One issue with using standard deviation of returns as a risk measure is that the positive deviation is also viewed as bad. The second issue is that the deviation is from the average instead of a fixed benchmark, such as a risk-free rate. To overcome these shortcomings, Sortino (1983) suggests the lower partial standard deviation, which is defined as the average of squared deviation from the risk-free rate conditional on negative excess returns, as shown in the following formula:

Because we need the risk-free rate in this equation, we could generate a Fama-French dataset that includes the risk-free rate as one of their time series. First, download their daily factors from http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library.html.Then, unzip it and delete the non-data part at the end of the text file. Assume the final text file is saved under C:/temp/:

import pandas as pd
import datetime
file=open("c:/temp/F-F_Research_Data_Factors_daily.txt"...