv”, “values”, “weights”)
- fin_libs.weighted_average.compute_weighted_average(csv_file_path, distr_col, weights_col)¶
Compute weighted average
- Parameters:
csv_file_path (str) – file path string
distr_col (str) – column name for distribution
weights_col (str) – column name for weights
- Returns:
weighted average
- Return type:
int