# Put any initialization logic here.  The context object will be passed to  
# the other methods in your algorithm.  
import numpy as np  
import math  
from sqlalchemy import or_  
def initialize(context):  
    context.limit = 500  
    schedule_function(rebalance,  
                      date_rule = date_rules.month_start(),  
                      time_rule = time_rules.market_open())  
def before_trading_start(context):  
    context.security = get_fundamentals(  
                        query(fundamentals.valuation.market_cap,  
                             fundamentals.company_reference.primary_exchange_id)  
                        .filter(fundamentals.valuation.market_cap > 4e9)  
                        .filter(fundamentals.company_reference.country_id == "USA")  
                        .filter(or_(fundamentals.company_reference.primary_exchange_id == "NAS", fundamentals.company_reference.primary_exchange_id == "NYS"))  
                        .order_by(fundamentals.valuation.market_cap.desc())  
                        .limit(500)  
                       )  # S&P 500 rebalances on an as needed basis, so we'll just use today  
    update_universe(context.security.columns.values)  
def rebalance(context,data):  
    for stock in data:  
        prices = history(bar_count = 253, frequency = '1d', field = 'price')  
        pct_change = (prices.ix[-252] - prices.ix[232]) / prices.ix[232]  
        pct_change2 = (prices.ix[-126]-prices.ix[-42]) / prices.ix[-42]  
        theory1 = pct_change+pct_change2  
        uptheory1 = np.percentile(theory1, 50)  
        most_theory = theory1[theory1>uptheory1]  
        amount = context.portfolio.positions[stock].amount  
        if stock not in most_theory:  
            order(stock, -amount)


# Will be called on every trade event for the securities you specify.  
def handle_data(context, data):  
    prices = history(bar_count = 253, frequency = '1d', field = 'price')  
    pct_change = (prices.ix[-252] - prices.ix[232]) / prices.ix[232]  
    pct_change2 = (prices.ix[-126]-prices.ix[-42]) / prices.ix[-42]  
    theory1 = pct_change+pct_change2  
    uptheory1 = np.percentile(theory1, 50)  
    most_theory = theory1[theory1>uptheory1]  
    #print uptheory1  
    print most_theory  
    #print pct_change  
    #print pct_change2  
    for stock in context.stocks:  
        cash = context.portfolio.cash  
        price = data[stock].price  
        if stock in most_theory:  
            if cash > 0:  
                hope = (math.floor(cash/price))/4  
                order(stock, hope)