import portfolio_optimizer as po

from po.load_data import LoadData   # for loading data
from po.portfolio import Portfolio  # for portfolio management
from po.optimizer import Optimizer  # for optimization purpose
from po.frontier import EfficientFrontier  # for the plot of Efficient Frontier

# loading the data from NSE exchange you can use BSE also
ld = LoadData()
data_dic = ld.load_data(['HDFCBANK', 'RELIANCE', 'TCS', 'INFY', 'BAJAJ-AUTO', 'TATAMOTORS'], 'NSE')

# loading the benchmark separately, Indices are always downloaded from nasdaq
benchmark = ld.load_data(['^NSEI'], 'nasdaq')

# Creating a Portfolio object, with the freq="M" and benchmark as NSEI (Nifty 50)
portfolio = Portfolio(freq="M", benchmark=benchmark)


# Adding the stocks to the portfolio
portfolio.add_stock(data_dic)

# You can view you Portfolio summary also by
portfolio.portfolio_summary()

# Creating a Optimizer object and adding the portfolio
model = Optimizer()
model.add_portfolio(portfolio=portfolio)

# Optimizing the portfolio using CAPM
risk = 1
model_ = "capm"
optimized_res = model.optimize_portfolio(model=model, max_risk=risk)
print(optimized_res)