!pip install openbb -q

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from openbb import obb

sector_etfs = ['XLF', 'XLE', 'XLK', 'XLY', 'XLI', 'XLU', 'XLV']

start_date = '2015-01-01'
etf_dataframes = []

for etf in sector_etfs:
    try:
        data = obb.etf.historical(
            symbol=etf,
            start_date=start_date,
            provider="yfinance"
        ).to_df()
        data['Symbol'] = etf
        etf_dataframes.append(data)
    except Exception as e:
        print(f"Failed to fetch data for {etf}: {str(e)}")

combined_etf_data = pd.concat(etf_dataframes)
combined_etf_data = combined_etf_data.reset_index()

combined_etf_data.head()

combined_etf_data['date'] = pd.to_datetime(combined_etf_data['date'])
combined_etf_data = combined_etf_data[['date', 'close', 'Symbol']]

pivoted_data = combined_etf_data.pivot_table(index='date', columns='Symbol', values='close')

pivoted_data.ffill()

pivoted_data.head()

def sector_rotation_strategy(etf_data, lookback_period=3, top_n=3):
    """
    Implements a simple sector rotation strategy that invests in the top 'n' sector ETFs based on
    past performance over a lookback period.

    Parameters:
    etf_data (DataFrame): ETF performance data
    lookback_period (int): Number of months to look back for performance evaluation
    top_n (int): Number of top sector ETFs to invest in

    Returns:
    DataFrame: Portfolio returns based on the sector rotation strategy
    """
    monthly_returns = etf_data.resample('ME').last().pct_change()

    portfolio_returns = pd.DataFrame(index=monthly_returns.index, columns=['Portfolio Return'])

    for date in monthly_returns.index[lookback_period:]:
        past_returns = monthly_returns.loc[date - pd.DateOffset(months=lookback_period):date].mean()
        top_etfs = past_returns.nlargest(top_n).index

        next_month_date = date + pd.DateOffset(months=1)

        if next_month_date in monthly_returns.index:
            next_month_return = monthly_returns.loc[next_month_date, top_etfs].mean()
            portfolio_returns.loc[next_month_date, 'Portfolio Return'] = next_month_return

    return portfolio_returns

portfolio_returns = sector_rotation_strategy(pivoted_data, lookback_period=3, top_n=3)
portfolio_returns.dropna(inplace=True)

portfolio_returns.head()

portfolio_returns['Cumulative Return'] = (1 + portfolio_returns['Portfolio Return']).cumprod()

pivoted_data['Market Average'] = pivoted_data.mean(axis=1)
market_returns = pivoted_data['Market Average'].resample('ME').last().pct_change()
market_cumulative_return = (1 + market_returns).cumprod()

plt.figure(figsize=(12, 7))
plt.plot(portfolio_returns.index, portfolio_returns['Cumulative Return'], label='Sector Rotation Strategy', color='green')
plt.plot(market_cumulative_return.index, market_cumulative_return, label='Market Average', color='blue')

plt.title('Sector Rotation Strategy vs Market Average', fontsize=16, fontweight='bold')
plt.xlabel('Date', fontsize=12)
plt.ylabel('Cumulative Return', fontsize=12)
plt.legend()
plt.grid(True)
plt.show()

final_strategy_return = portfolio_returns['Cumulative Return'].iloc[-1]

final_market_return = market_cumulative_return.iloc[-1]

print(f"Final cumulative return of sector rotation strategy: {final_strategy_return:.2f}")
print(f"Final cumulative return of market average: {final_market_return:.2f}")

strategy_daily_returns = portfolio_returns['Portfolio Return'].dropna()
sharpe_ratio = (strategy_daily_returns.mean() / strategy_daily_returns.std()) * np.sqrt(12)
print(f"Sharpe Ratio of the sector rotation strategy: {sharpe_ratio:.2f}")