def get_tdx_barfeed(code, start=None, dl=30):
df = tdd.get_tdx_append_now_df_api(code, start=start)
# df2 = get_tdx_append_now_df_api(code=stock_code, start=start, end=end, dl=dl).sort_index(ascending=True)
df.rename(columns={'vol': 'volume'}, inplace=True)
df = df.sort_index(ascending=True)
barfeed = dataFramefeed.Feed()
barfeed.addBarsFromDataFrame(code, df)
# print barfeed.get_dataFrame()
return barfeed
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()