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| import yfinance as yf | |
| import pandas as pd | |
| import gradio as gr | |
| import plotly.graph_objs as go | |
| def fetch_and_rebalance(etfs_str, period, rebalance_threshold_percentage): | |
| etfs = [etf.strip() for etf in etfs_str.split(",")] | |
| # Fetch historical data for the given period | |
| etf_data = {etf: yf.Ticker(etf).history(period=period) for etf in etfs} | |
| # Ensure the index is a DatetimeIndex | |
| for etf in etfs: | |
| etf_data[etf].index = pd.to_datetime(etf_data[etf].index) | |
| # Fetch dividend data and ensure the index is a DatetimeIndex | |
| etf_dividends = {etf: yf.Ticker(etf).dividends for etf in etfs} | |
| # Ensure both data series have the same dates | |
| common_dates = etf_data[etfs[0]].index | |
| for etf in etfs[1:]: | |
| common_dates = common_dates.intersection(etf_data[etf].index) | |
| for etf in etfs: | |
| etf_data[etf] = etf_data[etf].reindex(common_dates) | |
| etf_dividends[etf] = etf_dividends[etf].reindex(common_dates, fill_value=0) | |
| # Initial investment | |
| initial_investment = 10000 | |
| investments = {etf: initial_investment / len(etfs) for etf in etfs} | |
| shares = {etf: investments[etf] / etf_data[etf]['Close'].iloc[0] for etf in etfs} | |
| # DataFrame to track the investments | |
| df_rebalance = pd.DataFrame(columns=["Date"] + [f"{etf} Value" for etf in etfs] + ["Total Value"]) | |
| rebalance_dates = [] | |
| # Perform the rebalancing based on the percentage difference condition | |
| for date in common_dates[1:]: # Skip the first date for the initial investment | |
| total_value = 0 | |
| for etf in etfs: | |
| # Update the values based on daily returns | |
| previous_date = etf_data[etf].index[etf_data[etf].index.get_loc(date) - 1] | |
| investments[etf] *= (etf_data[etf]['Close'].loc[date] / etf_data[etf]['Close'].loc[previous_date]) | |
| # Add dividends | |
| investments[etf] += etf_dividends[etf].loc[date] * shares[etf] | |
| total_value += investments[etf] | |
| # Check if rebalancing is needed | |
| value_difference = abs(investments[etfs[0]] - investments[etfs[1]]) | |
| percentage_difference = value_difference / total_value | |
| rebalance_needed = percentage_difference > rebalance_threshold_percentage | |
| if rebalance_needed: | |
| # Rebalance to equal weight | |
| investments = {etf: total_value / len(etfs) for etf in etfs} | |
| shares = {etf: investments[etf] / etf_data[etf]['Close'].loc[date] for etf in etfs} | |
| rebalance_dates.append(date) | |
| # Append to DataFrame using pd.concat | |
| df_rebalance = pd.concat([ | |
| df_rebalance, | |
| pd.DataFrame({ | |
| "Date": [date], | |
| **{f"{etf} Value": [investments[etf]] for etf in etfs}, | |
| "Total Value": [total_value] | |
| }) | |
| ], ignore_index=True) | |
| # Check if df_rebalance is empty | |
| if df_rebalance.empty: | |
| return "No data was appended." | |
| final_value = df_rebalance["Total Value"].iloc[-1] | |
| total_return = (final_value - initial_investment) / initial_investment * 100 | |
| # Create the plot | |
| fig = go.Figure() | |
| for etf in etfs: | |
| fig.add_trace(go.Scatter(x=df_rebalance["Date"], y=df_rebalance[f"{etf} Value"], mode='lines', name=etf)) | |
| # Add rebalancing markers | |
| for date in rebalance_dates: | |
| fig.add_trace(go.Scatter(x=[date], y=[df_rebalance.loc[df_rebalance['Date'] == date, f"{etf} Value"].values[0]], | |
| mode='markers', name='Rebalance', marker=dict(size=10, color='red'))) | |
| fig.update_layout(title='ETF Valuations Over Time', xaxis_title='Date', yaxis_title='Value') | |
| return f"Final Value: {final_value}, Total Return: {total_return}%", fig | |
| interface = gr.Interface( | |
| fn=fetch_and_rebalance, | |
| inputs=[ | |
| gr.Textbox(lines=1, placeholder="ETFs (comma separated)", value="TQQQ,JEPI"), | |
| gr.Textbox(lines=1, placeholder="Period", value="ytd"), | |
| gr.Slider(minimum=0.0, maximum=1.0, value=0.05, label="Rebalance Threshold Percentage") | |
| ], | |
| outputs=["text", "plot"] | |
| ) | |
| interface.launch() | |