Eddie Hebert
36319122cc
Attack the startup bottleneck of creating the asset finders caches for a large universe, which was between 1-2 seconds on development and production machines. Instead, allow the AssetFinder to be passed a sqlite3 file that has already been populated and then hydrate asset objects only when an equity is referenced for the first time. To create aforementioned sqlite3, create an AssetFinder with an db_path and `create_table` set to True. If `create_table` is set to False, the prepopulated data in the sqlite file found at db_path will be used. Default behavior is to use an in memory database. Behavior that changes: - Fuzzy lookup now only works on one character, that character needs to be specified at write/metadata consumption time, since the fuzzy lookup key is created by dropping the character from each symbol. - Overwriting partially written metadata is no longer supported. i.e. some unit tests allowed for inserting just the identifier, and then later updating the symbol, end_date, etc. Instead of building an upsert behavior at this time, this patch changes the unit tests so that the data for each asset is only inserted once. Other notes: - populate_cache is now removed, since there is no longer a two step process of inserting metadata and then realizing that metadata into assets. _spawn_asset is rolled into insert_metadata, so that a call to insert_metadata both converts the metadata and makes it available in the data store.
Zipline
Zipline is a Pythonic algorithmic trading library. The system is fundamentally event-driven and a close approximation of how live-trading systems operate.
Zipline is currently used in production as the backtesting engine powering Quantopian (https://www.quantopian.com) -- a free, community-centered platform that allows development and real-time backtesting of trading algorithms in the web browser.
Want to contribute? See our open requests and our general guidelines below.
Features
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Ease of use: Zipline tries to get out of your way so that you can focus on algorithm development. See below for a code example.
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Zipline comes "batteries included" as many common statistics like moving average and linear regression can be readily accessed from within a user-written algorithm.
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Input of historical data and output of performance statistics is based on Pandas DataFrames to integrate nicely into the existing Python eco-system.
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Statistic and machine learning libraries like matplotlib, scipy, statsmodels, and sklearn support development, analysis and visualization of state-of-the-art trading systems.
Installation
The easiest way to install Zipline is via conda which comes as part of Anaconda or can be installed via pip install conda.
Once set up, you can install Zipline from our Quantopian channel:
conda install -c Quantopian zipline
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
PIP
Alternatively you can install Zipline via the more traditional pip
command. Since zipline is pure-python code it should be very easy to
install and set up:
pip install numpy # Pre-install numpy to handle dependency chain quirk
pip install zipline
If there are problems installing the dependencies or zipline we recommend installing these packages via some other means. For Windows, the Enthought Python Distribution includes most of the necessary dependencies. On OSX, the Scipy Superpack works very well.
Dependencies
- Python (2.7 or 3.3)
- numpy (>= 1.6.0)
- pandas (>= 0.9.0)
- pytz
- Logbook
- requests
- python-dateutil (>= 2.1)
- ta-lib
Quickstart
See our getting started tutorial.
The following code implements a simple dual moving average algorithm.
from zipline.api import order_target, record, symbol, history, add_history
def initialize(context):
# Register 2 histories that track daily prices,
# one with a 100 window and one with a 300 day window
add_history(100, '1d', 'price')
add_history(300, '1d', 'price')
context.i = 0
def handle_data(context, data):
# Skip first 300 days to get full windows
context.i += 1
if context.i < 300:
return
# Compute averages
# history() has to be called with the same params
# from above and returns a pandas dataframe.
short_mavg = history(100, '1d', 'price').mean()
long_mavg = history(300, '1d', 'price').mean()
sym = symbol('AAPL')
# Trading logic
if short_mavg[sym] > long_mavg[sym]:
# order_target orders as many shares as needed to
# achieve the desired number of shares.
order_target(sym, 100)
elif short_mavg[sym] < long_mavg[sym]:
order_target(sym, 0)
# Save values for later inspection
record(AAPL=data[sym].price,
short_mavg=short_mavg[sym],
long_mavg=long_mavg[sym])
You can then run this algorithm using the Zipline CLI. From the command line, run:
python run_algo.py -f dual_moving_average.py --symbols AAPL --start 2011-1-1 --end 2012-1-1 -o dma.pickle
This will download the AAPL price data from Yahoo! Finance in the specified time range and stream it through the algorithm and save the resulting performance dataframe to dma.pickle which you can then load and analyze from within python.
You can find other examples in the zipline/examples directory.
Contributions
If you would like to contribute, please see our Contribution Requests: https://github.com/quantopian/zipline/wiki/Contribution-Requests