Two days without sleep and still getting an error.

ValueError: need more than 1 value to unpack
... USER ALGORITHM:36, in trade

Here's the code.

import numpy as np  
import scipy  
import pandas as pd  
import statsmodels.api as sm

def initialize(context):  
    context.longleverage = 0.5  
    context.shortleverage = -0.5  
    context.reduce_correlation = True  
    context.reference = symbol('SPY')  
    # There's bad data for this security so I ignore it  
    context.ignores = [sid(7143)]  
    set_universe(universe.DollarVolumeUniverse(98.0, 100.0))

    schedule_function(trade,  
                      date_rule=date_rules.every_day(),  
                      time_rule=time_rules.market_open(minutes=20))  
def handle_data(context, data):  
    leverage=context.account.leverage  
    exposure=context.account.net_leverage  
    record(leverage=leverage, exposure=exposure)  
def trade(context, data):  
    p = history(100, '1d', 'price').dropna(axis=1)  
    p0 = p.iloc[-11:-1]  
    p1 = p.shift(1).iloc[-11:-1]  
    for stock in p:  
        n,k = regression(p0,p1,context.reference,stock)  
    # ranks = ...  
    # Take the top and botton percentiles for the long and short baskets  
    lower, upper = ranks.quantile([.05, .95])  
    shorts = ranks[ranks <= lower]  
    longs = ranks[ranks >= upper]  
    # Get weights that reduce the correlation within each basket  
    if context.reduce_correlation:  
        R = p.pct_change().dropna()  
        longs = get_reduced_correlation_weights(R[longs.index])  
        shorts = get_reduced_correlation_weights(R[shorts.index])  
    else:  
        # Use even weights  
        longs = longs.abs()  
        longs /= longs.sum()  
        shorts = shorts.abs()  
        shorts /= shorts.sum()  
    for stock in data:  
        if stock in context.ignores:  
            continue  
        try:  
            if get_open_orders(sid = stock):  
                continue  
            elif stock in shorts.index:  
                order_target_percent(stock,  
                                     context.shortleverage * shorts[stock])  
            elif stock in longs.index:  
                order_target_percent(stock,  
                                     context.longleverage * longs[stock])  
            else:  
                order_target(stock, 0)  
        except:  
            log.warn("[Failed Order] stock = %s"%stock.symbol)  
def get_reduced_correlation_weights(returns, risk_adjusted=True):  
    """  
    Implementation of minimum correlation algorithm.  
    ref: http://cssanalytics.com/doc/MCA%20Paper.pdf  
    :Params:  
        :returns <Pandas DataFrame>:Timeseries of asset returns  
        :risk_adjusted <boolean>: If True, asset weights are scaled  
                                  by their standard deviations  
    """  
    correlations = returns.corr()  
    adj_correlations = get_adjusted_cor_matrix(correlations)  
    initial_weights = adj_correlations.T.mean()

    ranks = initial_weights.rank()  
    ranks /= ranks.sum()

    weights = adj_correlations.dot(ranks)  
    weights /= weights.sum()

    if risk_adjusted:  
        weights = weights / returns.std()  
        weights /= weights.sum()  
    return weights

def get_adjusted_cor_matrix(cor):  
    values = cor.values.flatten()  
    mu = np.mean(values)  
    sigma = np.std(values)  
    distribution = scipy.stats.norm(mu, sigma)  
    return 1 - cor.apply(lambda x: distribution.cdf(x))

# Linear regression  
def regression(p0, p1, stock0, stock1):  
    s0 = p0[stock0]  
    s1 = sm.add_constant(p1[stock1], prepend=True)  
    return sm.OLS(s0, s1).fit().params  

The line with the error is

n,k = regression(p0,p1,context.reference,stock)

Any help will be greatly appreciated.