def turtle_test():
# Load the yahoo feed from the CSV file
feed = yahoofeed.Feed()
feed.addBarsFromCSV("orcl", "D:/data2/600687.csv")
# Evaluate the strategy with the feed's bars.
#myStrategy = pyalg_test.SMACrossOver(feed, "orcl", 20)
myStrategy = pyalg_test.turtle(feed, "orcl",20,10)
# Attach a returns analyzers to the strategy.
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(myStrategy)
# Include the SMA in the instrument's subplot to get it displayed along with the closing prices.
#plt.getInstrumentSubplot("orcl").addDataSeries("SMA", myStrategy.getSMA())
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())
# Run the strategy.
ds = pyalg_utils.dataSet(myStrategy) #抽取交易数据集语句，若使用系统自带画图功能则不需要该项
myStrategy.run()
myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())
rs = ds.getDefault() #获取默认的交易信息，dic格式
plot(rs["cumulativeReturns"][:,0],rs["cumulativeReturns"][:,1]) #简单作图示例
# Plot the strategy.
plt.plot()
def turtle_test():
# Load the yahoo feed from the CSV file
feed = yahoofeed.Feed()
feed.addBarsFromCSV("orcl", "D:/data2/600223.csv")
# Evaluate the strategy with the feed's bars.
#myStrategy = pyalg_test.SMACrossOver(feed, "orcl", 20)
myStrategy = pyalg_test.turtle(feed, "orcl",20,10)
# Attach a returns analyzers to the strategy.
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(myStrategy)
# Include the SMA in the instrument's subplot to get it displayed along with the closing prices.
#plt.getInstrumentSubplot("orcl").addDataSeries("SMA", myStrategy.getSMA())
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())
# Run the strategy.
myStrategy.run()
myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())
# Plot the strategy.
plt.plot()
def turtle_test(load_type='dataFrame', dataString='pyalg'):
if load_type == 'csv':
#Load the yahoo feed from the CSV file
feed = yahoofeed.Feed()
feed.addBarsFromCSV("orcl", "D:/data2/600687.csv")
elif load_type == 'dataFrame':
#从dataFrame中加载，
import os
dataRoot = os.path.abspath(
os.path.join(os.path.dirname(__file__), os.pardir, os.pardir,
os.pardir, 'histdata', 'tushare'))
code = '600209'
filename = dataRoot + os.sep + 'day' + os.sep + ('%s.csv' % code)
dat = pd.read_csv(filename, index_col=0, encoding='gbk')
feed = dataFramefeed.Feed()
feed.addBarsFromDataFrame("orcl", dat)
elif load_type == 'sql':
#此处也是
dat = data_sql.get_h_data('600848')
feed = dataFramefeed.Feed()
feed.addBarsFromDataFrame("orcl", dat)
# Evaluate the strategy with the feed's bars.
#myStrategy = pyalg_test.SMACrossOver(feed, "orcl", 20)
myStrategy = pyalg_test.turtle(feed, "orcl", 20, 10)
# Attach a returns analyzers to the strategy.
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(myStrategy)
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries(
"Simple returns", returnsAnalyzer.getReturns())
if dataString == 'pyalg_util':
ds = pyalg_utils.dataSet(myStrategy) #抽取交易数据集语句，若使用系统自带画图功能则不需要该项
myStrategy.run()
myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())
if dataString == 'pyalg_util':
rs = ds.getDefault() #获取默认的交易信息，dic格式
plot(rs["cumulativeReturns"][:, 0],
rs["cumulativeReturns"][:, 1]) #简单作图示例
plt.plot()
def turtle_test(load_type = 'dataFrame',dataString = 'pyalg'):
if load_type =='csv':
#Load the yahoo feed from the CSV file
feed = yahoofeed.Feed()
feed.addBarsFromCSV("orcl", "D:/data2/600687.csv")
elif load_type =='dataFrame':
#从dataFrame中加载，
import os
dataRoot = os.path.abspath(os.path.join(os.path.dirname(__file__),os.pardir,os.pardir,os.pardir,'histdata','tushare'))
code = '600209'
filename = dataRoot+os.sep+'day'+os.sep+('%s.csv'%code)
dat = pd.read_csv(filename,index_col=0,encoding='gbk')
feed = dataFramefeed.Feed()
feed.addBarsFromDataFrame("orcl", dat)
elif load_type == 'sql':
#此处也是
dat = data_sql.get_h_data('600848')
feed = dataFramefeed.Feed()
feed.addBarsFromDataFrame("orcl", dat)
# Evaluate the strategy with the feed's bars.
#myStrategy = pyalg_test.SMACrossOver(feed, "orcl", 20)
myStrategy = pyalg_test.turtle(feed, "orcl",20,10)
# Attach a returns analyzers to the strategy.
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(myStrategy)
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries("Simple returns", returnsAnalyzer.getReturns())
if dataString =='pyalg_util':
ds = pyalg_utils.dataSet(myStrategy) #抽取交易数据集语句，若使用系统自带画图功能则不需要该项
myStrategy.run()
myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())
if dataString =='pyalg_util':
rs = ds.getDefault() #获取默认的交易信息，dic格式
plot(rs["cumulativeReturns"][:,0],rs["cumulativeReturns"][:,1]) #简单作图示例
plt.plot()
def turtle_test(load_type='csv', dataString='pyalg'):
if load_type == 'csv':
#Load the yahoo feed from the CSV file
feed = yahoofeed.Feed()
feed.addBarsFromCSV("orcl", "000004.csv")
elif load_type == 'dataFrame':
#从dataFrame中加载，
# dat = pd.read_csv('000004.csv',encoding='gbk',sep='\t',index_col=0)
# dat['date']=dat['date'].apply(lambda x:x.replace('/','-'))
dat = ts.get_hist_data('000838', start='2016-01-01')
feed = dataFramefeed.Feed()
feed.addBarsFromDataFrame("orcl", dat)
elif load_type == 'sql':
#此处也是
dat = data_sql.get_h_data('600848')
feed = dataFramefeed.Feed()
feed.addBarsFromDataFrame("orcl", dat)
# Evaluate the strategy with the feed's bars.
#myStrategy = pyalg_test.SMACrossOver(feed, "orcl", 20)
pars = [20, 10]
myStrategy = pyalg_test.turtle(feed, "orcl", *pars)
# Attach a returns analyzers to the strategy.
returnsAnalyzer = returns.Returns()
myStrategy.attachAnalyzer(returnsAnalyzer)
# Attach the plotter to the strategy.
plt = plotter.StrategyPlotter(myStrategy)
# Plot the simple returns on each bar.
plt.getOrCreateSubplot("returns").addDataSeries(
"Simple returns", returnsAnalyzer.getReturns())
if dataString == 'pyalg_util':
ds = pyalg_utils.dataSet(myStrategy) #抽取交易数据集语句，若使用系统自带画图功能则不需要该项
myStrategy.run()
myStrategy.info("Final portfolio value: $%.2f" % myStrategy.getResult())
if dataString == 'pyalg_util':
rs = ds.getDefault() #获取默认的交易信息，dic格式
plot(rs["cumulativeReturns"][:, 0],
rs["cumulativeReturns"][:, 1]) #简单作图示例
plt.plot()