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 get_common_parameters(code, tradeCycle):
from Analyze import Technical as ta
from Strategies import False_Breakout_Divergence as FB
smaperiod = 20
colNames = [
'code', 'tradeCycle', 'curclose', 'sma', 'ATR', 'ATRRatio', 'ATRWZ',
'ImpLight', 'upchannel', 'downchannel', 'valueWZ', 'rewardratio'
]
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
print('No Enough Trading Days')
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 = get_macd_list(macddiclist)
absdis, perdis, maprice = ta.curdistosma(closes, smaperiod)
curATR = ta.last_atr(dates, closes, highs, lows)
upchannel = maprice + 2 * curATR
downchannel = maprice - 2 * curATR
curClose = closes[-1]
ATRRatio = curATR / curClose
ATRWZ = absdis / curATR
smadir = ta.SMA_Direction(dates, closes)
macddir = ta.MACD_Direction(dates, closes)
ImpLight = FB.ImpluseLight(smadir, macddir)
rewardratio = (upchannel - curClose) * 100 / curClose
value_re = ta.SMAValueZone_GX(dates, closes)
curATR = round(curATR, 2)
ATRRatio = round(ATRRatio * 100, 2)
rewardratio = round(rewardratio, 2)
ATRWZ = round(ATRWZ, 2)
maprice = round(maprice, 2)
upchannel = round(upchannel, 2)
downchannel = round(downchannel, 2)
s = pd.Series([
code, tradeCycle, curClose, maprice, curATR, ATRRatio, ATRWZ, ImpLight,
upchannel, downchannel, value_re, rewardratio
],
index=colNames)
return s
def ImpluseScore(dates, closes):
'''
:param dates:日期
:param closes: 收盘价
:return: 脉冲评分：红0分，绿1分，蓝2分
'''
smadir = ta.SMA_Direction(dates, closes)
macddir = ta.MACD_Direction(dates, closes)
lightcolor = ImpluseLight(smadir, macddir)
if (lightcolor == 'Red'):
score = 0
elif (lightcolor == 'green'):
score = 1
else:
score = 2
return score
def VecCycleIndicators(stockcode = None,Cycle = 'D'):
dftotal = ts.get_k_data(stockcode, ktype=Cycle)
totalrows = len(dftotal)
if(totalrows <100):
print('Insufficiennt Data for Analyze'
)
return None
startIndex = 0
colNames = ['date',Cycle+'-smadir',Cycle+'-macddir',Cycle+'-light',Cycle+'-bandratio',
Cycle+'-upband',Cycle+'-lowband',Cycle+'-valuerel',Cycle+'-rsi']
dfIndicator = DataFrame([],columns=colNames)
while(startIndex + 100 <= totalrows):
print(startIndex)
df = dftotal[:startIndex + 100]
dates = df['date'].values
closes = df['close'].values
lows = df['low'].values
highs = df['high'].values
date = dates[-1]
smadir = ta.SMA_Direction(dates, closes)
macddir = ta.MACD_Direction(dates, closes)
lightcolor = trpscr.ImpluseLight(smadir,macddir)
rsi = ta.get_cur_rsi(dates,closes)
uppenetrationCounter, downpenetrationCounter, bandratio, upband, lowband \
= ta.envelop_ratio(dates, highs, lows, closes)
value_re = ta.SMAValueZone_GX(dates, closes)
s = Series([date,smadir,macddir,lightcolor,bandratio,upband,lowband,value_re,rsi],index = colNames)
dfIndicator = dfIndicator.append(s,ignore_index=True)
startIndex += 1
FileName = stockcode + Cycle +'.csv'
FilePath = os.path.join(DataRoot,FileName)
dfIndicator.to_csv(FilePath)
return dfIndicator
def sz50Daily():
codelist = DI.get_stockcode_list('sz50')
colNames = [
'stockcode', 'smadir', 'uppenetrationCounter',
'downpenetrationCounter', 'macddir', 'bandratio', 'upband', 'lowband',
'value_re'
]
filterdf = DataFrame([], columns=colNames)
filepath = os.path.join(os.getcwd(), 'data', 'list')
for stockcode in codelist['code']:
df = ts.get_k_data(stockcode, ktype='D')
dates = df['date'].values
closes = df['close'].values
lows = df['low'].values
highs = df['high'].values
smadir = ta.SMA_Direction(dates, closes)
macddir = ta.MACD_Direction(dates, closes)
uppenetrationCounter, downpenetrationCounter, bandratio, upband, lowband\
= ta.envelop_ratio(dates,highs,lows,closes)
#print(stockcode,uppenetrationCounter, downpenetrationCounter, bandratio, upband, lowband)
value_re = ta.SMAValueZone_GX(dates, closes)
s = Series([
stockcode, smadir, uppenetrationCounter, downpenetrationCounter,
macddir, bandratio, upband, lowband, value_re
],
index=colNames)
filterdf = filterdf.append(s, ignore_index=True)
filterdf.to_csv(filepath + '.csv')