Backtest using Backtrader issue

[dbogatic]

I tried running your Backtest with Backtrader example and got stuck at this code:

models = {}

rms = ['MV', 'CVaR', 'WR', 'CDaR']

for j in rms:
    
    weights = pd.DataFrame([])

    for i in index_:
        Y = returns.iloc[i-1000:i,:] # taking last 4 years (250 trading days per year)

        # Building the portfolio object
        port = pf.Portfolio(returns=Y)
        
        # Add portfolio constraints
        port.ainequality = A
        port.binequality = B
        
        # Calculating optimum portfolio

        # Select method and estimate input parameters:

        method_mu='hist' # Method to estimate expected returns based on historical data.
        method_cov='hist' # Method to estimate covariance matrix based on historical data.

        port.assets_stats(method_mu=method_mu, method_cov=method_cov, d=0.94)
        
        # Estimate optimal portfolio:
        
        port.solvers = ['MOSEK']
        port.alpha = 0.05
        model='Classic' # Could be Classic (historical), BL (Black Litterman) or FM (Factor Model)
        rm = j # Risk measure used, this time will be variance
        obj = 'Sharpe' # Objective function, could be MinRisk, MaxRet, Utility or Sharpe
        hist = True # Use historical scenarios for risk measures that depend on scenarios
        rf = 0 # Risk free rate
        l = 0 # Risk aversion factor, only useful when obj is 'Utility'

        w = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)

        if w is None:
            w = weights.tail(1).T
        weights = pd.concat([weights, w.T], axis = 0)
    
    models[j] = weights.copy()
    models[j].index = index_ 

I am getting the following error:

How can I resolve this? Thank you.

[dcajasn]

You need a valid Mosek license to run this example. You can comment Mosek line to run without Mosek.

[dbogatic]

Oh, yes, you did mention this. I subscribed to their academic license. I will try it out. Thank you for the quick response, as always!