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| import streamlit as st | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| st.title("Investment App 💼") | |
| A = st.number_input( | |
| "Insert the initial investment (in $): " | |
| ) | |
| r = st.number_input( | |
| "Insert the interest rate (nominal in %): " | |
| ) | |
| T = st.number_input( | |
| "Insert the number of months of your investment: ", | |
| step=1 | |
| ) | |
| # Numpy array with the months | |
| t = np.arange(T + 1) | |
| # Numpy array for the interest rate | |
| r_list = np.array([r] * len(t)) | |
| # Numpy array with the returns for each month in t | |
| y = A * (1 + (r_list / (12*100)))**t | |
| # Numpy array with the continuously compunded returns | |
| y_c = A * np.exp(r_list * t / (12*100)) | |
| # Create the investment dictionary | |
| inv_dict = {"Month": t, | |
| "Rate": r_list, | |
| "Returns": y, | |
| "Continuous returns": y_c} | |
| # Create the investment dataframe | |
| inv_df = pd.DataFrame(inv_dict) | |
| inv_df.columns = inv_dict.keys() | |
| st.write(f"Table with returns at an interest rate of {r}") | |
| new_df = st.data_editor(inv_df) | |
| new_y = [A] | |
| new_y_c = [A] | |
| for i in range(1, len(t)): | |
| r_temp = new_df["Rate"].values[i] | |
| # New numpy array with the returns for each month in t | |
| val = new_y[i-1] * (1 + r_temp / (12*100)) | |
| new_y.append(val) | |
| # New numpy array with the continuously compunded returns | |
| val_c = new_y_c[i-1] * np.exp(r_temp / (12*100)) | |
| new_y_c.append(val_c) | |
| new_df["Returns"] = new_y | |
| new_df["Continuous returns"] = new_y_c | |
| new_df["Actual returns"] = 100 * (new_df["Continuous returns"] - new_df["Continuous returns"].shift(1)) / new_df["Continuous returns"].shift(1) | |
| st.write("Table with variable rates") | |
| st.dataframe(new_df) | |
| fig, ax = plt.subplots() | |
| ax.set_title("Investment returns plot") | |
| ax.set_xlabel("Months") | |
| ax.set_ylabel("Actual returns") | |
| ax.plot(new_df["Month"], new_df["Actual returns"]) | |
| st.pyplot(fig) | |