From 5c57be859d400454435ff2e478da6731756e24fa Mon Sep 17 00:00:00 2001 From: llllllllll Date: Fri, 6 Nov 2015 15:04:13 -0500 Subject: [PATCH] DOC: final cleanup --- README.rst | 99 +++++++++++++++------------------- docs/source/whatsnew/0.8.0.txt | 7 +-- 2 files changed, 45 insertions(+), 61 deletions(-) diff --git a/README.rst b/README.rst index e1fe9c00..d2e15993 100644 --- a/README.rst +++ b/README.rst @@ -8,14 +8,13 @@ Zipline |Coverage Status| |Code quality| -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 a Pythonic algorithmic trading library. It is an event-driven +system that supports both backtesting and live-trading. -Zipline is currently used in production as the backtesting engine -powering `Quantopian Inc. `__ -- a free, -community-centered platform that allows development and real-time -backtesting of trading algorithms in the web browser. +Zipline is currently used in production as the backtesting and live-trading +engine powering `Quantopian `__ -- a free, +community-centered, hosted platform for building and executing trading +strategies. `Join our community! `__ @@ -29,29 +28,38 @@ below. Features ======== -- 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. - -- Zipline comes "batteries included" as many common statistics like - moving average and linear regression can be readily accessed from - within a user-written algorithm. - -- Input of historical data and output of performance statistics is - based on Pandas DataFrames to integrate nicely into the existing - Python eco-system. - -- Statistic and machine learning libraries like matplotlib, scipy, - statsmodels, and sklearn support development, analysis and - visualization of state-of-the-art trading systems. +- 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. +- Zipline comes "batteries included" as many common statistics like + moving average and linear regression can be readily accessed from + within a user-written algorithm. +- Input of historical data and output of performance statistics are + based on Pandas DataFrames to integrate nicely into the existing + PyData eco-system. +- 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 +pip +--- + +You can install Zipline via the ``pip`` command: +:: + + $ pip install zipline + + +conda +----- + +Another 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: +Once set up, you can install Zipline from our ``Quantopian`` channel: :: @@ -59,44 +67,18 @@ Once set up, you can install Zipline from our Quantopian channel: Currently supported platforms include: -- Windows 32-bit (can be 64-bit Windows but has to be 32-bit Anaconda) - +- GNU/Linux 64-bit - OSX 64-bit -- Linux 64-bit +.. note:: -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. + Windows may work; however, it is currently untested. Dependencies ------------ -- Python (2.7 or 3.3) -- numpy (>= 1.6.0) -- pandas (>= 0.9.0) -- pytz -- Logbook -- requests -- `python-dateutil `__ - (>= 2.1) -- ta-lib +See our `requirements file +`__ Quickstart ========== @@ -108,7 +90,13 @@ The following code implements a simple dual moving average algorithm. .. code:: python - from zipline.api import order_target, record, symbol, history, add_history + from zipline.api import ( + add_history, + history, + order_target, + record, + symbol, + ) def initialize(context): @@ -116,7 +104,6 @@ The following code implements a simple dual moving average algorithm. # 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 diff --git a/docs/source/whatsnew/0.8.0.txt b/docs/source/whatsnew/0.8.0.txt index 7e2cc8a6..8e2a647b 100644 --- a/docs/source/whatsnew/0.8.0.txt +++ b/docs/source/whatsnew/0.8.0.txt @@ -2,7 +2,7 @@ Release 0.8.0 ------------- :Release: 0.8.0 -:Date: TBD +:Date: November 6, 2015 Highlights ~~~~~~~~~~ @@ -148,7 +148,7 @@ Experimental Features Experimental features are subject to change. * Adds new Pipeline API. The pipeline API is a high-level declarative API for - representing trailing window computaions on large datasets (:issue:`630`). + representing trailing window computations on large datasets (:issue:`630`). * Adds support for futures trading (:issue:`637`). * Adds Pipeline loader for blaze expressions. This allows users to pull data from any format blaze understands and use it in the Pipeline @@ -187,6 +187,3 @@ Documentation ~~~~~~~~~~~~~ * Switched to sphinx for the documentation (:issue:`816`). - -Contributors -~~~~~~~~~~~~