The code and backtest are attached below. I'm sure this is an easy answer with an easy solution, but my lack of knowledge with the migration is making this difficult.

# Implementation of - Volatility Weighted Momentum  
# as described in the paper Adaptive Asset Allocation: A Primer  
# which can be found here -  http://bpgassociates.com/docs/Adaptive-Asset-Allocation-A-Primer.pdf  
# the Volatility Weighted Momentum algorithm is described in Exhibit 4 of the paper

import math

def initialize(context):  
    set_symbol_lookup_date('2005-01-01')  
    #Asset #1 - test 7 asset scenario from paper - the small global multi asset universe  
    # from Keller/Butler - Elastic Asset Allocation (EAA)  
    #removed HYG from sample set to allow for longer backtest  
    '''  
    context.etfs = symbols('SPY', #proxy for S&P  
                           'EFA', #proxy for EAFE  
                           'EEM', #proxy for EEM  
                           'QQQ', #proxy for US Tech  
                           'EWJ', #proxy for Japan Topix  
                           'IEF') #proxy for US Govt 10 yr  
    '''  
    #Asset #2 - test a more generalized distribution of assets  
    #US stocks, European stocks, Emerging market stocks, US bonds, gold, real estate  
    #context.etfs = symbols('SPY','EFA','EEM','IEF','GLD','IYR')  
    #Asset #3 - test against S&P sectors to estimate upside extraction  
    context.etfs = [symbols('XLY',  # XLY Consumer Discrectionary SPDR Fund  
                           'IYR', #Real Estate ETF in place of XLFS  
                           'IYG', #Financial Services etf in place of  
                           'XLF',  # XLF Financial SPDR Fund  
                           'XLK',  # XLK Technology SPDR Fund  
                           'XLE',  # XLE Energy SPDR Fund  
                           'XLV',  # XLV Health Care SPRD Fund  
                           'XLI',  # XLI Industrial SPDR Fund  
                           'XLP',  # XLP Consumer Staples SPDR Fund  
                           'XLB',  # XLB Materials SPDR Fund  
                           'XLU')  # XLU Utilities SPRD Fund  
                    ]  
    context.mom_lookback = 240  
    context.risk_free_asset = symbol('SHY')  
    context.target_vol = 0.01 #target 1% volatility  
    context.vol_lookback = 60  
    schedule_function(rebalance,date_rules.month_end(days_offset=0), time_rules.market_close(minutes=60))  
#calculate volatility for etf universe  
def get_vol(context):  
    h = data.history(context.etfs, 'price', context.vol_lookback*2, '1d')  
    hs = h.ix[-1*context.vol_lookback:]

    context.hvol = hs.pct_change().std()  
#calculate weights based on relative momentum ranking and absolute momentum test  
def get_mom_weights(context):  
    #init weights  
    context.weights = {}  
    for s in context.etfs:  
        context.weights[s] = 0  
    h = data.history(context.etfs, 'price', context.vol_lookback*2, '1d')  
    h = h.resample('M',how='last')  
    #drop first row because it is nan  
    pct_change =h.iloc[[0,-2]].pct_change()[1:]  
    #drop any other nan values  
    pct_change = pct_change.dropna(axis=1)  
    #convert dataframe to series for sorting. Then sort in descending order  
    context.pct_change_series =  pct_change.squeeze().order(ascending=False)  
    l = int(math.floor(len(context.etfs)/2.0))  
    context.weight = 1.0/l  
    for i in range(l) :  
        if context.pct_change_series.ix[i] > 0.0 :  
            s = context.pct_change_series.index[i]  
            context.weights[s] = context.weight

def rebalance(context,data):  
    get_vol(context)  
    get_mom_weights(context)  
    total_weight = 0.0  
    for s in context.etfs:  
        mom_vol_weight = context.weights[s]*(context.target_vol/context.hvol[s])  
        if mom_vol_weight > context.weight:  
            mom_vol_weight = context.weight  
        order_target_percent(s,mom_vol_weight)  
        total_weight += mom_vol_weight  
    if total_weight < 1.0:  
        order_target_percent(context.risk_free_asset,1.0-total_weight)  
    else:  
        order_target_percent(context.risk_free_asset,0.0)  

# Will be called on every trade event for the securities you specify.  
def handle_data(context, data):  
    record(leverage = context.account.leverage)