Hi this is the first algorithm I've written and it is very simple. I want to implement an approximation of the 'magic formula' of Joel Greenblatt. I want the algorithm to:

1. run once a month at market open on the first trading day of the month
   
2. find the list of stocks at this time that have greater than 25% return on equity
   
3. sort this list to find the company with the lowest price to earnings ratio greater than five
   
4. place a market order for that stock to a value of $83333
   
5. after exactly one year of holding the stock, place a market sell order
   
6. repeat until end of back test.

Here is my code so far:

def before_trading_start(context):  
    fundamental_df = get_fundamentals(  
        query(  
            fundamentals.operation_ratios.roa,  
            fundamentals.valuation_ratios.pe_ratio  
        )  
        .filter(fundamentals.operation_ratios.roa > 0.25)  
        .filter(fundamentals.valuation_ratios.pe_ratio > 5)  
        .order_by(fundamentals.valuation_ratios.pe_ratio)  
        .limit(1)  
    )  
    update_universe(context.fundamental_df.columns.values)

def initialize(context):  
    pass

def handle_data(context, data):  
    order_value(security, 83333)  

I'm pretty sure the before_trading_start(context) method will put one stock into my universe that has the required attributes but I'm not really sure where to go from here.

My questions are:

1. How do I access the stock that I placed into the universe? i.e. how do I buy the stock that the algorithm has found

2. How do I get the algorithm to run once at the start of every month? also if the start of the month is a day that the market is not open, like a weekend, how would that affect the algorithm?

Thanks any help appreciated.