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import gradio as gr | |
import pandas as pd | |
def calculate_pb_ratio(initial_pb_ratio, book_value_growth_rate, stock_price_growth_rate, years): | |
pb_ratios = [initial_pb_ratio] | |
for i in range(1, int(years) + 1): # Convert years to an integer | |
projected_book_value = pb_ratios[-1] * (1 + book_value_growth_rate) | |
projected_stock_price = projected_book_value * pb_ratios[-1] * (1 + stock_price_growth_rate) | |
projected_pb_ratio = projected_stock_price / projected_book_value | |
pb_ratios.append(projected_pb_ratio) | |
return pb_ratios | |
def pb_ratio_valuation(initial_pb_ratio, book_value_growth_rate, stock_price_growth_rate, years): | |
projected_pb_ratios = calculate_pb_ratio(initial_pb_ratio, book_value_growth_rate, stock_price_growth_rate, years) | |
results = {"Year": [], "Projected P/B Ratio": []} | |
for i in range(len(projected_pb_ratios)): | |
results["Year"].append(i + 1) | |
results["Projected P/B Ratio"].append(projected_pb_ratios[i]) | |
return pd.DataFrame(results) | |
# Define the Gradio interface | |
gr.Interface( | |
fn=pb_ratio_valuation, | |
inputs=[ | |
gr.inputs.Slider(minimum=0, maximum=10, default=1.5, label="Initial P/B Ratio"), | |
gr.inputs.Slider(minimum=0, maximum=0.5, default=0.05, label="Annual Book Value Growth Rate"), | |
gr.inputs.Slider(minimum=0, maximum=0.5, default=0.08, label="Annual Stock Price Growth Rate"), | |
gr.inputs.Number(default=10, label="Years") | |
], | |
outputs=gr.outputs.Dataframe(type='pandas'), # Use Dataframe as the output with type 'pandas' | |
title="Price-to-Book (P/B) Ratio Valuation", | |
description="Calculate projected P/B ratios over the next 10 years.", | |
).launch() | |