ma_crossover.py

# sample importing from your own library
#from samplelib.lib1 import print_something

from zipline.api import order_target, record, symbol, symbols, history
from zipline import run_algorithm

from time import time
import datetime
import pytz
import matplotlib.pyplot as plt
import pandas as pd

def initialize(context):
    context.i = 0
    context.assets = symbols('AAPL','AMZN','MSFT','GOOG')


def handle_data(context, data):    
    # Skip first 200 days to get full windows
    context.i += 1
    if context.i < 200:
        return

    start = time()
    for asset in context.assets:
        # Compute averages
        # data.history() has to be called with the same params
        # from above and returns a pandas dataframe.
        short_mavg =  data.history(asset, 'price', bar_count=50, frequency="1d").mean()
        long_mavg = data.history(asset, 'price', bar_count=200, frequency="1d").mean()

        # Trading logic
        if short_mavg > long_mavg:
            # order_target orders as many shares as needed to
            # achieve the desired number of shares.
            order_target(asset, 100)
        elif short_mavg < long_mavg:
            order_target(asset, 0)

        # Save values for later inspection
        record(**{
                    asset.symbol:data.current(asset, 'price'),
                    asset.symbol + "short_mavg": short_mavg,
                    asset.symbol + "long_mavg" : long_mavg
                })
    
    end = time()
    print("handle data time", end-start)


def __main__():
    start = time()
    perf = run_algorithm(start=datetime.datetime(2010, 1, 1,tzinfo=pytz.utc),end=datetime.datetime(2018,1,1,tzinfo=pytz.utc),
                                 handle_data=handle_data,
                                 initialize=initialize,
                                 capital_base=10000000,
                                 bundle='quandl')    
    end = time()
    print("Core backtesting took: " , end-start)

__main__()