def stock_in_divergece(code, tradeCycle='W', startdate='', enddate=''):
dfdata = ts.get_k_data(code,
ktype=tradeCycle,
start=startdate,
end=enddate)
if (len(dfdata) < 120): # 剔除交易时间少于120天的个股
return False
dates = dfdata['date'].values
closes = dfdata['close'].values
highs = dfdata['high'].values
lows = dfdata['low'].values
macddiclist = ta.cal_macd(dates, closes)
macd, dif, dea = DP.get_macd_list(macddiclist)
divstatus = ta.macd_in_bulldivergence(dates, macd)
divstatus['div']
if (divstatus['div'] != 0):
return True
else:
return False
def batch_invaluezone_divergence(dataset='hs300', tradeCycle='D', recent=5):
lasttradetimestr = DP.get_last_trade_time('D')
colNames = [
'code', 'name', 'period', 'curclose', 'sma', 'ATR', 'ATRRatio',
'rewardrisk', 'primImpScore', 'tradeImpScore', 'primdivscore',
'divscore', 'difdeascore', 'macdscore', 'totalscore'
]
filename = StrategyName + lasttradetimestr + '-' + dataset + '-' + tradeCycle
RootDir = os.path.join(os.pardir, 'data', lasttradetimestr)
DP.mkdir(RootDir)
filepath = os.path.join(RootDir, filename + '.xls')
codelist = DI.get_stockcode_list(dataset)
dfSelected = pd.DataFrame([], columns=colNames)
dfScreen = codelist[['code', 'name']]
totalrows = len(dfScreen)
for i in range(totalrows):
code = dfScreen.loc[i, 'code']
code = DP.codeType(code)
name = dfScreen.loc[i, 'name']
if (DI.is_tingpai(code)):
continue
dfdata = ts.get_k_data(code, ktype=tradeCycle)
if (len(dfdata) < 120): #剔除交易时间少于120天的个股
continue
dates = dfdata['date'].values
closes = dfdata['close'].values
highs = dfdata['high'].values
lows = dfdata['low'].values
if (not ta.invaluezone(closes, 10, 20)):
continue
macddiclist = ta.cal_macd(dates, closes)
macd, dif, dea = DP.get_macd_list(macddiclist)
if (min(macd[-3:]) > 0):
continue
divStatus = ta.macd_in_bulldivergence(dates, macd)
divscore = divStatus['div']
macdscore = divStatus['macd']
period = divStatus['period']
if (divscore != 0):
print(code, name, divscore, macdscore, period)
absdis, perdis, maprice = ta.curdistosma(closes, 20)
curATR = ta.last_atr(dates, closes, highs, lows)
ATRWZ = absdis / curATR
curClose = closes[-1]
ATRRatio = curATR / curClose
primCycle = GetPrimaryCycle(tradeCycle)
dataprimCycle = ts.get_k_data(code, ktype=primCycle)
primCloses = dataprimCycle['close'].values
primdates = dataprimCycle['date'].values
primlows = dataprimCycle['low'].values
primImpScore = FBD.ImpluseScore(primdates, primCloses)
tradeImpScore = FBD.ImpluseScore(dates, closes)
primmacd = pd.Series(ta.cal_macd(primdates, primCloses))
primmacd, primdif, primdea = DP.get_macd_list(primmacd)
primdivStatus = ta.macd_in_bulldivergence_signal(
primdates, primmacd)
primdivscore = primdivStatus['div']
if (primdivscore != 0):
primdivscore = 1
difdeadivscore = 0
if (ta.in_loose_bulldivergence(dif, period)):
difdeadivscore += 1
if (ta.in_loose_bulldivergence(dea, period)):
difdeadivscore += 1
totalscore = primImpScore + tradeImpScore + primdivscore + divscore + difdeadivscore + macdscore
curATR = round(curATR, 2)
ATRRatio = round(ATRRatio * 100, 2)
ATRWZ = round(ATRWZ, 2)
maprice = round(maprice, 2)
colNames = [
'code', 'name', 'period', 'curclose', 'sma', 'ATR', 'ATRRatio',
'ATRWZ', 'primImpScore', 'tradeImpScore', 'primdivscore',
'divscore', 'difdeascore', 'macdscore', 'totalscore'
]
s = Series([
code, name, period, curClose, maprice, curATR, ATRRatio, ATRWZ,
primImpScore, tradeImpScore, primdivscore, divscore,
difdeadivscore, macdscore, totalscore
],
index=colNames)
dfSelected = dfSelected.append(s, ignore_index=True)
dfSelected = dfSelected.sort_values(by='totalscore', ascending=False)
dfSelected = pd.DataFrame(dfSelected.values, columns=dfSelected.columns)
DI.Write_DF_T0_Excel(filepath, dfSelected, 'selected')