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Applies PEP-8 and pyflakes style to tests and zipline.

Browse Source 
Mostly whitespace, line width and other spacing changes.
Also, removes use of deprecated has_key in favor of `in`

Going forward new patches should pass running `flake8` before
submission.
This commit is contained in:
Eddie Hebert
2012-10-05 12:14:09 -04:00
parent 0cd8931a5b
commit 77af1ca632
36 changed files with 1461 additions and 868 deletions
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zipline/optimize/example.py
+18 -8
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@@ -6,10 +6,10 @@ import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import cProfile
from zipline.gens.mavg import MovingAverage
from zipline.algorithm import TradingAlgorithm
class DMA(TradingAlgorithm):
"""Dual Moving Average algorithm.
"""
@@ -34,10 +34,14 @@ class DMA(TradingAlgorithm):
for sid in self.sids:
# access transforms via their user-defined tag
if (data[sid].short_mavg['price'] > data[sid].long_mavg['price']) and not self.invested[sid]:
if (data[sid].short_mavg['price'] >
data[sid].long_mavg['price']) \
and not self.invested[sid]:
self.order(sid, self.amount)
self.invested[sid] = True
elif (data[sid].short_mavg['price'] < data[sid].long_mavg['price']) and self.invested[sid]:
elif (data[sid].short_mavg['price'] <
data[sid].long_mavg['price']) \
and self.invested[sid]:
self.order(sid, -self.amount)
self.invested[sid] = False
@@ -48,7 +52,7 @@ def load_close_px(indexes=None, stocks=None):
from collections import OrderedDict
if indexes is None:
indexes = {'SPX' : '^GSPC'}
indexes = {'SPX': '^GSPC'}
if stocks is None:
stocks = ['AAPL', 'GE', 'IBM', 'MSFT', 'XOM', 'AA', 'JNJ', 'PEP']
@@ -78,12 +82,16 @@ def load_close_px(indexes=None, stocks=None):
def run((short_window, long_window)):
data = pd.load('close_px.dat')
#data = load_close_px()
myalgo = DMA([0, 1], amount=100, short_window=short_window, long_window=long_window)
myalgo = DMA([0, 1],
amount=100,
short_window=short_window,
long_window=long_window)
stats = myalgo.run(data)
stats['sw'] = short_window
stats['lw'] = long_window
return stats
def explore_params():
sws, lws = np.mgrid[10:20:5, 10:20:5]
@@ -97,7 +105,6 @@ def explore_params():
plt.savefig('DMA_contour.png')
plt.show()
#stats = run((10, 50))
def get_opt_holdings_qp(univ_rets, track_rets):
from cvxopt import matrix
@@ -115,6 +122,7 @@ def get_opt_holdings_qp(univ_rets, track_rets):
raise Exception('optimum not reached by QP')
return pd.Series(np.array(result['x']).ravel(), index=univ_rets.columns)
def opt_portfolio(cov, budget, min_return):
from cvxopt import matrix
from cvxopt.solvers import qp
@@ -122,7 +130,7 @@ def opt_portfolio(cov, budget, min_return):
cov = matrix(2 * cov)
q = matrix(np.zeros(n))
h = matrix(budget) # G*x < h
h = matrix(budget) # G*x < h
# coneqp
result = qp(cov, q, h=h)
if result['status'] != 'optimal':
@@ -130,10 +138,12 @@ def opt_portfolio(cov, budget, min_return):
return pd.Series(np.array(result['x']).ravel())
def calc_te(weights, univ_rets, track_rets):
port_rets = (univ_rets * weights).sum(1)
return (port_rets - track_rets).std()
def plot_returns(port_returns, bmk_returns):
plt.figure()
cum_port = ((1 + port_returns).cumprod() - 1)
@@ -145,4 +155,4 @@ def plot_returns(port_returns, bmk_returns):
plt.title('Portfolio performance')
plt.legend(loc='best')
print run((10, 20))
print run((10, 20))