human
Solve the following python programming problem given the following code:
# Custom Factor 1 : Price to Trailing 12 Month Sales       
class Price_to_TTM_Sales(CustomFactor):
    inputs = [morningstar.valuation_ratios.ps_ratio]
    window_length = 1
    
    def compute(self, today, assets, out, ps):
        out[:] = ps[-1]

# create pipeline
temp_pipe_1 = Pipeline()

# add our factor
temp_pipe_1.add(Price_to_TTM_Sales(), 'Price / TTM Sales')

# add sector classifier
temp_pipe_1.add(Sector(), 'Sector')

# get data
temp_res_1 = run_pipeline(temp_pipe_1, '2015-06-06', '2015-06-06')

# show first 15 rows
temp_res_1.head(15)
Given the above code, generate some markdown instructions for it.
assistant
#Here is the Python code you requested.

Relative Value
The metrics used to create this strategy are given by Lo/Patel as:

Industry-Relative Price to Trailing 12-Month Sales
Industry-Relative Price to Trailing 12-Month Earnings
Industry-Relative Price to Trailing 12-Month Cash Flows

While many of these metrics may seem similar to those of the last notebook, the key addition is the fact that they are taken in the context of their own industry. In this notebook, we will see how this industry-relative approach captures certain nuances that more general factors miss.
Industry-Relative Price to TTM Sales
This is calculated as a simple ratio between price per share and trailing 12-month (TTM) sales. This data will eventually be standardized over industry groups. The standardization procedure is explained later in the notebook.
Let us create a factor for this metric and have a look at the data. For an introduction to Pipeline Factors, have a look at (https://www.quantopian.com/posts/quantopian-lecture-series-long-slash-short-traditional-value-case-study)
NB: Like the previous notebook, we are using Morningstar's built-in Price to Sales Ratio as an approximation of Price to TTM Sales for speed of computation.