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DOC: Update README on conda installation, CLI and new example algo.
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twiecki
@@ -1,18 +1,17 @@
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Zipline
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=======
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Zipline is a Pythonic algorithmic trading library.
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The system is fundamentally event-driven and a close
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approximation of how live-trading systems operate.
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Currently, backtesting is well supported, but the intent is
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to develop the library for both paper and live trading,
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so that the same logic used for backtesting can be applied
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to the market.
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Zipline is a Pythonic algorithmic trading library. The system is
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fundamentally event-driven and a close approximation of how
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live-trading systems operate. Currently, backtesting is well
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supported, but the intent is to develop the library for both paper and
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live trading, so that the same logic used for backtesting can be
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applied to the market.
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Zipline is currently used in production as the backtesting engine
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powering Quantopian (https://www.quantopian.com) -- a free, community-centered
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platform that allows development and real-time backtesting of trading
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algorithms in the web browser.
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powering Quantopian (https://www.quantopian.com) -- a free,
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community-centered platform that allows development and real-time
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backtesting of trading algorithms in the web browser.
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Want to contribute? See our [open requests](https://github.com/quantopian/zipline/wiki/Contribution-Requests)
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and our [general guidelines](https://github.com/quantopian/zipline#contributions) below.
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@@ -45,8 +44,25 @@ visualization of state-of-the-art trading systems.
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Installation
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============
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Since zipline is pure-python code it should be very easy to install
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and set up with pip:
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The easiest way to install Zipline is via `conda` which comes as part of [Anaconda](http://continuum.io/downloads) or can be installed via `pip install conda`.
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Once set up, you can install Zipline from our Quantopian channel:
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```
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conda install -c Quantopian zipline
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```
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Currently supported platforms include:
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* Windows 32-bit (can be 64-bit Windows but has to be 32-bit Anaconda)
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* OSX 64-bit
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* Linux 64-bit
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PIP
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---
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Alternatively you can install Zipline via the more traditional `pip`
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command. Since zipline is pure-python code it should be very easy to
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install and set up:
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```
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pip install numpy # Pre-install numpy to handle dependency chain quirk
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@@ -72,108 +88,58 @@ Dependencies
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* [python-dateutil](https://pypi.python.org/pypi/python-dateutil) (>= 2.1)
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Conda
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-----
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We provide experimental support for conda packages. Thus if you installed [Anaconda](http://continuum.io/downloads)
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you can try:
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```
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conda install -c Quantopian zipline
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```
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Currently this only works for linux 64 bit. If you want to help extend this,
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have a look at the `conda` subdirectory.
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Quickstart
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==========
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The following code implements a simple dual moving average algorithm
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and tests it on data extracted from yahoo finance.
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The following code implements a simple dual moving average algorithm.
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```python
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from zipline import TradingAlgorithm
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from zipline.transforms import MovingAverage
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from zipline.utils.factory import load_from_yahoo
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from zipline.api import order_target, record, symbol
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from collections import deque as moving_window
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import numpy as np
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from datetime import datetime
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import pytz
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import matplotlib.pyplot as plt
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class DualMovingAverage(TradingAlgorithm):
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"""Dual Moving Average Crossover algorithm.
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def initialize(context):
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# Add 2 windows, one with a long window, one
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# with a short window.
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# Note that this is bound to change soon and will be easier.
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context.short_window = moving_window(maxlen=100)
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context.long_window = moving_window(maxlen=300)
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This algorithm buys apple once its short moving average crosses
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its long moving average (indicating upwards momentum) and sells
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its shares once the averages cross again (indicating downwards
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momentum).
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"""
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def initialize(self, short_window=100, long_window=400):
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# Add 2 mavg transforms, one with a long window, one
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# with a short window.
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self.add_transform(MovingAverage, 'short_mavg', ['price'],
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window_length=short_window)
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def handle_data(context, data):
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# Save price to window
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context.short_window.append(data[symbol('AAPL')].price)
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context.long_window.append(data[symbol('AAPL')].price)
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self.add_transform(MovingAverage, 'long_mavg', ['price'],
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window_length=long_window)
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# Compute averages
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short_mavg = np.mean(context.short_window)
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long_mavg = np.mean(context.long_window)
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# To keep track of whether we invested in the stock or not
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self.invested = False
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# Trading logic
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if short_mavg > long_mavg:
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order_target(symbol('AAPL'), 100)
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elif short_mavg < long_mavg:
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order_target(symbol('AAPL'), 0)
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def handle_data(self, data):
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short_mavg = data['AAPL'].short_mavg['price']
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long_mavg = data['AAPL'].long_mavg['price']
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buy = False
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sell = False
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# Has short mavg crossed long mavg?
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if short_mavg > long_mavg and not self.invested:
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self.order('AAPL', 100)
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self.invested = True
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buy = True
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elif short_mavg < long_mavg and self.invested:
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self.order('AAPL', -100)
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self.invested = False
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sell = True
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# Record state variables. A column for each
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# variable will be added to the performance
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# DataFrame returned by .run()
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self.record(short_mavg=short_mavg,
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long_mavg=long_mavg,
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buy=buy,
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sell=sell)
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# Load data
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start = datetime(1990, 1, 1, 0, 0, 0, 0, pytz.utc)
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end = datetime(2002, 1, 1, 0, 0, 0, 0, pytz.utc)
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data = load_from_yahoo(stocks=['AAPL'], indexes={}, start=start,
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end=end, adjusted=False)
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# Run algorithm
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dma = DualMovingAverage()
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perf = dma.run(data)
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# Plot results
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fig = plt.figure()
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ax1 = fig.add_subplot(211, ylabel='Price in $')
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data['AAPL'].plot(ax=ax1, color='r', lw=2.)
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perf[['short_mavg', 'long_mavg']].plot(ax=ax1, lw=2.)
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ax1.plot(perf.ix[perf.buy].index, perf.short_mavg[perf.buy],
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'^', markersize=10, color='m')
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ax1.plot(perf.ix[perf.sell].index, perf.short_mavg[perf.sell],
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'v', markersize=10, color='k')
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ax2 = fig.add_subplot(212, ylabel='Portfolio value in $')
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perf.portfolio_value.plot(ax=ax2, lw=2.)
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ax2.plot(perf.ix[perf.buy].index, perf.portfolio_value[perf.buy],
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'^', markersize=10, color='m')
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ax2.plot(perf.ix[perf.sell].index, perf.portfolio_value[perf.sell],
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'v', markersize=10, color='k')
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# Save values for later inspection
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record(AAPL=data[symbol('AAPL')].price,
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short_mavg=short_mavg,
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long_mavg=long_mavg)
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```
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You can then run this algorithm using the Zipline CLI. From the
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command line, run:
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```bash
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python run_algo.py -f dual_moving_avg.py --symbols AAPL --start 2011-1-1 --end 2012-1-1 -o dma.pickle
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```
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This will download the AAPL price data from Yahoo! Finance in the
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specified time range and stream it through the algorithm and save the
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resulting performance dataframe to dma.pickle which you can then load
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and analyze from within python.
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You can find other examples in the zipline/examples directory.
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Contributions
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