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| import yfinance as yf | |
| from scipy.optimize import minimize | |
| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| def get_historical_returns(tickers, start_date, end_date): | |
| """ | |
| Fetch historical returns data for the given tickers. | |
| Args: | |
| - tickers: list of strings, tickers of assets | |
| - start_date: string, start date in the format 'YYYY-MM-DD' | |
| - end_date: string, end date in the format 'YYYY-MM-DD' | |
| Returns: | |
| - pandas DataFrame, historical returns data | |
| """ | |
| data = yf.download(tickers, start=start_date, end=end_date)['Adj Close'] | |
| returns = data.pct_change().dropna() | |
| return returns | |
| def get_risk_free_rate_india(): | |
| """ | |
| Get the risk-free rate for the Indian market using the yield of the 10-year Indian Government Bond. | |
| Returns: | |
| - float, risk-free rate | |
| """ | |
| # Ticker symbol for the 10-year Indian Government Bond yield | |
| bond_ticker = 'INR=X' # You can replace this with the actual ticker symbol for the bond | |
| # Fetch the bond data | |
| bond_data = yf.Ticker(bond_ticker) | |
| # Get the latest yield | |
| risk_free_rate_india = bond_data.history(period='1d')['Close'][-1] / 100 | |
| return risk_free_rate_india | |
| def sharpe_ratio(weights, returns, risk_free_rate): | |
| """ | |
| Calculate the Sharpe Ratio of a portfolio. | |
| Args: | |
| - weights: array-like, weights of assets in the portfolio | |
| - returns: pandas DataFrame, historical returns of assets | |
| - risk_free_rate: float, risk-free rate of return | |
| Returns: | |
| - float, Sharpe Ratio of the portfolio | |
| """ | |
| portfolio_return = np.sum(weights * returns.mean() * 252) # 252 trading days in a year | |
| portfolio_std_dev = np.sqrt(np.dot(weights.T, np.dot(returns.cov() * 252, weights))) | |
| sharpe_ratio = (portfolio_return - risk_free_rate) / portfolio_std_dev | |
| return -sharpe_ratio # Minimize negative Sharpe Ratio for maximization | |
| def optimize_portfolio(returns, risk_free_rate): | |
| """ | |
| Optimize portfolio to maximize the Sharpe Ratio. | |
| Args: | |
| - returns: pandas DataFrame, historical returns of assets | |
| - risk_free_rate: float, risk-free rate of return | |
| Returns: | |
| - array, optimal weights of assets in the portfolio | |
| """ | |
| num_assets = len(returns.columns) | |
| initial_weights = np.array([1 / num_assets] * num_assets) | |
| bounds = [(0, 1)] * num_assets # Bounds for asset weights (0 <= weight <= 1) | |
| constraints = ({'type': 'eq', 'fun': lambda weights: np.sum(weights) - 1}) # Sum of weights equals 1 constraint | |
| optimized_result = minimize(sharpe_ratio, initial_weights, args=(returns, risk_free_rate), | |
| method='SLSQP', bounds=bounds, constraints=constraints) | |
| return optimized_result.x |