# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import pandas as pd
from zipline import TradingAlgorithm
from zipline.api import order, sid, get_datetime
from zipline.data.loader import load_data
from zipline.api import order_target, record, symbol, history, add_history, symbol, set_commission, order_percent, set_long_only, get_open_orders
from zipline.finance.commission import OrderCost
from pylab import *
mpl.rcParams['font.sans-serif'] = ['SimHei']
mpl.rcParams['axes.unicode_minus'] = False
# loading the data
input_data = load_data(stockList=['002057'],
start="2015-11-04",
end="2016-01-16")
def analyze(context=None, results=None):
import matplotlib.pyplot as plt
# Plot the portfolio and asset data.
ax1 = plt.subplot(211)
results.algorithm_period_return.plot(ax=ax1, color='blue', legend=u'策略收益')
ax1.set_ylabel(u'收益')
results.benchmark_period_return.plot(ax=ax1, color='red', legend=u'基准收益')
# Show the plot.
plt.gcf().set_size_inches(18, 8)
plt.show()
from zipline.api import order, sid,get_datetime
from zipline.data.loader import load_data
from zipline.api import order_target, record, \
symbol, history, add_history,symbol,set_commission,order_percent,set_long_only,get_open_orders,order_value,order_target
from zipline.finance.commission import OrderCost
from pylab import *
mpl.rcParams['font.sans-serif'] = ['SimHei']
mpl.rcParams['axes.unicode_minus'] = False
import pytz
from datetime import datetime, timedelta
input_data = load_data(
stockList=['000001'],
start="2014-11-04",
end="2016-01-16"
)
# 初始化函数，设定要操作的股票、基准等等
def initialize(context):
# 定义一个全局变量, 保存要操作的股票
# 000001(股票:平安银行)
context.security = '000001'
# 每个单位时间(如果按天回测,则每天调用一次,如果按分钟,则每分钟调用一次)调用一次
def handle_data(context, data):
# 获取股票的收盘价
close_data = history(12,'1d','close')
# 取得过去五天的平均价格
# Plot the portfolio and asset data.
ax1 = plt.subplot(211)
results.algorithm_period_return.plot(ax=ax1,color='blue',legend=u'策略收益')
ax1.set_ylabel(u'收益')
results.benchmark_period_return.plot(ax=ax1,color='red',legend=u'基准收益')
# Show the plot.
plt.gcf().set_size_inches(18, 8)
plt.show()
# loading the data
input_data = load_data(
stockList= ['000001','000002','000004','000005'],
start="2013-11-01",
end="2016-01-16"
)
def initialize(context):
# 初始化此策略
# 设置我们要操作的股票池
context.stocks = ['000001','000002','000004','000005']
# 每个单位时间(如果按天回测,则每天调用一次,如果按分钟,则每分钟调用一次)调用一次
def handle_data(context, data):
# context.i+=1
moment = Moment(app)
app.config['MONGO_HOST'] = '127.0.0.1'
app.config['MONGO_PORT'] = 27017
mongo = PyMongo(app)
client = pymongo.MongoClient('127.0.0.1',27017)
oriprice=client.stockoriginalprice #股票原始数据
stocks=oriprice.collection_names()
stocklist=[]
# for s in stocks:
# stocklist.append(s[2:8])
# client.close()
input_data = load_data(stockList=['000919'],start="1990-01-01",end="2016-01-16")
print input_data
def is_valid_date(str):
'''判断是否是一个有效的日期字符串'''
try:
time.strptime(str, "%Y-%m-%d")
return True
except:
return False
def toJson(data):
return json.dumps(data, default=json_util.default)
# @app.errorhandler(404)
# def page_not_found(e):