def setUp(self):
data_file = get_data("path", "data_file") #获取数据文件
auth_user = get_data("server", "auth_user") #获取登录用户名
auth_password = get_data("server", "auth_password") #获取登录密码
self.url = get_data("server", "url_users") #获取url
self.auth = (auth_user, auth_password)
self.data_list = excel_to_list(data_file, "Sheet1")
def test_run():
stock_symbol = 'EME'
# Read data
df = get_data(['SPY', stock_symbol], '2016-01-01', '2016-10-24')
# plot_data(df)
# Compute global statistics for each stock
# print(df.mean())
# print(df.median())
# print(df.std())
# Compute Bollinger Bands
# 1. Compute rolling mean using a 20-day window
rm_SPY = get_rolling_mean(df[stock_symbol], window=20)
# 2. Compute rolling standard deviation
rstd_SPY = get_rolling_std(df[stock_symbol], window=20)
# 3. Compute upper and lower bands
upper_band, lower_band = get_bollinger_bands(rm_SPY, rstd_SPY)
# Plot raw SPY data, rolling mean and Bollinger Bands
ax = df[stock_symbol].plot(title="Bollinger Bands", label=stock_symbol)
rm_SPY.plot(label='Rolling mean', ax=ax)
upper_band.plot(label='upper band', ax=ax)
lower_band.plot(label='lower band', ax=ax)
# Add axis labels and legend
ax.set_xlabel("Date")
ax.set_ylabel("Price")
ax.legend(loc='upper left')
plt.show()
def test_run2():
# Read data
symbols = ['SPY', 'XOM', 'GOLD']
df = get_data(symbols, '2009-01-01', '2012-12-31')
# Compute daily returns
daily_returns = compute_daily_returns(df)
# Scatterplot SPY vs XOM
daily_returns.plot(kind='scatter', x='SPY', y='XOM')
beta_XOM, alpha_XOM = np.polyfit(daily_returns['SPY'],
daily_returns['XOM'], 1)
plt.plot(daily_returns['SPY'],
beta_XOM * daily_returns['SPY'] + alpha_XOM,
'-',
color='r')
plt.show()
# Scatterplot SPY vs XOM
daily_returns.plot(kind='scatter', x='SPY', y='GOLD')
beta_XOM, alpha_XOM = np.polyfit(daily_returns['SPY'],
daily_returns['GOLD'], 1)
plt.plot(daily_returns['SPY'],
beta_XOM * daily_returns['SPY'] + alpha_XOM,
'-',
color='r')
plt.show()
def test_run():
# Read data
symbols = ['SPY', 'XOM']
df = get_data(symbols, '2009-01-01', '2012-12-31')
plot_data(df)
# Compute daily returns
daily_returns = compute_daily_returns(df)
plot_data(daily_returns, title="Daily Returns")
# Plot a histogram
for symbol in symbols:
daily_returns[symbol].hist(bins=20, label=symbol)
# Get mean and standard deviation
for symbol in symbols:
mean = daily_returns[symbol].mean()
print("{}: mean={}".format(symbol, mean))
std = daily_returns[symbol].std()
print("{}: std={}".format(symbol, std))
# Compute kurtosis
print(daily_returns.kurtosis())
# Add mean and std to graph
# plt.axvline(mean, color='w', linestyle='dashed', linewidth=2)
# plt.axvline(mean + std, color='r', linestyle='dashed', linewidth=2)
# plt.axvline(mean - std, color='r', linestyle='dashed', linewidth=2)
plt.legend(loc='upper right')
plt.show()
def send_email():
#获取收件人、发件人、密码、标题、服务器、文件路径信息
toAddr = get_data('mail', "to_addr")
fromAddr = get_data('mail', "from_addr")
password = get_data('mail', "from_password")
title = get_data('mail', "title")
smtpserver = get_data('mail', "server")
html_dir = get_data('mail', 'html_dir')
#读取HTML文件内容
f = open(html_dir, "rb")
mail_boby = f.read()
f.close()
#邮件内容格式编码
message = MIMEMultipart()
txt = MIMEText(mail_boby, 'html', 'utf-8')
message.attach(txt)
message['From'] = (u'tester<%s>' % fromAddr)
message['To'] = (u'other<%s>' % toAddr)
message['Subject'] = Header(u'自动化测试<%s>' % title, 'utf-8')
#添加HTML附件
part = MIMEApplication(open(html_dir, 'rb').read())
part.add_header('Content-Disposition',
'attachment',
filename="report.html")
message.attach(part)
try:
server = smtplib.SMTP()
server.connect(smtpserver, 25)
#server.set_debuglevel(1)
server.login(fromAddr, password)
server.sendmail(fromAddr, toAddr, message.as_string())
print("发送邮件成功")
server.quit()
except smtplib.SMTPException:
print("发送邮件失败")
poi_messages
if all_messages == 'NaN':
all_messages = 0
else:
all_messages
try:
fraction = poi_messages / all_messages
except ZeroDivisionError:
fraction = 0
return fraction
data_dict = get_data()
submit_dict = {}
for name in data_dict:
data_point = data_dict[name]
from_poi_to_this_person = data_point["from_poi_to_this_person"]
to_messages = data_point["to_messages"]
fraction_from_poi = compute_fraction(from_poi_to_this_person, to_messages)
print(fraction_from_poi)
data_point["fraction_from_poi"] = fraction_from_poi
from_this_person_to_poi = data_point["from_this_person_to_poi"]
from_messages = data_point["from_messages"]
fraction_to_poi = compute_fraction(from_this_person_to_poi, from_messages)
print(fraction_to_poi)
#！/usr/bin/env python
#-*- coding:utf-8 -*-
import unittest
import HTMLTestRunner
from lib.get_data import get_data
import os
from lib.send_email import send_email
from tester.user.test_add import *
from tester.user.test_add_group import *
if __name__ == '__main__':
# suite = unittest.TestSuite()
# suite.addTests(unittest.TestLoader().loadTestsFromName('tester.user.test_add.TestAdd'))
# suite.addTests(unittest.TestLoader().loadTestsFromName('tester.user.test_add_group.TestAddGroup'))
base_path = os.path.dirname(__file__)
testdir = os.path.join(base_path + "\\tester\\user\\")
discover = unittest.defaultTestLoader.discover(testdir, "test*.py")
#生成测试报告
report_path = get_data("path", "report_file")
fp = open(report_path, "wb")
runner = HTMLTestRunner.HTMLTestRunner(stream=fp,
title=u'Api Test',
description=u'用例执行情况',
tester=u'fishyu')
runner.run(discover)
#runn.run(suite)
fp.close()
send_email() #将结果用邮件发送
return standard_deviations
def compute_sharpe_ratios(df, trading_days=252, daily_risk_free_rate=0):
average_daily_returns = compute_average_daily_returns(df)
standard_deviations = compute_standard_deviations_of_daily_returns(df)
sharpe_ratios = {}
for symbol in average_daily_returns.keys():
sharpe_ratios[symbol] = math.sqrt(trading_days) * ((average_daily_returns[symbol] - daily_risk_free_rate) / standard_deviations[symbol])
return sharpe_ratios
if __name__ == '__main__':
symbols = ['SPY', 'GOOG', 'AAPL', 'XOM']
start_date = '2015-01-01'
end_date = '2015-12-31'
df = get_data(symbols, start_date, end_date)
print("DAILY RETURNS")
print(compute_daily_returns(df))
print("CUMULATIVE RETURNS")
print(compute_cumulative_returns(df))
print("AVERAGE DAILY RETURNS")
print(compute_average_daily_returns(df))
print("STANDARD DEVIATIONS OF DAILY RETURNS")
print(compute_standard_deviations_of_daily_returns(df))
print("SHARPE RATIOS")
print(compute_sharpe_ratios(df))