Zipline is an open-source algorithmic trading simulator written in Python.
The source can be found at: https://github.com/quantopian/zipline
Some benefits include:
This tutorial assumes that you have zipline correctly installed, see the installation instructions if you haven't set up zipline yet.
Every zipline algorithm consists of two functions you have to define:
initialize(context)handle_data(context, data)Before the start of the algorithm, zipline calls the initialize() function and passes in a context variable. context is a persistent namespace for you to store variables you need to access from one algorithm iteration to the next.
After the algorithm has been initialized, zipline calls the handle_data() function once for each event. At every call, it passes the same context variable and an event-frame called data containing the current trading bar with open, high, low, and close (OHLC) prices as well as volume for each stock in your universe. For more information on these functions, see the relevant part of the Quantopian docs.
In [1]:
# assuming you're running this notebook in zipline/docs/notebooks
import os
if os.name == 'nt':
# windows doesn't have the cat command, but uses 'type' similarly
! type "..\..\zipline\examples\buyapple.py"
else:
! cat ../../zipline/examples/buyapple.py
As you can see, we first have to import some functions we would like to use. All functions commonly used in your algorithm can be found in zipline.api. Here we are using order() which takes two arguments -- a security object, and a number specifying how many stocks you would like to order (if negative, order() will sell/short stocks). In this case we want to order 10 shares of Apple at each iteration. For more documentation on order(), see the Quantopian docs.
Finally, the record() function allows you to save the value of a variable at each iteration. You provide it with a name for the variable together with the variable itself: varname=var. After the algorithm finished running you will have access to each variable value you tracked with record() under the name you provided (we will see this further below). You also see how we can access the current price data of the AAPL stock in the data event frame (for more information see here).
Before we can run the algorithm, we'll need some historical data for our algorithm to ingest, which we can get through a data bundle. A data bundle is a collection of pricing data, adjustment data, and an asset database. Bundles allow us to preload all of the data we will need to run backtests and store the data for future runs. Quantopian provides a default bundle called quandl which uses the Quandl WIKI Dataset. You'll need a Quandl API Key, and then you can ingest that data by running:
In [ ]:
! QUANDL_API_KEY=<yourkey> zipline ingest -b quandl
For more information on data bundles, such as building custom data bundles, you can look at the zipline docs.
To now test this algorithm on financial data, zipline provides two interfaces. A command-line interface and an IPython Notebook interface.
After you installed zipline you should be able to execute the following from your command line (e.g. cmd.exe on Windows, or the Terminal app on OSX):
In [24]:
!zipline run --help
Note that you have to omit the preceding '!' when you call run_algo.py, this is only required by the IPython Notebook in which this tutorial was written.
As you can see there are a couple of flags that specify where to find your algorithm (-f) as well as the time-range (--start and --end). Finally, you'll want to save the performance metrics of your algorithm so that you can analyze how it performed. This is done via the --output flag and will cause it to write the performance DataFrame in the pickle Python file format.
Thus, to execute our algorithm from above and save the results to buyapple_out.pickle we would call run_algo.py as follows:
In [25]:
!zipline run -f ../../zipline/examples/buyapple.py --start 2016-1-1 --end 2018-1-1 -o buyapple_out.pickle
run_algo.py first outputs the algorithm contents. It then uses historical price and volume data of Apple from the quantopian-quandl bundle in the desired time range, calls the initialize() function, and then streams the historical stock price day-by-day through handle_data(). After each call to handle_data() we instruct zipline to order 10 stocks of AAPL. After the call of the order() function, zipline enters the ordered stock and amount in the order book. After the handle_data() function has finished, zipline looks for any open orders and tries to fill them. If the trading volume is high enough for this stock, the order is executed after adding the commission and applying the slippage model which models the influence of your order on the stock price, so your algorithm will be charged more than just the stock price * 10. (Note, that you can also change the commission and slippage model that zipline uses, see the Quantopian docs for more information).
Note that there is also an analyze() function printed. run_algo.py will try and look for a file with the ending with _analyze.py and the same name of the algorithm (so buyapple_analyze.py) or an analyze() function directly in the script. If an analyze() function is found it will be called after the simulation has finished and passed in the performance DataFrame. (The reason for allowing specification of an analyze() function in a separate file is that this way buyapple.py remains a valid Quantopian algorithm that you can copy&paste to the platform).
Lets take a quick look at the performance DataFrame. For this, we use pandas from inside the IPython Notebook and print the first ten rows. Note that zipline makes heavy usage of pandas, especially for data input and outputting so it's worth spending some time to learn it.
In [26]:
import pandas as pd
perf = pd.read_pickle('buyapple_out.pickle') # read in perf DataFrame
perf.head()
Out[26]:
As you can see, there is a row for each trading day, starting on the first business day of 2016. In the columns you can find various information about the state of your algorithm. The very first column AAPL was placed there by the record() function mentioned earlier and allows us to plot the price of apple. For example, we could easily examine now how our portfolio value changed over time compared to the AAPL stock price.
In [27]:
%pylab inline
figsize(12, 12)
import matplotlib.pyplot as plt
ax1 = plt.subplot(211)
perf.portfolio_value.plot(ax=ax1)
ax1.set_ylabel('Portfolio Value')
ax2 = plt.subplot(212, sharex=ax1)
perf.AAPL.plot(ax=ax2)
ax2.set_ylabel('AAPL Stock Price')
Out[27]:
As you can see, our algorithm performance as assessed by the portfolio_value closely matches that of the AAPL stock price. This is not surprising as our algorithm only bought AAPL every chance it got.
The IPython Notebook is a very powerful browser-based interface to a Python interpreter (this tutorial was written in it). As it is already the de-facto interface for most quantitative researchers zipline provides an easy way to run your algorithm inside the Notebook without requiring you to use the CLI.
To use it you have to write your algorithm in a cell and let zipline know that it is supposed to run this algorithm. This is done via the %%zipline IPython magic command that is available after you run %load_ext zipline in a separate cell. This magic takes the same arguments as the command line interface described above.
In [28]:
%load_ext zipline
In [29]:
%%zipline --start 2016-1-1 --end 2018-1-1 -o perf_ipython.pickle
from zipline.api import symbol, order, record
def initialize(context):
context.asset = symbol('AAPL')
def handle_data(context, data):
order(context.asset, 10)
record(AAPL=data.current(context.asset, 'price'))
Out[29]:
Note that we did not have to specify an input file as above since the magic will use the contents of the cell and look for your algorithm functions there.
In [30]:
pd.read_pickle('perf_ipython.pickle').head()
Out[30]:
data.history()The Dual Moving Average (DMA) is a classic momentum strategy. It's probably not used by any serious trader anymore but is still very instructive. The basic idea is that we compute two rolling or moving averages (mavg) -- one with a longer window that is supposed to capture long-term trends and one shorter window that is supposed to capture short-term trends. Once the short-mavg crosses the long-mavg from below we assume that the stock price has upwards momentum and long the stock. If the short-mavg crosses from above we exit the positions as we assume the stock to go down further.
As we need to have access to previous prices to implement this strategy we need a new concept: History
data.history() is a convenience function that keeps a rolling window of data for you. The first argument is the asset or iterable of assets you're using, the second argument is the field you're looking for i.e. price, open, volume, the third argument is the number of bars, and the fourth argument is your frequency (either '1d' for '1m' but note that you need to have minute-level data for using 1m).
For a more detailed description of data.history()'s features, see the Quantopian docs. Let's look at the strategy which should make this clear:
In [31]:
%pylab inline
figsize(12, 12)
In [32]:
%%zipline --start 2014-1-1 --end 2018-1-1 -o perf_dma.pickle
from zipline.api import order_target, record, symbol
import numpy as np
import matplotlib.pyplot as plt
def initialize(context):
context.i = 0
context.asset = symbol('AAPL')
def handle_data(context, data):
# Skip first 300 days to get full windows
context.i += 1
if context.i < 300:
return
# Compute averages
# data.history() has to be called with the same params
# from above and returns a pandas dataframe.
short_mavg = data.history(context.asset, 'price', bar_count=100, frequency="1d").mean()
long_mavg = data.history(context.asset, 'price', bar_count=300, 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(context.asset, 100)
elif short_mavg < long_mavg:
order_target(context.asset, 0)
# Save values for later inspection
record(AAPL=data.current(context.asset, 'price'),
short_mavg=short_mavg,
long_mavg=long_mavg)
def analyze(context, perf):
ax1 = plt.subplot(211)
perf.portfolio_value.plot(ax=ax1)
ax1.set_ylabel('portfolio value in $')
ax1.set_xlabel('time in years')
ax2 = plt.subplot(212, sharex=ax1)
perf['AAPL'].plot(ax=ax2)
perf[['short_mavg', 'long_mavg']].plot(ax=ax2)
perf_trans = perf.ix[[t != [] for t in perf.transactions]]
buys = perf_trans.ix[[t[0]['amount'] > 0 for t in perf_trans.transactions]]
sells = perf_trans.ix[[t[0]['amount'] < 0 for t in perf_trans.transactions]]
ax2.plot(buys.index, perf.short_mavg.ix[buys.index], '^', markersize=10, color='m')
ax2.plot(sells.index, perf.short_mavg.ix[sells.index],'v', markersize=10, color='k')
ax2.set_ylabel('price in $')
ax2.set_xlabel('time in years')
plt.legend(loc=0)
plt.show()
Out[32]:
Here we are explicitly defining an analyze() function that gets automatically called once the backtest is done (this is not possible on Quantopian currently).
Although it might not be directly apparent, the power of history (pun intended) can not be under-estimated as most algorithms make use of prior market developments in one form or another. You could easily devise a strategy that trains a classifier with scikit-learn which tries to predict future market movements based on past prices (note, that most of the scikit-learn functions require numpy.ndarrays rather than pandas.DataFrames, so you can simply pass the underlying ndarray of a DataFrame via .values).
We also used the order_target() function above. This and other functions like it can make order management and portfolio rebalancing much easier. See the Quantopian documentation on order functions fore more details.
We hope that this tutorial gave you a little insight into the architecture, API, and features of zipline. For next steps, check out some of the examples.
Feel free to ask questions on our mailing list, report problems on our GitHub issue tracker, get involved, and checkout Quantopian.