def MakeDataToCsv():
'''
生成沪深300的股票数据到指定的文件夹
:return:
'''
stockList = mt_read_index_codes('000300.SH')
stockList = list(map(lambda x: x.split(".")[0], stockList))
stockList = list(map(mt_add_suffix_name, stockList))
count = 0
resultTemp = pd.DataFrame()
for stock in stockList:
df = QAStock2btData(stock, '2010-01-01', '2020-10-10')
df = IndicateBias(df, 20, 60, 120)
df = IndicatePctChange(df, 1)
df = IndicatePctChange(df, 3)
df = IndicatePctChange(df, 5)
df = IndicatePctChange(df, 10)
df = IndicatePctChange(df, 20)
df = IndicatePctChange(df, 60)
df = IndicatePctChange(df, 120)
df = IndicatePctChange(df, 1, True)
df = IndicatePctChange(df, 3, True)
df = IndicatePctChange(df, 5, True)
df = IndicatePctChange(df, 10, True)
df = IndicatePctChange(df, 20, True)
df = IndicateBias(df, 20, 60, 120)
df.to_csv('./selectStock/' + stock + '.csv')
count += 1
print("current code is :%s" % stock)
resultTemp = pd.concat([resultTemp, df])
if count > 5000:
break
def runstrat():
args = parse_args()
cerebro = bt.Cerebro(cheat_on_open=True)
cerebro.broker.setcommission(commission=0.0012, stocklike=True)
# cerebro.broker.set_coo(True)
# Data feed kwargs
# '15min', '30min', '60min',
# dataframe = read_dataframe(args.data, args.years, ['15min', '60min', 'd'])
# dataframe = readFromDb(args.data, args.fq, args.years, ["5min", '15min', '60min', 'd'])
# dataframe = readFromDb(args.data, args.fq, args.years, ['d'])
# for i in range(len(dataframe)):
# cerebro.adddata(bt.feeds.PandasData(dataname=dataframe[i]))
# for i in range(len(dataframe)):
# temp_df = macd_extend_data(dataframe[i])
# cerebro.adddata(pandas_divergence(dataname=temp_df,
# divergence_top=temp_df.columns.to_list().index('divergence_top'),
# divergence_bottom=temp_df.columns.to_list().index('divergence_bottom')))
# dataframe = QAIndex2btData("159934", '2014-01-01', '2020-10-13')
dataframe = QAStock2btData("000002", '2008-01-01', '2021-04-30')
# dataframe = read_dataframe('000651.csv', '2014-2020', ['d'])[0]
cerebro.adddata(bt.feeds.PandasData(dataname=dataframe))
cerebro.addstrategy(lnkyzhangSt)
# cerebro.addstrategy(MacdDivergenceSt)
# cerebro.addstrategy(LeiGoneSt)
cerebro.addanalyzer(bt.analyzers.DrawDown, _name='DrawDown')
cerebro.addanalyzer(bt.analyzers.AnnualReturn, _name='AnnualReturn')
cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio')
strat = cerebro.run(stdstats=True, runonce=False)
drawDown = strat[0].analyzers.DrawDown.get_analysis()
print("max drawdown: %f", drawDown.max.drawdown)
annualReturn = strat[0].analyzers.AnnualReturn.get_analysis()
print("annualReturn: %s", annualReturn)
pyfoliozer = strat[0].analyzers.getbyname('pyfolio')
returns, positions, transactions, gross_lev = pyfoliozer.get_pf_items()
transactions.to_csv("transtion.csv")
if args.plot:
# cerebro.plot(style='line')
cerebro.plot(style='candle', iplot=False)
def MakeDataToCsv():
'''
生成沪深300的股票数据到指定的文件夹
:return:
'''
stockList = mt_read_index_codes('000300.SH')
stockList = list(map(lambda x: x.split(".")[0], stockList))
stockList = list(map(mt_add_suffix_name, stockList))
count = 0
resultTemp = pd.DataFrame()
for stock in stockList:
df = QAStock2btData(stock, '2010-01-01', '2020-10-10')
df_indBias = IndicateBias(df, 20, 60, 120)
df = stopPriceStatistic(df)
df = df.merge(df_indBias[["s_bias", "m_bias", "l_bias"]],
left_on="startDate",
right_index=True)
df.to_csv('./analysisData/' + stock + '.csv')
count += 1
print("current code is :%s" % stock)
resultTemp = pd.concat([resultTemp, df])
if count > 5000:
break
# # Create a calendar
# sse = mcal.get_calendar('SSE', open_time=time(9, 30), close_time=time(15, 00))
# print(sse.tz.zone)
# print('open, close: %s, %s' % (sse.open_time, sse.close_time))
# # Show available calendars
# # print(mcal.get_calendar_names())
# early = sse.schedule(start_date='2000-01-01', end_date='2000-04-10')
#
# print(early)
#
from BackTraderTest.BackTraderFunc.makeData import QAStock2btData
from matplotlib import pyplot as plt
import sympy
dataframe = QAStock2btData("000651", '2012-01-01', '2029-01-13')
# 测试natr
# a = ta.ATR(dataframe.high, dataframe.low, dataframe.close)
# ema20 = ta.EMA(dataframe.close)
#
# a = a / ema20
upperband, middleband, lowerband = ta.BBANDS(dataframe.close,timeperiod=20)
real = (upperband - lowerband) / middleband
fig, ax = plt.subplots(figsize=(10, 7))
ax.hist(real, bins=20, rwidth=0.5)
ax.set_title("Simple Histogram")