Zipline is the algorithmic trading library that powers the Quantopian backtesting and live-trading platform. It is also available offline to develop a strategy using a limited number of free data bundles that can be ingested and used to test the performance of trading ideas before porting the result to the online Quantopian platform for paper and live trading.

The following code illustrates how zipline permits us to access daily stock data for a range of companies. You can run zipline scripts in the Jupyter Notebook using the magic function of the same name.

First, you need to initialize the context with the desired security symbols. We'll also use a counter variable. Then zipline calls handle_data, where we use the data.history() method to look back a single period and append the data for the last day to a .csv file:

%load_ext zipline
%%zipline --start 2010-1-1 --end 2018-1-1 --data-frequency daily
from zipline.api import order_target, record, symbol

def initialize(context):
 context.i = 0
 context.assets = [symbol('FB'), symbol('GOOG'), symbol('AMZN')]
 
def handle_data(context, data):
 df = data.history(context.assets, fields=['price', 'volume'], 
                   bar_count=1, frequency="1d")
 df = df.to_frame().reset_index()
 
 if context.i == 0:
 df.columns = ['date', 'asset', 'price', 'volumne']
 df.to_csv('stock_data.csv', index=False)
 else:
     df.to_csv('stock_data.csv', index=False, mode='a', header=None)
                context.i += 1

df = pd.read_csv('stock_data.csv')
df.date = pd.to_datetime(df.date)
df.set_index('date').groupby('asset').price.plot(lw=2, legend=True, 
       figsize=(14, 6));

We get the following plot for the preceding code:

We will explore the capabilities of zipline, and, in particular, the online Quantopian platform, in more detail in the coming chapters.