Proof-reading my Python codes and logic

I have a short excerpt from a script I got somewhere and would like to clarify the logic behind it with the experts. The script comes from a program for backtesting a mean-reversion strategy.

def create_long_short_market_signals(pairs, symbols, z_entry_threshold=2.0, z_exit_threshold=1.0):

I assume the above function creates the entry and exit criteria of Z-score. The criteria is to enter at 2.0 Standard Deviation and exit when Z-score falls to 1.0 Standard Deviation.

    pairs['longs'] = (pairs['zscore'] <= -z_entry_threshold)*1.0  
    pairs['shorts'] = (pairs['zscore'] >= z_entry_threshold)*1.0  
    pairs['exits'] = (np.abs(pairs['zscore']) <= z_exit_threshold)*1.0

The above codes within the def function() is to go Long when Zscore is -2.0SD and Short when Zscore is +2.0SD. The signal will exit when Zscore falls to <= +1.0SD.

I think the above codes, I can understand the logic for the program to create a Short and Long command if Zscore is above 2.0 Standard Deviation or below -2.0 Standard Deviation.

But regarding the Exit command I need some clarification about my understanding of it.

Q1) The program is written by telling the signal to exit the trade only when Zscore is <= 1.0. So assuming between -2.0SD to 1.0SD, the program continues to stay Long and it do nothing even if the signal crosses a Zscore of -1.0?

Q2) Also, if we want to create both an Exit signal of 1.0 Standard Dev and -1.0 Standard Dev, we should write the functions code as per below am I correct? :

def create_long_short_market_signals(pairs, symbols, z_entry_threshold_1=2.0, z_entry_threshold_2= -2.0, z_exit_threshold_1=1.0, z_exit_threshold_2= -1.0):

If anyone is interested to see the full function, it is below. The codes are written with no specific backtesting libraries. Its just python and pandas.

# mr_spy_iwm.py  
def create_long_short_market_signals(pairs, symbols,  
                                 z_entry_threshold=2.0,  
                                 z_exit_threshold=1.0):  
    """Create the entry/exit signals based on the exceeding of  
    z_enter_threshold for entering a position and falling below  
    z_exit_threshold for exiting a position."""

    # Calculate when to be long, short and when to exit  
    pairs['longs'] = (pairs['zscore'] <= -z_entry_threshold)*1.0  
    pairs['shorts'] = (pairs['zscore'] >= z_entry_threshold)*1.0  
    pairs['exits'] = (np.abs(pairs['zscore']) <= z_exit_threshold)*1.0

    # These signals are needed because we need to propagate a  
    # position forward, i.e. we need to stay long if the zscore  
    # threshold is less than z_entry_threshold by still greater  
    # than z_exit_threshold, and vice versa for shorts.  
    pairs['long_market'] = 0.0  
    pairs['short_market'] = 0.0

    # These variables track whether to be long or short while  
    # iterating through the bars  
    long_market = 0  
    short_market = 0

    # Calculates when to actually be "in" the market, i.e. to have a  
    # long or short position, as well as when not to be.  
    # Since this is using iterrows to loop over a dataframe, it will  
    # be significantly less efficient than a vectorised operation,  
    # i.e. slow!  
    print "Calculating when to be in the market (long and short)..."  
    for i, b in enumerate(pairs.iterrows()):  
        # Calculate longs  
        if b[1]['longs'] == 1.0:  
            long_market = 1  
        # Calculate shorts  
        if b[1]['shorts'] == 1.0:  
            short_market = 1  
        # Calculate exists  
        if b[1]['exits'] == 1.0:  
            long_market = 0  
            short_market = 0  
        # This directly assigns a 1 or 0 to the long_market/short_market  
        # columns, such that the strategy knows when to actually stay in!  
        pairs.ix[i]['long_market'] = long_market  
        pairs.ix[i]['short_market'] = short_market  
    return pairs

I hope my question is asked in a clear manner and people can understand what I'm trying to ask. Thanks a lot in advance :)