FactorMaxLimit error

[marketneutral]

I am getting an exception when trying to use FactorMaxLimit and FactorMinLimit.

This is the minimal reproducible code:

import numpy as np
import pandas as pd
import cvxportfolio as cp

np.random.seed(100)

# spoof a factor loadings matrix with 100 stocks and 6 factors (e.g., think sectors)
factor_loadings = pd.get_dummies(pd.Series(np.random.randint(0,6,100)))

# create the expected returns DataFrame
r_hat_s = pd.Series(np.random.random(100))
r_hat_s.T['USDOLLAR']=0
r_hat = pd.DataFrame(r_hat_s, columns=[pd.Timestamp('2017-01-03')]).T

# create the prices DataFrame
prices_s = pd.Series(np.random.randint(20, 75, 100))
prices_noUSD = pd.DataFrame(prices_s, columns=[pd.Timestamp('2017-01-03')]).T
prices_s.T['USDOLLAR']=1.0
prices = pd.DataFrame(prices_s, columns=[pd.Timestamp('2017-01-03')]).T

# set up the optimization policy
spo_policy = cp.SinglePeriodOpt(
    return_forecast=r_hat,
    costs=[],
    constraints=[
        cp.LeverageLimit(1),
        cp.constraints.DollarNeutral(),
        cp.constraints.MaxWeights(0.10),
        cp.constraints.MinWeights(-0.10),
        cp.FactorMaxLimit(factor_loadings, 0.2),
        cp.FactorMinLimit(factor_loadings, -0.2)        
    ]
)

# setup empty starting portfolio
current_portfolio = pd.Series(index=r_hat.columns, data=0)
current_portfolio.USDOLLAR=100000

# run the single period optimization
shares_to_trade = spo_policy.get_rounded_trades(
    current_portfolio,
    prices,
    t=pd.Timestamp('2017-01-04')
)

If I eliminate the lines

        cp.FactorMaxLimit(factor_loadings, 0.2),
        cp.FactorMinLimit(factor_loadings, -0.2) 

then the code runs fine and produces the optimal trade list.

With the FactorMaxLimit, I get the following exception:

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-4-542a0a7449e6> in <module>()
     36     current_portfolio,
     37     prices,
---> 38     t=pd.Timestamp('2017-01-04')
     39 )

/Users/jonathan/devwork/cvxportfolio/cvxportfolio/policies.pyc in get_rounded_trades(self, portfolio, prices, t)
     52         """Get trades vector as number of shares, rounded to integers."""
     53         return np.round(self.get_trades(portfolio,
---> 54                                         t) / time_locator(prices, t))[:-1]
     55 
     56 

/Users/jonathan/devwork/cvxportfolio/cvxportfolio/policies.pyc in get_trades(self, portfolio, t)
    269 
    270         constraints += [item for item in (con.weight_expr(t, wplus, z, value)
--> 271                                           for con in self.constraints)]
    272 
    273         for el in costs:

/Users/jonathan/devwork/cvxportfolio/cvxportfolio/policies.pyc in <genexpr>((con,))
    269 
    270         constraints += [item for item in (con.weight_expr(t, wplus, z, value)
--> 271                                           for con in self.constraints)]
    272 
    273         for el in costs:

/Users/jonathan/devwork/cvxportfolio/cvxportfolio/constraints.py in weight_expr(self, t, w_plus, z, v)
     46         if w_plus is None:
     47             return self._weight_expr(t, None, z, v)
---> 48         return self._weight_expr(t, w_plus - self.w_bench, z, v)
     49 
     50     @abstractmethod

/Users/jonathan/devwork/cvxportfolio/cvxportfolio/constraints.py in _weight_expr(self, t, w_plus, z, v)
    224         """
    225         #import pdb; pdb.set_trace()
--> 226         if isinstance(self.limit, pd.Series):
    227             limit = self.limit.loc[t]
    228         else:

/anaconda3/envs/py27/lib/python2.7/site-packages/pandas/core/ops.pyc in f(self, other, axis, level, fill_value)
   1265                 self = self.fillna(fill_value)
   1266 
-> 1267             return self._combine_const(other, na_op)
   1268 
   1269     f.__name__ = name

/anaconda3/envs/py27/lib/python2.7/site-packages/pandas/core/frame.pyc in _combine_const(self, other, func, errors, try_cast)
   3985         new_data = self._data.eval(func=func, other=other,
   3986                                    errors=errors,
-> 3987                                    try_cast=try_cast)
   3988         return self._constructor(new_data)
   3989 

/anaconda3/envs/py27/lib/python2.7/site-packages/pandas/core/internals.pyc in eval(self, **kwargs)
   3433 
   3434     def eval(self, **kwargs):
-> 3435         return self.apply('eval', **kwargs)
   3436 
   3437     def quantile(self, **kwargs):

/anaconda3/envs/py27/lib/python2.7/site-packages/pandas/core/internals.pyc in apply(self, f, axes, filter, do_integrity_check, consolidate, **kwargs)
   3327 
   3328             kwargs['mgr'] = self
-> 3329             applied = getattr(b, f)(**kwargs)
   3330             result_blocks = _extend_blocks(applied, result_blocks)
   3331 

/anaconda3/envs/py27/lib/python2.7/site-packages/pandas/core/internals.pyc in eval(self, func, other, errors, try_cast, mgr)
   1321             return block.eval(func, orig_other,
   1322                               errors=errors,
-> 1323                               try_cast=try_cast, mgr=mgr)
   1324 
   1325         # get the result, may need to transpose the other

/anaconda3/envs/py27/lib/python2.7/site-packages/pandas/core/internals.pyc in eval(self, func, other, errors, try_cast, mgr)
   1396 
   1397                 raise TypeError('Could not compare [%s] with block values' %
-> 1398                                 repr(other))
   1399 
   1400         # transpose if needed

TypeError: Could not compare [Expression(AFFINE, UNKNOWN, (100,))] with block values

This is failing on this line in constraints.py:

self.factor_exposure.T * w_plus[:-1] <= limit

I've tried to debug with no success. Any ideas on this?

Lastly, thank you for a great package!

@weiyialanchen

[wec7]

@marketneutral

If you change those two lines in this way, you will be good (i.e. the first argument is np.array instead of pd.DataFrame) -

        cp.FactorMaxLimit(factor_loadings.values, 0.2),
        cp.FactorMinLimit(factor_loadings.values, -0.2)

Apologies for the inconvenience.

[marketneutral]

Thanks @weiyialanchen for coming back so quickly. Yes, that did the trick! Fantastic.