The problem doesn't have a solution with actual input parameters

[2611999]

Hey guys,
I am facing this problem and I cannot find a solution for this vanilla risk parity portfolio optimization.

The code is the following:

port = rp.Portfolio(returns=underlying_returns)
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)

model='Classic' # Could be Classic (historical), BL (Black Litterman) or FM (Factor Model)
rm = 'MV' # Risk measure used, this time will be variance
hist = True # Use historical scenarios for risk measures that depend on scenarios
rf = 0 # Risk free rate

b = None # Risk contribution constraints vector

w_rp = port.rp_optimization(model=model, rm=rm, rf=rf, b=b, hist=hist)

where the variable underlying_returns is a dataframe containing returns of sp500, eurostoxx 50 and 10 years note futures.

It comes up with 'The problem doesn't have a solution with actual input parameters'
Can someone help me please? Don't hesitate to ask me more detail if you need it