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DOC: Add development guide .rst

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swacad
2017-05-22 17:19:40 -07:00
committed by Freddie Vargus
parent 1e4a094fec
commit 246eed215f
5 changed files with 238 additions and 41 deletions
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docs/source/beginner-tutorial.rst
+15 -15
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@@ -1,4 +1,4 @@
Zipline beginner tutorial
Zipline Beginner Tutorial
-------------------------
Basics
@@ -624,7 +624,7 @@ 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
``history()`` is a convenience function that keeps a rolling window of
``data.history()`` is a convenience function that keeps a rolling window of
data for you. The first argument is the number of bars you want to
collect, the second argument is the unit (either ``'1d'`` for ``'1m'``
but note that you need to have minute-level data for using ``1m``). For
@@ -634,14 +634,14 @@ Let's look at the strategy which should make this clear:
.. code-block:: python
%%zipline --start 2000-1-1 --end 2014-1-1 -o perf_dma
%%zipline --start 2000-1-1 --end 2012-1-1 -o dma.pickle
from zipline.api import order_target, record, symbol, history
import numpy as np
from zipline.api import order_target, record, symbol
def initialize(context):
context.i = 0
context.asset = symbol('AAPL')
def handle_data(context, data):
@@ -651,23 +651,23 @@ Let's look at the strategy which should make this clear:
return
# Compute averages
# history() has to be called with the same params
# data.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()
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[0] > long_mavg[0]:
if short_mavg > long_mavg:
# order_target orders as many shares as needed to
# achieve the desired number of shares.
order_target(symbol('AAPL'), 100)
elif short_mavg[0] < long_mavg[0]:
order_target(symbol('AAPL'), 0)
order_target(context.asset, 100)
elif short_mavg < long_mavg:
order_target(context.asset, 0)
# Save values for later inspection
record(AAPL=data[symbol('AAPL')].price,
short_mavg=short_mavg[0],
long_mavg=long_mavg[0])
record(AAPL=data.current(context.asset, 'price'),
short_mavg=short_mavg,
long_mavg=long_mavg)
def analyze(context, perf):