def test_prices_from_returns():
df = get_data()
returns_df = df.pct_change() # keep NaN row
# convert pseudo-price to price
pseudo_prices = expected_returns.prices_from_returns(returns_df)
initial_prices = df.bfill().iloc[0]
test_prices = pseudo_prices * initial_prices
# check equality, robust to floating point issues
assert ((test_prices[1:] - df[1:]).fillna(0) < 1e-10).all().all()
def optimize_max_sharpe(returns_df, lowerbound=0, upperbound=1):
wb = (lowerbound, upperbound)
ema_returns_df = ema_historical_return(prices_from_returns(returns_df))
returns_cov_df = returns_df.cov()
EFOptimizer = EfficientFrontier(ema_returns_df,
returns_cov_df,
weight_bounds=wb)
weights_dict = EFOptimizer.max_sharpe()
return weights_dict