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| import streamlit as st | |
| import numpy as np | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| import random | |
| # Streamlit app layout | |
| st.title("Estimating statistics from two sets of historical data") | |
| with st.form("data_input_form"): | |
| N = st.number_input("Enter the number of data points for each set:", step=1) | |
| # Button to generate random data sets | |
| if st.form_submit_button("Generate Random Data Sets"): | |
| # Generate two sets of N random data points each | |
| set1 = [random.randint(0, 20) for _ in range(N)] | |
| set2 = [random.randint(0, 20) for _ in range(N)] | |
| if "set1" in globals(): | |
| # Calculating mean of each set | |
| set1_mean = np.mean(set1) | |
| set2_mean = np.mean(set2) | |
| st.subheader("Expected Values") | |
| st.write(f"Mean of Set 1: {set1_mean:,.2f}") | |
| st.write(f"Mean of Set 2: {set2_mean:,.2f}") | |
| # Calculating variances | |
| set1_var = np.var(set1) | |
| set2_var = np.var(set2) | |
| st.subheader("Variances") | |
| st.write(f"Variance of Set 1: {set1_var:,.2f}") | |
| st.write(f"Variance of Set 2: {set2_var:,.2f}") | |
| # Calculating correlation coefficient | |
| corr_coefficient = np.corrcoef(set1, set2)[0, 1] | |
| st.subheader("Correlation Coefficient") | |
| st.write(f"Correlation Coefficient: {corr_coefficient:,.2f}") | |
| # Scatter plot | |
| plt.scatter(set1, set2) | |
| plt.title("Scatter Plot") | |
| plt.xlabel("Set 1") | |
| plt.ylabel("Set 2") | |
| st.pyplot(plt) | |
| plt.close() | |
| # Line plot | |
| plt.title("Line Plot of Data Sets") | |
| plt.plot(np.arange(len(set1)), set1, label="Set 1") | |
| plt.plot(np.arange(len(set2)), set2, label="Set 2") | |
| plt.ylim(-10, 40) | |
| plt.xlabel("Time") | |
| plt.ylabel("Returns") | |
| plt.legend() | |
| st.pyplot(plt) | |
| plt.close() | |
| # Calculating weights and portfolio returns | |
| w_set1 = (set1_mean - set2_mean + set2_var - corr_coefficient * np.sqrt(set1_var * set2_var)) / ( | |
| set1_var + set2_var - 2 * corr_coefficient * np.sqrt(set1_var * set2_var) | |
| ) | |
| w_set2 = 1 - w_set1 | |
| portfolio_var = (w_set1**2)*set1_var + (w_set2**2)*set2_var + 2*w_set1*w_set2*corr_coefficient*np.sqrt(set1_var * set2_var) | |
| st.subheader("Weights and Portfolio Returns") | |
| st.write("Assuming Set 1 and Set 2 represent returns of portfolios") | |
| st.write(f"Weight of Set 1: {w_set1:,.2f}") | |
| st.write(f"Weight of Set 2: {w_set2:,.2f}") | |
| st.write(f"Expected Portfolio Return: {w_set1*set1_mean + w_set2*set2_mean:,.2f}") | |
| st.write(f"Portfolio Variance: {portfolio_var:,.2f}") | |
| else: | |
| st.write("Please input the number of data points for each set.") |