def getStrategy_STOCH(start_trading_day,end_trading_day,_time,_close,_max,_min):
point = []
iday = _time.index(start_trading_day)
eday = _time.index(end_trading_day)
slowk,slowd = ta.STOCH(np.array(_max, dtype='f8'),np.array(_min, dtype='f8'),np.array(_close, dtype='f8'), fastk_period = 5,slowk_period=3,slowd_period=3)
slowk = slowk[iday:eday]
slowd = slowd[iday:eday]
point.append(0)
for i in range(1,len(slowk)):
if (slowk[i-1] <= slowd[i-1]) and (slowk[i] >= slowd[i]) and (slowk[i] <= 30):
point.append(1)
elif (slowk[i-1] <= 50) and (slowk[i] >= 50):
point.append(-1)
else:
point.append(0)
return point
python类STOCH的实例源码
def getStrategy_STOCH(start_trading_day,end_trading_day,_time,_close,_max,_min):
point = []
iday = _time.index(start_trading_day)
eday = _time.index(end_trading_day)
slowk,slowd = ta.STOCH(np.array(_max, dtype='f8'),np.array(_min, dtype='f8'),np.array(_close, dtype='f8'), fastk_period = 5,slowk_period=3,slowd_period=3)
slowk = slowk[iday:eday]
slowd = slowd[iday:eday]
point.append(0)
for i in range(1,len(slowk)):
if (slowk[i-1] <= slowd[i-1]) and (slowk[i] >= slowd[i]) and (slowk[i] <= 30):
point.append(1)
elif (slowk[i-1] <= 50) and (slowk[i] >= 50):
point.append(-1)
else:
point.append(0)
return point
def compute(self, today, assets, out, close, high, low):
stoch_list = []
for col_c, col_h, col_l in zip(close.T, high.T, low.T):
try:
_, slowd = talib.STOCH(col_h, col_l, col_c,
fastk_period=5, slowk_period=3, slowk_matype=0,
slowd_period=3, slowd_matype=0)
stoch_list.append(slowd[-1])
# if error calculating
except:
stoch_list.append(np.nan)
out[:] = stoch_list
# 5-day Money Flow / Volume
def STOCH(df, n = 14, slowk_period = 3, slowd_period = 3):
fastk, fastd = talib.STOCHF(df['high'].values, df['low'].values, df['close'].values, fastk_period = n, fastd_period=slowk_period)
slowk, slowd = talib.STOCH(df['high'].values, df['low'].values, df['close'].values, fastk_period = n, slowk_period=slowk_period, slowd_period=slowd_period)
fk = pd.Series(fastk, index = df.index, name = "STOCHFK_%s_%s_%s" % (str(n), str(slowk_period), str(slowd_period)))
sk = pd.Series(slowk, index = df.index, name = "STOCHSK_%s_%s_%s" % (str(n), str(slowk_period), str(slowd_period)))
sd = pd.Series(slowd, index = df.index, name = "STOCHSD_%s_%s_%s" % (str(n), str(slowk_period), str(slowd_period)))
return pd.concat([fk, sk, sd], join='outer', axis=1)
def add_STOCH(self, fastk_period=5, slowk_period=3,
slowk_matype=0, slowd_period=3, slowd_matype=0,
types=['line', 'line'],
colors=['primary', 'tertiary'],
**kwargs):
"""Slow Stochastic Oscillator.
Note that the first argument of types and colors refers to Slow Stoch %K,
while second argument refers to Slow Stoch %D
(signal line of %K obtained by MA).
"""
if not (self.has_high and self.has_low and self.has_close):
raise Exception()
utils.kwargs_check(kwargs, VALID_TA_KWARGS)
if 'kind' in kwargs:
kwargs['type'] = kwargs['kind']
if 'kinds' in kwargs:
types = kwargs['type']
if 'type' in kwargs:
types = [kwargs['type']] * 2
if 'color' in kwargs:
colors = [kwargs['color']] * 2
name = 'STOCH({},{},{})'.format(str(fastk_period),
str(slowk_period),
str(slowd_period))
slowk = name + r'[%k]'
slowd = name + r'[%d]'
self.sec[slowk] = dict(type=types[0], color=colors[0])
self.sec[slowd] = dict(type=types[1], color=colors[1], on=slowk)
self.ind[slowk], self.ind[slowd] = talib.STOCH(self.df[self.hi].values,
self.df[self.lo].values,
self.df[self.cl].values,
fastk_period, slowk_period,
slowk_matype, slowd_period,
slowd_matype)
def calculate_stoch(self, period_name, closing_prices):
slowk, slowd = talib.STOCH(self.highs, self.lows, closing_prices, fastk_period=14, slowk_period=2, slowk_matype=0, slowd_period=3, slowd_matype=0)
self.current_indicators[period_name]['stoch_slowk'] = slowk[-1]
self.current_indicators[period_name]['stoch_slowd'] = slowd[-1]
def get_kdj(sorted_data):
close,high,low,ma5,ma10,ma20 = get_case_data(sorted_data)
slowk, slowd = ta.STOCH(high,low,close, fastk_period=9, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
slowkMA5 = ta.MA(slowk, timeperiod=5, matype=0)
slowkMA10 = ta.MA(slowk, timeperiod=10, matype=0)
slowkMA20 = ta.MA(slowk, timeperiod=20, matype=0)
slowdMA5 = ta.MA(slowd, timeperiod=5, matype=0)
slowdMA10 = ta.MA(slowd, timeperiod=10, matype=0)
slowdMA20 = ta.MA(slowd, timeperiod=20, matype=0)
operator = ''
#1.K???????——???90?????????10??????D??80???????????D??20???????????
if slowk[-1] >= 90:
operator += 'S9'
elif slowk[-1] <= 10:
operator += 'B1'
elif slowd[-1] >=80:
operator += 'S8'
elif slowd[-1] <= 20:
operator += 'B2'
#2.??????K???D??K?????D????????
if slowk[-1] > slowd[-1] and slowk[-2] <= slowd[-2]:
operator += 'BX'
elif slowk[-1] < slowd[-1] and slowk[-2] >= slowd[-2]:
operator += 'SX'
#3.???????????????????????
if ma5[-1] >= ma10[-1] and ma10[-1] >= ma20[-1]: #k???
if (slowkMA5[-1] <= slowkMA10[-1] and slowkMA10[-1] <= slowkMA20[-1]) or (slowdMA5[-1] <= slowdMA10[-1] and slowdMA10[-1] <= slowdMA20[-1]):
operator += 'S><'
elif ma5[-1] <= ma10[-1] and ma10[-1] <= ma20[-1]: #k???
if (slowkMA5[-1] >= slowkMA10[-1] and slowkMA10[-1] >= slowkMA20[-1]) or (slowdMA5[-1] >= slowdMA10[-1] and slowdMA10[-1] >= slowdMA20[-1]):
operator += 'B><'
return operator
def Get_KDJ(df):
#??9,3,3
slowk, slowd = ta.STOCH(np.array(df['high']), np.array(df['low']), np.array(df['close']), fastk_period=9, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
slowkMA5 = ta.MA(slowk, timeperiod=5, matype=0)
slowkMA10 = ta.MA(slowk, timeperiod=10, matype=0)
slowkMA20 = ta.MA(slowk, timeperiod=20, matype=0)
slowdMA5 = ta.MA(slowd, timeperiod=5, matype=0)
slowdMA10 = ta.MA(slowd, timeperiod=10, matype=0)
slowdMA20 = ta.MA(slowd, timeperiod=20, matype=0)
#16-17 K,D
df['slowk']=pd.Series(slowk,index=df.index) #K
df['slowd']=pd.Series(slowd,index=df.index)#D
dflen = df.shape[0]
MAlen = len(slowkMA5)
operate = 0
#1.K???????——???90?????????10??????D??80???????????D??20???????????
if df.iat[(dflen-1),16]>=90:
operate = operate + 3
elif df.iat[(dflen-1),16]<=10:
operate = operate - 3
if df.iat[(dflen-1),17]>=80:
operate = operate + 3
elif df.iat[(dflen-1),17]<=20:
operate = operate - 3
#2.??????K???D??K?????D?????????#???
if df.iat[(dflen-1),16]> df.iat[(dflen-1),17] and df.iat[(dflen-2),16]<=df.iat[(dflen-2),17]:
operate = operate + 10
#??????K??D?K?????D?????????#???
elif df.iat[(dflen-1),16]< df.iat[(dflen-1),17] and df.iat[(dflen-2),16]>=df.iat[(dflen-2),17]:
operate = operate - 10
#3.???????????????????????
if df.iat[(dflen-1),7]>=df.iat[(dflen-1),8] and df.iat[(dflen-1),8]>=df.iat[(dflen-1),9]:#K???
if (slowkMA5[MAlen-1]<=slowkMA10[MAlen-1] and slowkMA10[MAlen-1]<=slowkMA20[MAlen-1]) or \
(slowdMA5[MAlen-1]<=slowdMA10[MAlen-1] and slowdMA10[MAlen-1]<=slowdMA20[MAlen-1]): #K,D??
operate = operate - 1
elif df.iat[(dflen-1),7]<=df.iat[(dflen-1),8] and df.iat[(dflen-1),8]<=df.iat[(dflen-1),9]:#K???
if (slowkMA5[MAlen-1]>=slowkMA10[MAlen-1] and slowkMA10[MAlen-1]>=slowkMA20[MAlen-1]) or \
(slowdMA5[MAlen-1]>=slowdMA10[MAlen-1] and slowdMA10[MAlen-1]>=slowdMA20[MAlen-1]): #K,D??
operate = operate + 1
return (df,operate)
#??RSI??????
