1. To get started, we will work with Tesla Motors (TSLA) stock and presume a purchase on the first trading day of 2017:

>>> import pandas_datareader as pdr
>>> tsla = pdr.DataReader('tsla', data_source='google',
                          start='2017-1-1')
>>> tsla.head(8)

2. For simplicity, we will work with the closing price of each trading day:

>>> tsla_close = tsla['Close']

3. Use the cummax method to track the highest closing price until the current date:

>>> tsla_cummax = tsla_close.cummax()
>>> tsla_cummax.head(8)
Date
2017-01-03    216.99
2017-01-04    226.99
2017-01-05    226.99
2017-01-06    229.01
2017-01-09    231.28
2017-01-10    231.28
2017-01-11    231.28
2017-01-12    231.28
Name: Close, dtype: float64

4. To limit the downside to 10%, we multiply tsla_cummax by 0.9. This creates the trailing stop order:

>>> tsla_trailing_stop = tsla_cummax * .9
>>> tsla_trailing_stop.head(8)
Date
2017-01-03    195.291
2017-01-04    204.291
2017-01-05    204.291
2017-01-06    206.109
2017-01-09    208.152
2017-01-10    208.152
2017-01-11    208.152
2017-01-12    208.152
Name: Close, dtype: float64