def smaexit(df,smaperiod,cycle):
for i in range(len(df)):
code = DP.codeType(df.loc[i, 'code'])
curclose = df.loc[i, 'curclose']
startdate = df.loc[i, 'time']
tradata = ts.get_k_data(code, ktype=cycle)
tradata = pd.DataFrame(tradata.values, columns=tradata.columns)
posindex =list(tradata['date']).index(startdate)
closes = tradata['close']
posindex += 1
while(posindex < len(closes)):
posclose = tradata.loc[posindex,'close']
possmaprice = sum(closes[posindex-smaperiod+1:posindex+1])/smaperiod
if(posclose < possmaprice):
exitprice = posclose
exitdate = tradata.loc[posindex, 'date']
exitreturn = (exitprice - curclose) * 100 / curclose
break
posindex += 1
if(posindex == len(closes)):
exitprice = posclose
exitdate = tradata.loc[posindex-1, 'date']
exitreturn = (exitprice - curclose) * 100 / curclose
df.loc[i,'exitdate'] = exitdate
df.loc[i,'exitprice'] = exitprice
df.loc[i,'exitreturn'] = exitreturn
return df
def batch_position(codelist, period=240, value='position', Cycle='D'):
colNames = ['code', 'name', 'position', 'curup', 'curdown']
dfPosition = DataFrame([], columns=colNames)
index_date = DP.get_last_trade_time()
for code, name in zip(codelist['code'], codelist['name']):
code = DP.codeType(code)
print(code)
if (DI.is_tingpai(code)):
continue
df = ts.get_k_data(code, ktype=Cycle)
if (len(df) == 0):
continue
closes = df['close'].values
last_date = df.loc[len(df) - 1, 'date']
if (len(df) < period or last_date != index_date):
continue
closes = closes[-period:]
position, curup, curdown = position_in_period(closes)
s = Series([code, name, position, curup, curdown], index=colNames)
dfPosition = dfPosition.append(s, ignore_index=True)
dfPosition = dfPosition.sort_values(by=value, ascending=True)
dfPosition = DataFrame(dfPosition.values, columns=dfPosition.columns)
return dfPosition
def batch_vectorized_divergence(dataset='hs300', cycle='D'):
colNames = [
'code', 'time', 'period', 'curclose', 'sma', 'ATR', 'ATRRatio',
'rewardratio', 'riskratio', 'targetprice', 'cutshort', 'ATRWZ',
'rewardrisk', 'primImpScore', 'tradeImpScore', 'valuescore',
'primdivscore', 'divscore', 'difdeascore', 'macdscore', 'totalscore',
'targetprice1', 'targetprice2'
]
codelist = DI.get_stockcode_list(dataset)
dfScreen = codelist[['code', 'name']]
totalrows = len(dfScreen)
dftotal = DataFrame([], columns=colNames)
for i in range(totalrows):
code = dfScreen.loc[i, 'code']
print(code)
code = DP.codeType(code)
name = dfScreen.loc[i, 'name']
dfsub = vectorized_falsebreakout_divergence(code, cycle)
dfsub['name'] = name
dftotal = pd.concat([dftotal, dfsub])
RootDir = os.path.join(os.pardir, 'data', 'Vectorized', StrategyName)
if (not os.path.exists(RootDir)):
DP.mkdir(RootDir)
filename = dataset + 'whole' + cycle + '.xls'
filepath = os.path.join(RootDir, filename)
dftotal = dftotal.sort_values(by='time')
dftotal = DataFrame(dftotal.values, columns=dftotal.columns)
dftotal = Performance.vectorized_forward_return(dftotal, cycle)
DI.Write_DF_T0_Excel(filepath, dftotal, 'selected')
def get_stock_basic_parameters(code, Cycle='D', period=480):
'''
:param code:股票代码
:param Cycle: 分析周期
:param period: 分析的时间周期
:return:返回指标列表：
['EMADir','SMADir','MACDDir','SMALight','EMALight','valuerel','ATRWz','ATRRatio',
'oneATR','twoATR','threeATR','moneATR','mtwoATR','mthreeATR','position','curup',
'curdown']
'''
code = DP.codeType(code)
print(code)
dfdata = ts.get_k_data(code, ktype=Cycle)
if (len(dfdata) < 60):
return None
closes = dfdata['close'].values
dates = dfdata['date'].values
EMADir = ta.EMA_Direction(dates, closes)
SMADir = ta.SMA_Direction(dates, closes)
MACDDir = ta.MACD_Direction(dates, closes)
SMALight = Triple.ImpluseLight(SMADir, MACDDir)
EMALight = Triple.ImpluseLight(EMADir, MACDDir)
valuerel = ta.SMAValueZone_GX(dates, closes)
highs = dfdata['high'].values
lows = dfdata['low'].values
curatr = ta.last_atr(dates, closes, highs, lows)
absdis, perdis, maprice = ta.curdistosma(closes, 20)
ATRWz = absdis / curatr
ATRRatio = absdis / maprice
oneATR = maprice + curatr
twoATR = maprice + 2 * curatr
threeATR = maprice + 3 * curatr
moneATR = maprice - curatr
mtwoATR = maprice - 2 * curatr
mthreeATR = maprice - 3 * curatr
if (len(closes) > period):
closes = closes[-period:]
position, curup, curdown = Statistical.position_in_period(closes)
valuelist = [
EMADir, SMADir, MACDDir, SMALight, EMALight, valuerel, ATRWz, ATRRatio,
oneATR, twoATR, threeATR, moneATR, mtwoATR, mthreeATR, position, curup,
curdown
]
namelist = [
'EMADir', 'SMADir', 'MACDDir', 'SMALight', 'EMALight', 'valuerel',
'ATRWz', 'ATRRatio', 'oneATR', 'twoATR', 'threeATR', 'moneATR',
'mtwoATR', 'mthreeATR', 'position', 'curup', 'curdown'
]
colnames = []
for ele in namelist:
name = Cycle + '-' + ele
colnames.append(name)
s = pd.Series(valuelist, index=colnames)
return s
def vectorized_forward_return(df,cycle):
forperiods = [1, 3, 5, 10, 15]
for i in range(len(df)):
code = DP.codeType(df.loc[i,'code'])
curclose = df.loc[i,'curclose']
startdate = df.loc[i,'time']
tradata = ts.get_k_data(code,start=startdate,ktype=cycle)
tradata = pd.DataFrame(tradata.values,columns=tradata.columns)
closes = tradata['close']
for period in forperiods:
if (period < len(closes)):
forclose = closes[period]
forreturn = (forclose - curclose) * 100 / curclose
else:
forclose = None
forreturn = None
df.loc[i,'close'+str(period)] = forclose
df.loc[i, 'return' + str(period)] = forreturn
return df
def batch_divergence_monitor(stocklist=None):
lasttradetimestr = DP.get_last_trade_time('D')
if stocklist is None:
stocklist = ['longtou', 'etf']
for dataset in stocklist:
for tradeCycle in ['W', 'D', '60', '30', '15']:
colNames = [
'code', 'name', 'cycle', 'period', 'curclose', 'sma', 'ATR',
'ATRRatio', 'rewardrisk', 'primImpScore', 'tradeImpScore',
'primdivscore', 'divscore', 'difdeascore', 'macdscore',
'totalscore'
]
filename = StrategyName + '-' + dataset
RootDir = os.path.join(os.pardir, 'data', lasttradetimestr)
DP.mkdir(RootDir)
filepath = os.path.join(RootDir, filename + '.csv')
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
macddiclist = ta.cal_macd(dates, closes)
macd, dif, dea = DP.get_macd_list(macddiclist)
divStatus = ta.macd_in_bulldivergence_signal(dates, macd)
divscore = divStatus['div']
macdscore = divStatus['macd']
period = divStatus['period']
if (divscore != 0):
print(code, name, tradeCycle, 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)
s = Series([
code, name, tradeCycle, 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)
if os.path.exists(filepath):
dfSelected.to_csv(filepath, mode='a', header=None)
else:
dfSelected.to_csv(filepath)
def screener2():
totalrows = len(dfScreen)
for index in range(totalrows):
code = dfScreen.loc[index, 'code']
code = DP.codeType(code)
stockcode = code
df_trade = ts.get_k_data(code=stockcode, ktype=tradecycle)
dates = df_trade['date']
closes = df_trade['close']
lows = df_trade['low']
sma5 = ta.cal_sma(dates, closes, 5)
sma5 = Series(sma5)
macd = ta.cal_macd(dates, closes)
macd = macd[len(macd) - len(sma5):]
macd = Series(macd)
sma5 = sma5[len(macd) - len(sma5):]
sma5 = Series(sma5)
df_trade = df_trade[len(df_trade) - len(sma5):]
df_trade = DataFrame(df_trade.values, columns=df_trade.columns)
df_trade['sma5'] = sma5
df_trade['macd'] = macd
totalrows = len(df_trade)
i = 5
while (i < totalrows):
if (df_trade.loc[i, 'close'] > df_trade.loc[i, 'sma5']['sma5']
and df_trade.loc[i - 1,
'close'] < df_trade.loc[i, 'sma5']['sma5']
and df_trade.loc[i, 'macd']['diff'] >
df_trade.loc[i, 'macd']['dea']):
j = i
while (j > 0):
if df_trade.loc[j, 'macd']['diff'] < df_trade.loc[
j, 'macd']['dea']:
break
j -= 1
closesslice = closes[j:i]
if (len(closesslice) < 8):
i += 1
continue
print(closesslice)
k = np.argmax(closesslice)
print('here')
print(j, k, i)
if (min(closesslice[j:k]) < min(closesslice[k:i])):
date = df_trade.loc[i, 'date'][:10]
df_prim = ts.get_k_data(code=stockcode,
ktype=primarycycle,
start='2000-01-01',
end=date)
primcloses = df_prim['close'][-5:]
primclose = primcloses.values[-1]
primsma5 = np.average(primcloses)
primmacd = ta.cal_macd(dates, closes)
primdiff = primmacd[len(primmacd) - 1]['diff']
primdea = primmacd[len(primmacd) - 1]['dea']
if (primclose > primsma5 and primdiff > primdea):
#print(df_trade.loc[i, 'date'])
curlows = lows[:i]
curdates = dates[:i]
difenxinglist = ta.get_pre_difenxing(
curdates, curlows, 4)
#print(difenxinglist)
if (len(difenxinglist) < 4):
continue
# difenxinglist[0]['low'] < difenxinglist[1]['low'] and
else:
if (difenxinglist[1]['low'] <
difenxinglist[2]['low']
and difenxinglist[2]['low'] <
difenxinglist[3]['low']):
print(stockcode, df_trade.loc[i, 'date'])
i += 1
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')
def vectorized_falsebreakout_divergence(code='510300',
tradeCycle='D',
recent=5):
colNames = [
'code', 'time', 'period', 'curclose', 'sma', 'ATR', 'ATRRatio',
'rewardratio', 'riskratio', 'targetprice', 'cutshort', 'ATRWZ',
'rewardrisk', 'primImpScore', 'tradeImpScore', 'valuescore',
'primdivscore', 'divscore', 'difdeascore', 'macdscore', 'totalscore',
'targetprice1', 'targetprice2'
]
filename = StrategyName + '-' + code + '-' + tradeCycle
dfSelected = pd.DataFrame([], columns=colNames)
RootDir = os.path.join(os.pardir, 'data', 'Vectorized')
DP.mkdir(RootDir)
filepath = os.path.join(RootDir, filename + '.xls')
code = DP.codeType(code)
dfdata = ts.get_k_data(code, start='2000-01-01',
ktype=tradeCycle) #start = '2016-01-01',
if (len(dfdata) < 120): #剔除交易时间少于120天的个股
print('Not Enough data for decision making.')
totaldates = dfdata['date'].values
totalcloses = dfdata['close'].values
totalhighs = dfdata['high'].values
totallows = dfdata['low'].values
macddiclist = ta.cal_macd(totaldates, totalcloses)
totalmacd, totaldif, totaldea = DP.get_macd_list(macddiclist)
i = 120
while i < len(totalcloses):
closes = totalcloses[:i]
highs = totalhighs[:i]
lows = totallows[:i]
macd = totalmacd[:i]
dif = totaldif[:i]
dea = totaldea[:i]
dates = totaldates[:i]
if (macd[-1] < macd[-2] or macd[-3] <
macd[-2]): #macd[-1]> 0 这个条件由于without right shoulder删除
i = i + 1
continue
divStatus = ta.macd_in_bulldivergence_signal(dates, macd)
divscore = divStatus['div']
macdscore = divStatus['macd']
period = divStatus['period']
if (divscore != 0 and isPriceNewLow(lows, recent, period, tradeCycle)):
date = dates[-1]
absdis, perdis, maprice = ta.curdistosma(closes, 20)
curATR = ta.last_atr(dates, closes, highs, lows)
targetprice = maprice + 2 * curATR #应该是前高更合理
prelow = min(lows[-recent:]) # 因此此处要求近recent天创period天新低
cutshort = prelow
ATRWZ = absdis / curATR
curClose = closes[-1]
targetprice2 = curClose + 2 * curATR
targetprice3 = np.max(closes[-period:])
ATRRatio = curATR / curClose
rewardratio = (targetprice - curClose) * 100 / curClose
riskratio = (curClose - cutshort) * 100 / curClose
rewardrisk = (targetprice - curClose) / (curClose - cutshort)
primCycle = GetPrimaryCycle(tradeCycle)
dataprimCycle = ts.get_k_data(code, ktype=primCycle)
primCloses = dataprimCycle['close'].values
primdates = dataprimCycle['date'].values
primlows = dataprimCycle['low'].values
primImpScore = ImpluseScore(primdates, primCloses)
tradeImpScore = ImpluseScore(dates, closes)
valuescore = value_score(dates, closes)
primmacd = pd.Series(ta.cal_macd(primdates, primCloses))
primmacd, primdif, primdea = DP.get_macd_list(primmacd)
isPrimNewLow = isPriceNewLow(primlows, recent, 30)
primdivStatus = ta.macd_in_bulldivergence_signal(
primdates, primmacd)
primdivscore = primdivStatus['div']
if (primdivscore != 0 and isPrimNewLow != 0):
primdivscore = 2
elif (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 + valuescore + primdivscore + divscore + difdeadivscore + macdscore
curATR = round(curATR, 2)
ATRRatio = round(ATRRatio * 100, 2)
rewardratio = round(rewardratio, 2)
riskratio = round(riskratio, 2)
ATRWZ = round(ATRWZ, 2)
rewardrisk = round(rewardrisk, 2)
targetprice = round(targetprice, 2)
maprice = round(maprice, 2)
s = Series([
code, date, period, curClose, maprice, curATR, ATRRatio,
rewardratio, riskratio, targetprice, cutshort, ATRWZ,
rewardrisk, primImpScore, tradeImpScore, valuescore,
primdivscore, divscore, difdeadivscore, macdscore, totalscore,
targetprice2, targetprice3
],
index=colNames)
dfSelected = dfSelected.append(s, ignore_index=True)
i += 1
#dfSelected = dfSelected.sort_values(by='totalscore',ascending=False)
dfSelected = pd.DataFrame(dfSelected.values, columns=dfSelected.columns)
#dfSelected.to_csv(filepath)
DI.Write_DF_T0_Excel(filepath, dfSelected, 'selected')
return dfSelected
def batch_falsebreakout_divergence(dataset='hs300', tradeCycle='D', recent=3):
lasttradetimestr = DP.get_last_trade_time('D')
'''
colNames = ['code', 'time', 'period', 'curclose', 'sma', 'ATR', 'ATRRatio', 'rewardratio',
'riskratio', 'targetprice', 'cutshort', 'ATRWZ', 'rewardrisk', 'primImpScore',
'tradeImpScore', 'valuescore', 'primdivscore', 'divscore', 'difdeascore', 'macdscore',
'totalscore','targetprice1','targetprice2']
'''
colNames = [
'code', 'name', 'time', 'period', 'curclose', 'sma', 'ATR', 'ATRRatio',
'rewardratio', 'riskratio', 'targetprice', 'cutshort', 'ATRWZ',
'rewardrisk', 'primImpScore', 'tradeImpScore', 'valuescore',
'primdivscore', 'divscore', 'difdeascore', 'macdscore', 'totalscore',
'targetprice2', 'targetprice3'
]
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
macddiclist = ta.cal_macd(dates, closes)
macd, dif, dea = DP.get_macd_list(macddiclist)
if (not (macd[-2] < 0 and macd[-2] < macd[-1]
and macd[-2] < macd[-3])):
continue
if (code in dfSelected['code'].values):
continue
divStatus = ta.macd_in_bulldivergence_signal(dates, macd)
divscore = divStatus['div']
macdscore = divStatus['macd']
period = divStatus['period']
if (divscore != 0 and isPriceNewLow(lows, recent, period)):
absdis, perdis, maprice = ta.curdistosma(closes, 20)
curATR = ta.last_atr(dates, closes, highs, lows)
targetprice = maprice + 2 * curATR
prelow = min(lows[-recent:]) #因此此处要求近recent天创period天新低
ATRWZ = absdis / curATR
curClose = closes[-1]
#cutshort = min(prelow, curClose - curATR)
cutshort = prelow
ATRRatio = curATR / curClose
#rewardratio = (targetprice-curClose)*100/curClose
riskratio = (curClose - cutshort) * 100 / curClose
#rewardrisk = (targetprice-curClose)/(curClose-cutshort)
primCycle = GetPrimaryCycle(tradeCycle)
dataprimCycle = ts.get_k_data(code, ktype=primCycle)
primCloses = dataprimCycle['close'].values
primdates = dataprimCycle['date'].values
primlows = dataprimCycle['low'].values
primImpScore = ImpluseScore(primdates, primCloses)
tradeImpScore = ImpluseScore(dates, closes)
valuescore = value_score(dates, closes)
primmacd = pd.Series(ta.cal_macd(primdates, primCloses))
primmacd, primdif, primdea = DP.get_macd_list(primmacd)
isPrimNewLow = isPriceNewLow(primlows, recent, 30)
primdivStatus = ta.macd_in_bulldivergence_signal(
primdates, primmacd)
primdivscore = primdivStatus['div']
if (primdivscore != 0 and isPrimNewLow != 0):
primdivscore = 2
elif (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 + valuescore + primdivscore + divscore + difdeadivscore + macdscore
curATR = round(curATR, 4)
targetprice2 = curClose + 2 * curATR
targetprice3 = np.max(closes[-period:])
target = np.min(
[float(targetprice),
float(targetprice2),
float(targetprice3)])
rewardratio = (target - curClose) * 100 / curClose
rewardrisk = (target - curClose) / (curClose - cutshort)
ATRRatio = round(ATRRatio * 100, 3)
rewardratio = round(rewardratio, 3)
riskratio = round(riskratio, 3)
ATRWZ = round(ATRWZ, 3)
rewardrisk = round(rewardrisk, 3)
targetprice = round(targetprice, 3)
maprice = round(maprice, 3)
cutshort = round(cutshort, 3)
import datetime
curtime = datetime.datetime.now()
selectedcolNames = [
'ATR', 'ATRRatio', 'code', 'name', 'time', 'curclose',
'cutshort', 'rewardratio', 'riskratio', 'rewardrisk', 'sma',
'targetprice', 'targetprice2', 'targetprice3'
]
print(curATR, ATRRatio, code, tradeCycle, name, curtime, curClose,
cutshort, rewardratio, riskratio, rewardrisk, maprice,
targetprice, targetprice2, targetprice3)
s = Series([
code, name, curtime, period, curClose, maprice, curATR,
ATRRatio, rewardratio, riskratio, targetprice, cutshort, ATRWZ,
rewardrisk, primImpScore, tradeImpScore, valuescore,
primdivscore, divscore, difdeadivscore, macdscore, totalscore,
targetprice2, targetprice3
],
index=colNames)
dfSelected = dfSelected.append(s, ignore_index=True)
dfSelected = dfSelected[selectedcolNames]
dfSelected = dfSelected.sort_values(by='time', ascending=False)
dfSelected = pd.DataFrame(dfSelected.values, columns=dfSelected.columns)
if (not os.path.exists(filepath)):
DI.Write_DF_T0_Excel(filepath, dfSelected, 'selected')
else:
dftotal = DI.Get_TrdData_FromExcel(filepath, 'selected')
dftotal = pd.concat([dftotal, dfSelected])
DI.Write_DF_T0_Excel(filepath, dftotal, 'selected')
def vectorized_smabreak_divergence(code='510300', tradeCycle='D'):
colNames = [
'time', 'code', 'curclose', 'sma', 'ATR', 'ATRRatio', 'ATRWZ',
'tradeImpScore', 'primImpScore'
]
filename = StrategyName + '-' + code + '-' + tradeCycle
dfSelected = pd.DataFrame([], columns=colNames)
RootDir = os.path.join(os.pardir, 'data', 'Vectorized')
filepath = os.path.join(RootDir, filename + '.xls')
code = DP.codeType(code)
startdate = '2000-01-01'
dfdata = ts.get_k_data(code, start=startdate, end='', ktype=tradeCycle)
if (len(dfdata) < 120): # 剔除交易时间少于120天的个股
print('Not Enough data for decision making.')
totaldates = dfdata['date'].values
totalcloses = dfdata['close'].values
totalhighs = dfdata['high'].values
totallows = dfdata['low'].values
macddiclist = ta.cal_macd(totaldates, totalcloses)
totalmacd, totaldif, totaldea = DP.get_macd_list(macddiclist)
i = 120
while i < len(totalcloses):
closes = totalcloses[:i]
highs = totalhighs[:i]
lows = totallows[:i]
dates = totaldates[:i]
if (not ta.smaupcrosssignal(closes, 20)):
i += 1
continue
primCycle = GetPrimaryCycle(tradeCycle)
if (stock_in_divergece(code,
primCycle,
startdate=startdate,
enddate=dates[-1])):
absdis, perdis, maprice = ta.curdistosma(closes, 20)
curATR = ta.last_atr(dates, closes, highs, lows)
ATRWZ = absdis / curATR
curClose = closes[-1]
ATRRatio = curATR / curClose
tradeImpScore = FBD.ImpluseScore(dates, closes)
curATR = round(curATR, 2)
ATRRatio = round(ATRRatio * 100, 2)
ATRWZ = round(ATRWZ, 2)
maprice = round(maprice, 2)
dataprimCycle = ts.get_k_data(code, ktype=primCycle)
primCloses = dataprimCycle['close'].values
primdates = dataprimCycle['date'].values
primImpScore = FBD.ImpluseScore(primdates, primCloses)
s = Series([
dates[-1], code, curClose, maprice, curATR, ATRRatio, ATRWZ,
tradeImpScore, primImpScore
],
index=colNames)
dfSelected = dfSelected.append(s, ignore_index=True)
else:
pass
i += 1
# dfSelected = dfSelected.sort_values(by='totalscore',ascending=False)
dfSelected = pd.DataFrame(dfSelected.values, columns=dfSelected.columns)
# dfSelected.to_csv(filepath)
DI.Write_DF_T0_Excel(filepath, dfSelected, 'selected')
return dfSelected