def macd(
self,
fast_period: int,
slow_period: int,
signal_period: int,
islog: bool = False,
array: bool = False
) -> Union[
Tuple[np.ndarray, np.ndarray, np.ndarray],
Tuple[float, float, float]
]:
"""
MACD.
"""
if islog:
macd, signal, hist = talib.MACDEXT(
np.log(self.close), fastperiod=fast_period, fastmatype=1, slowperiod=slow_period,slowmatype=1,
signalperiod=signal_period, signalmatype=1
)
else:
macd, signal, hist = talib.MACDEXT(
self.close, fastperiod=fast_period, fastmatype=1, slowperiod=slow_period,slowmatype=1,
signalperiod=signal_period, signalmatype=1
)
hist = hist * 2
if array:
return macd, signal, hist
return macd[-1], signal[-1], hist[-1]
def macdext(close_ts, fastperiod=12, fastmatype=0, slowperiod=26, slowmatype=0, signalperiod=9, signalmatype=0):
import talib
close_np = close_ts.cpu().detach().numpy()
close_df = pd.DataFrame(close_np)
DIF = close_df.apply(lambda x: talib.MACDEXT(x, fastperiod=12, fastmatype=0, slowperiod=26, slowmatype=0, signalperiod=9, signalmatype=0)[0])
DEA = close_df.apply(lambda x: talib.MACDEXT(x, fastperiod=12, fastmatype=0, slowperiod=26, slowmatype=0, signalperiod=9, signalmatype=0)[1])
MACD = close_df.apply(lambda x: talib.MACDEXT(x, fastperiod=12, fastmatype=0, slowperiod=26, slowmatype=0, signalperiod=9, signalmatype=0)[2])
DIF_ts = torch.tensor(DIF.values, dtype=close_ts.dtype, device=close_ts.device)
DEA_ts = torch.tensor(DEA.values, dtype=close_ts.dtype, device=close_ts.device)
MACD_ts = torch.tensor(MACD.values, dtype=close_ts.dtype, device=close_ts.device)
return DIF_ts, DEA_ts, MACD_ts
def MACD(security_list, fastperiod=12, slowperiod=26, signalperiod=9):
# 修复传入为单只股票的情况
if isinstance(security_list, str):
security_list = [security_list]
# 计算 MACD
security_data = history(slowperiod * 2,
'1d',
'close',
security_list,
df=False,
skip_paused=True)
macd_DIF = {}
macd_DEA = {}
macd_HIST = {}
for stock in security_list:
macd_DIF[stock], macd_DEA[stock], macd = talib.MACDEXT(
security_data[stock],
fastperiod=fastperiod,
fastmatype=1,
slowperiod=slowperiod,
slowmatype=1,
signalperiod=signalperiod,
signalmatype=1)
macd_HIST[stock] = macd * 2
return macd_DIF, macd_DEA, macd_HIST
def momentum(self): adx = talib.ADX(self.high,self.low,self.close,self.period) adxr = talib.ADXR(self.high,self.low,self.close,self.period) apo = talib.APO(self.high,self.low,self.close,self.period) aroondown, aroonup = talib.AROON(self.high, self.low, period) aroonosc = talib.AROONOSC(self.high,self.low,self.period) bop = talib.BOP(self.opens,self.high,self.low,self.close) cci = talib.CCI(self.high,self.low,self.close,self.period) cmo = talib.CMO(self.close,self.period) dx = talib.DX(self.high,self.low,self.close,self.period) macd, macdsignal, macdhist = talib.MACD(self.close, fastperiod=period, slowperiod=period*5, signalperiod=period*2) macd1, macdsignal1, macdhist1 = talib.MACDEXT(self.close, fastperiod=12, fastmatype=0, slowperiod=26, slowmatype=0, signalperiod=9, signalmatype=0) macd2, macdsignal2, macdhist2 = talib.MACDFIX(self.close, signalperiod=9) mfi = talib.MFI(self.high, self.low, self.close, self.volume, timeperiod=14) minus_di = talib.MINUS_DI(self.high, self.low, self.close, timeperiod=14) minus_dm = talib.MINUS_DM(self.high, self.low, timeperiod=14) mom = talib.MOM(self.close, timeperiod=10) plus_di = talib.PLUS_DI(self.high, self.low, self.close, timeperiod=14) plus_dm = talib.PLUS_DM(self.high, self.low, timeperiod=14) ppo = talib.PPO(self.close, fastperiod=12, slowperiod=26, matype=0) roc = talib.ROC(self.close, timeperiod=10) rocp = talib.ROCP(self.close, timeperiod=10) rocr = talib.ROCR(self.close, timeperiod=10) rocr100 = talib.ROCR100(self.close, timeperiod=10) rsi = talib.RSI(self.close, timeperiod=14) slowk, slowd = talib.STOCH(self.high, self.low, self.close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0) fastk, fastd = talib.STOCHF(self.high, self.low, self.close, fastk_period=5, fastd_period=3, fastd_matype=0) fastk1, fastd1 = talib.STOCHRSI(self.close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0) trix = talib.TRIX(self.close, timeperiod=30) ultosc = talib.ULTOSC(self.high, self.low, self.close, timeperiod1=7, timeperiod2=14, timeperiod3=28) willr = talib.WILLR(self.high, self.low, self.close, timeperiod=14)
def fetch_hist_min_label_k_data(self, code, k_type, curr, total):
sys.stdout.write('\r fetching %s hist %s k_data, %d - %d' % (code, k_type, curr, total))
try:
table_name = 'hist_' + k_type
df = ts.get_k_data(code=code, autype='qfq', ktype=k_type, retry_count=20, pause=3)
if len(df) < 80:
return
last_index = len(df) - 1
if not if_times(df['date'][last_index], k_type):
df = df.drop(last_index)
macd_dif, macd_dea, macd_val = ta.MACDEXT(np.array(df['close']), fastperiod=12, fastmatype=1, slowperiod=26,
slowmatype=1, signalperiod=9, signalmatype=1)
macd_val = macd_val * 2
df['dif'] = pd.Series(macd_dif, index=df.index)
df['dea'] = pd.Series(macd_dea, index=df.index)
df['macd'] = pd.Series(macd_val, index=df.index)
remain_count = 40;
if k_type == '15':
remain_count = 80
df1 = df[(len(df) - remain_count):len(df)]
df1.to_sql(table_name, self.__db.get_engine(), if_exists='append', index=False)
except Exception as e:
print(":", e.__repr__(), code)
def get_technicals(self):
import talib as ta
stock_history = self.__get_history()
# Get Bollinger bands
stock_history['up_band'], \
stock_history['mid_band'], \
stock_history['low_band'] = ta.BBANDS(stock_history['Close'], timeperiod=20)
# Get MACD
stock_history['MACD'], \
stock_history['MACD_signal'], \
stock_history['MACD_hist'] = ta.MACDEXT(stock_history['Close'],
fastperiod=12, fastmatype=1,
slowperiod=26, slowmatype=1,
signalperiod=9, signalmatype=1)
# Get OBV
stock_history['OBV'] = ta.OBV(stock_history['Close'], stock_history['Volume'])
# Get RSI
stock_history['RSI'] = ta.RSI(stock_history['Close'], 14)
if self.graph:
self.__graph_technicals(stock_history)
return stock_history
def logic (ohlc, exchange="NSE"):
inputs = {"open":np.array(ohlc.open.values),
"high": np.array(ohlc.high.values),
"low": np.array(ohlc.low.values),
"close": np.array(ohlc.close.values),
"volume": np.array(ohlc.volume.values).astype(float)}
try:
sma8 = talib.SMA(inputs["close"], 8)
sma21 = talib.SMA(inputs["close"], 21)
rsi = talib.RSI(inputs["close"], timeperiod=15)
sma_rsi = talib.SMA(rsi,8)
#natr = talib.NATR(inputs["high"],inputs["low"],inputs["close"], timeperiod=14)
macd, macdsignal, macdhist = talib.MACDEXT(inputs["close"], fastperiod=12, fastmatype=0, slowperiod=26, slowmatype=0, signalperiod=9, signalmatype=0)
ultosc = talib.ULTOSC(inputs["high"],inputs["low"],inputs["close"],timeperiod1=7, timeperiod2=14, timeperiod3=28)
slowk, slowd = talib.STOCH(inputs["high"],inputs["low"],inputs["close"], 5, 3, 0, 3, 0)
#obv = talib.OBV(inputs["close"], inputs["volume"])
bop = talib.SMA(talib.BOP(inputs["open"], inputs["high"],inputs["low"],inputs["close"]),5)
data = pd.DataFrame({ "sma_rsi":sma_rsi,"slowk": slowk, "slowd": slowd,"rsi":rsi, "ultosc": ultosc,
"macd": macd, "macdsignal": macdsignal,"macdhist":macdhist,# "obv":obv, "natr": natr,
"bop":bop, "sma8":sma8, "sma21":sma21,
})
except Exception as e:
print(item + str(e) + " can't process")
return data
def calculations(self):
'''calculations'''
self.df['rsi'] = ta.RSI(self.df['close'], timeperiod=5)
self.df['apo'] = ta.APO(self.df['close'],
fastperiod=10,
slowperiod=5,
matype=0)
self.df['upperband'], self.df['middleband'], self.df[
'lowerband'] = ta.BBANDS(self.df['close'],
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)
self.df['ema'] = ta.EMA(self.df['close'], timeperiod=5)
self.df['ma'] = ta.MA(self.df['close'], timeperiod=5, matype=0)
self.df['sma'] = ta.MA(self.df['close'], timeperiod=5)
self.df['t3'] = ta.T3(self.df['close'], timeperiod=5, vfactor=0)
self.df['wma'] = ta.WMA(self.df['close'], timeperiod=5)
self.df['aroonosc'] = ta.AROONOSC(self.df['high'],
self.df['low'],
timeperiod=5)
self.df['cci'] = ta.CCI(self.df['high'],
self.df['low'],
self.df['close'],
timeperiod=5)
self.df['cmo'] = ta.CMO(self.df['close'], timeperiod=14)
self.df['macd'], self.df['macdsignal'], self.df[
'macdhist'] = ta.MACDEXT(self.df['close'],
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
self.df['slowk'], self.df['slowd'] = ta.STOCH(self.df['high'],
self.df['low'],
self.df['close'],
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
self.df['fastk'], self.df['fastd'] = ta.STOCHRSI(self.df['close'],
timeperiod=5,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
self.df['ultosc'] = ta.ULTOSC(self.df['high'],
self.df['low'],
self.df['close'],
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
self.df['adosc'] = ta.ADOSC(self.df['high'],
self.df['low'],
self.df['close'],
self.df['volume'],
fastperiod=3,
slowperiod=10)
return self.df
def macdext(self, fastPeriod, slowPeriod, signalPeriod, array=False):
"""MACD指标"""
macd, signal, hist = talib.MACDEXT(self.close, fastPeriod, 1,
slowPeriod, 1, signalPeriod, 1)
if array:
return macd, signal, hist * 2
return macd[-1], signal[-1], hist[-1] * 2
def indicator(self, period, proj_scores):
# Relative position
Relative_Position = []
for i in range(period, len(proj_scores)):
Relative_Position.append((proj_scores[i] - min(proj_scores[(i - period):i])) \
/(max(proj_scores[(i - period):i]) - min(proj_scores[(i - period):i])))
# Relative Strength Index
RSI = talib.RSI(np.array(proj_scores), period)
RSI = RSI[~np.isnan(RSI)]
# Momentum
MOM = (proj_scores / proj_scores.shift(period)).dropna() * 100
# Moving Average Convergence-Divergence
MACD_slow = period
MACD_fast = int(floor(period * 0.5))
MACD, MACD_signal, MACD_hist = talib.MACDEXT(np.array(proj_scores), fastperiod = MACD_fast, \
fastmatype = MA_Type.EMA, slowperiod = MACD_slow, \
slowmatype = MA_Type.EMA, signalperiod = 2, \
signalmatype = 0)
MACD = MACD[~np.isnan(MACD)]
# All Technical Indicators as Independent Variables for Prediction Models in DataFrame
predictor_in_df = pd.DataFrame({'RP': Relative_Position, 'RSI': RSI, 'MOM': MOM, 'MACD': MACD}, \
index = proj_scores.index[period:])
return predictor_in_df
def macd_cn(close, fast_period, slow_period, signal_period):
# short_win = 12 # 短期EMA平滑天数
# long_win = 26 # 长期EMA平滑天数
# macd_win = 20 # DEA线平滑天数
macd_dif, macd_dea, macd = tl.MACDEXT(close, fastperiod=fast_period, fastmatype=1, slowperiod=slow_period,
slowmatype=1, signalperiod=signal_period, signalmatype=1)
macd = macd * 2
return macd_dif, macd_dea, macd
def getMacd(self, close):
macdDIFF, macdDEA, macd = talib.MACDEXT(close,
fastperiod=12,
fastmatype=1,
slowperiod=26,
slowmatype=1,
signalperiod=9,
signalmatype=1)
macd = macd * 2
return macdDIFF, macdDEA, macd
def macd_cn(close_arr, fastperiod, slowperiod, signalperiod):
macdDIFF, macdDEA, macd = ta.MACDEXT(close_arr,
fastperiod=fastperiod,
fastmatype=1,
slowperiod=slowperiod,
slowmatype=1,
signalperiod=signalperiod,
signalmatype=1)
macd = macd * 2
return macdDIFF, macdDEA, macd
def get_MACD(close, fastperiod, slowperiod, signalperiod):
macdDIFF, macdDEA, macd = talib.MACDEXT(close,
fastperiod=fastperiod,
fastmatype=1,
slowperiod=slowperiod,
slowmatype=1,
signalperiod=signalperiod,
signalmatype=1)
macd = macd * 2
return macdDIFF, macdDEA, macd
def __init__(self, stock_data, fastperiod, slowperiod, signalperiod):
close = stock_data['close']
date = stock_data['date']
self.dif, self.dea, self.macd = tl.MACDEXT(close, fastperiod=fastperiod, fastmatype=1, slowperiod=slowperiod, slowmatype=1, signalperiod=signalperiod, signalmatype=1)
self.dif = pd.Series(self.dif).sort_index(ascending=False).dropna().reset_index(drop=True)
self.dea = pd.Series(self.dea).sort_index(ascending=False).dropna().reset_index(drop=True)
self.macd = pd.Series(self.macd).sort_index(ascending=False).dropna().reset_index(drop=True) * 2
self.sz = len(self.dif)
self.close = pd.Series(close).sort_index(ascending=False).reset_index(drop=True).head(self.sz)
self.date = pd.Series(date).sort_index(ascending=False).reset_index(drop=True).head(self.sz)
def TALIB_MACDEXT(close,
fastperiod=12,
fastmatype=talib.MA_Type.SMA,
slowperiod=26,
slowmatype=talib.MA_Type.SMA,
signalperiod=9,
signalmatype=talib.MA_Type.SMA):
'''00354,7,3'''
return talib.MACDEXT(close, fastperiod, fastmatype, slowperiod, slowmatype,
signalperiod, signalmatype)
def get_macdext(df):
df['DIFF'], df['DEA'], df['MACD'] = talib.MACDEXT(df['close'],
fastperiod=12,
fastmatype=1,
slowperiod=26,
slowmatype=1,
signalperiod=9,
signalmatype=1)
df['MACD'] = df['MACD'] * 2
return df
def MACD_CN(close, fastperiod, slowperiod, signalperiod):
macdDIFF, macdDEA, macd = tl.MACDEXT(close,
fastperiod=fastperiod,
fastmatype=1,
slowperiod=slowperiod,
slowmatype=1,
signalperiod=signalperiod,
signalmatype=1)
macd = macd * 2
return macdDIFF, macdDEA, macd
def get_macdext(ohlc):
# MACD (先行 12 日移動平均、遅行 26 日移動平均、 9 日シグナル線) を求める
macdext, macdsignalext, macdhistext = ta.MACDEXT(ohlc['4_close'], fastperiod=12, fastmatype=0, slowperiod=26,
slowmatype=0, signalperiod=9, signalmatype=0)
ohlc = ohlc.assign(
macdext=macdext
, macdsignalext=macdsignalext
, macdhistext=macdhistext
)
return ohlc
def add_MACDEXT(
self,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0,
types=["line", "line", "histogram"],
colors=["primary", "tertiary", "fill"],
**kwargs
):
"""Moving Average Convergence Divergence with Controllable MA Type.
Note that the first argument of types and colors refers to MACD,
the second argument refers to MACD signal line and the third argument
refers to MACD histogram.
"""
if not 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"]] * 3
if "color" in kwargs:
colors = [kwargs["color"]] * 3
name = "MACDEXT({},{},{})".format(
str(fastperiod), str(slowperiod), str(signalperiod)
)
macd = name
smacd = name + "[Sign]"
hmacd = name + "[Hist]"
self.sec[macd] = dict(type=types[0], color=colors[0])
self.sec[smacd] = dict(type=types[1], color=colors[1], on=macd)
self.sec[hmacd] = dict(type=types[2], color=colors[2], on=macd)
(self.ind[macd], self.ind[smacd], self.ind[hmacd]) = talib.MACDEXT(
self.df[self.cl].values,
fastperiod,
fastmatype,
slowperiod,
slowmatype,
signalperiod,
signalmatype,
)
def MACDEXT(Series,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0):
macd, macdsignal, macdhist = talib.MACDEXT(Series.values, fastperiod,
fastmatype, slowperiod,
slowmatype, signalperiod,
signalmatype)
return pd.Series(macd, index=Series.index), pd.Series(
macdsignal, index=Series.index), pd.Series(macdhist,
index=Series.index)
def MACDEXT(Close,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0):
macd, macdsignal, macdhist = pd.DataFrame(), pd.DataFrame(
), pd.DataFrame()
for i in Close.columns:
macd[i], macdsignal[i], macdhist[i] = ta.MACDEXT(
Close[i], fastperiod, fastmatype, slowperiod, slowmatype,
signalperiod, signalmatype)
return macd, macdsignal, macdhist
def MACDEXT(raw_df,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0):
# extract necessary data from raw dataframe (close)
# returns 3 things
macd, macdsignal, macdhist = ta.MACDEXT(raw_df.Close.values, fastperiod,
fastmatype, slowperiod, slowmatype,
signalperiod, signalmatype)
singleMerged = np.stack((macd, macdsignal, macdhist), axis=-1)
return singleMerged.tolist()
def get_stocks_weekly_macd():
result_list = []
result_dict = {}
df_stocks = get_stockbasket('', '')
for stockcode in df_stocks['ts_code']:
#获取前复权数据，默认为日期降序
df_qfq = ts.pro_bar(ts_code=stockcode, freq='W', adj='qfq')
if (df_qfq is None or len(df_qfq) < 2):
continue
else:
#按照日期升序df
df_qfq_asc = df_qfq.sort_values(by="trade_date", ascending=True)
#重新设置索引
df_qfq_asc.reset_index(drop=True, inplace=True)
#初始化CDMA df
df = pd.DataFrame()
df['DIF'], df['DEA'], df['MACD'] = talib.MACDEXT(
df_qfq_asc['close'],
fastperiod=12,
fastmatype=1,
slowperiod=26,
slowmatype=1,
signalperiod=9,
signalmatype=1)
df['MACD'] = df['MACD'] * 2
#按照index反序
df = df.sort_index(ascending=False)
#重新设置索引
df.reset_index(drop=True, inplace=True)
#赋值series给df_qfq
df_qfq['DIF'] = df['DIF']
df_qfq['DEA'] = df['DEA']
df_qfq['MACD'] = df['MACD']
DIF_1 = round(df_qfq['DIF'][1], 4)
DEA_1 = round(df_qfq['DEA'][1], 4)
DIF_0 = round(df_qfq['DIF'][0], 4)
DEA_0 = round(df_qfq['DEA'][0], 4)
#MACD_0 = round(df_qfq['MACD'][0],4)
#MACD_1 = round(df_qfq['MACD'][1],4)
#print (df_qfq['trade_date'][0],df_qfq['close'][0],DIF_0,DEA_0,MACD_0)
if (DIF_1 < DEA_1 and DIF_0 > DEA_0):
print(stockcode, df_qfq['trade_date'][0], df_qfq['close'][0],
DIF_1, DEA_1, DIF_0, DEA_0)
result_list.append(stockcode)
result_dict['ts_code'] = stockcode
result_dict['trade_date'] = df_qfq['trade_date'][0]
result_dict['close'] = df_qfq['close'][0]
result_dict['reason'] = str(sys._getframe().f_code.co_name)
print(len(result_list))
def get_macd(df, fast, slow, signal):
fast_d, slow_d, macd_d = talib.MACDEXT(df[config.close_key].values, fastperiod=fast, slowperiod=slow,
signalperiod=signal, fastmatype=1, slowmatype=1,
signalmatype=1)
macd_d *= 2
df['fast'], df['slow'], df['macd'] = fast_d, slow_d, macd_d
# df.dropna(how='any')
df[config.macd_positon_key] = ''
macd_jincha = df.macd > 0
df.loc[macd_jincha[np.logical_and(macd_jincha == True, macd_jincha.shift() == False)].index, config.macd_positon_key] = config.jincha
df.loc[macd_jincha[np.logical_and(macd_jincha == False, macd_jincha.shift() == True)].index, config.macd_positon_key] = config.sicha
return df
def MACDEXT(self, fastperiod=12, fastmatype=0, slowperiod=26, slowmatype=0, signalperiod=9, signalmatype=0):
real_data = np.array([self.df.close], dtype='f8')
# macd, macdsignal, macdhist = talib.MACDEXT(real_data[0], fastperiod=fastperiod, fastmatype=fastmatype,
# slowperiod=slowperiod,
# slowmatype=slowmatype, signalperiod=signalperiod,
# signalmatype=signalmatype)
# return go.Scatter(
# x=self.df.index,
# y=macd,
# name='MACDEXT'
# )
return talib.MACDEXT(real_data[0], fastperiod=fastperiod, fastmatype=fastmatype,
slowperiod=slowperiod,
slowmatype=slowmatype, signalperiod=signalperiod,
signalmatype=signalmatype)
def macd(security, fastperiod=12, slowperiod=26, signalperiod=9):
closes = attribute_history(security, slowperiod * 5, '1d',
('close'))['close'].values
# 增加当日数据去计算
cur_close = attribute_history(security, 1, '1m', ('close'))['close'].values
closes = numpy.concatenate((closes, cur_close))
#log.info('prices:', close)
macdDIFF, macdDEA, macd = talib.MACDEXT(closes,
fastperiod=fastperiod,
fastmatype=1,
slowperiod=slowperiod,
slowmatype=1,
signalperiod=signalperiod,
signalmatype=1)
macd = macd * 2
#log.info("%s macd ==> DIFF=%s, DEA=%s, macd=%s", security, macdDIFF[-1], macdDEA[-1], macd[-1])
return macdDIFF, macdDEA, macd
def MACDEXT(close,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0):
''' MACD with controllable MA type
分组: Momentum Indicator 动量指标
简介:
macd, macdsignal, macdhist = MACDEXT(close, fastperiod=12, fastmatype=0, slowperiod=26, slowmatype=0, signalperiod=9, signalmatype=0)
'''
return talib.MACDEXT(close, fastperiod, fastmatype, slowperiod, slowmatype,
signalperiod, signalmatype)
def stc(candles: np.ndarray,
fast_period: int = 23,
fast_matype: int = 1,
slow_period: int = 50,
slow_matype: int = 1,
k_period: int = 10,
d_period: int = 3,
source_type: str = "close",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
STC - Schaff Trend Cycle (Oscillator)
:param candles: np.ndarray
:param fast_period: int - default: 23
:param fastmatype: int - default: 1
:param slow_period: int - default: 50
:param slowmatype: int - default: 1
:param k_period: int - default: 10
:param d_period: int - default: 3
:param source_type: str - default: "close"
:param sequential: bool - default=False
:return: float | np.ndarray
"""
candles = slice_candles(candles, sequential)
source = get_candle_source(candles, source_type=source_type)
macd, macdsignal, macdhist = talib.MACDEXT(source,
fastperiod=fast_period,
fastmatype=fast_matype,
slowperiod=slow_period,
slowmatype=slow_matype)
stok = (macd - talib.MIN(macd, k_period)) / (
talib.MAX(macd, k_period) - talib.MIN(macd, k_period)) * 100
d = talib.EMA(stok, d_period)
kd = (d - talib.MIN(d, k_period)) / (talib.MAX(d, k_period) -
talib.MIN(d, k_period)) * 100
res = talib.EMA(kd, d_period)
return res if sequential else res[-1]
def get_macdext(df):
df['DIF'], df['DEA'], df['MACD'] = talib.MACDEXT(df['close'],
fastperiod=12,
fastmatype=1,
slowperiod=26,
slowmatype=1,
signalperiod=9,
signalmatype=1)
df['MACD'] = df['MACD'] * 2
df['MACD'].dropna(inplace=True)
df['MACD'] = df['MACD'].reset_index(drop=True)
df['DIF'].dropna(inplace=True)
df['DIF'] = df['DIF'].reset_index(drop=True)
df['DEA'].dropna(inplace=True)
df['DEA'] = df['DEA'].reset_index(drop=True)
#交易日降序排列
df = df.sort_values(by="trade_date", ascending=False)
return df
