def add_indicators(df):
high = df["HA_High"].values
close = df["HA_Close"].values
low = df["HA_Low"].values
_open = df["HA_Open"].values
volume = df["volume"].values.astype('uint32')
df["APO"] = talib.APO(close, fastperiod=9, slowperiod=21, matype=0)
df["APO"] = talib.APO(close, fastperiod=9, slowperiod=21, matype=0)
df["aroondown"], df["aroonup"] = talib.AROON(high, low, timeperiod=14)
df["BOP"] = talib.BOP(_open, high, low, close)
df["CCI"] = talib.CCI(high, low, close, timeperiod=10)
df["DX"] = talib.DX(high, low, close, timeperiod=10)
df["MOM"] = talib.MOM(close, timeperiod=10)
df["slowk"], df["slowd"] = talib.STOCH(high,
low,
close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
df["OBV"] = talib.OBV(close, np.asarray(volume, dtype='float'))
df["ADOSC"] = talib.ADOSC(high,
low,
close,
np.asarray(volume, dtype='float'),
fastperiod=3,
slowperiod=10)
df["upperband"], df["middleband"], df["lowerband"] = talib.BBANDS(
close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)
return df
def trade(context, data):
for stock in context.stocks1:
if data.can_trade(stock) and not data.is_stale(stock):
prices = data.history(stock, 'price', 50, '1d')
try:
apo = talib.APO(prices, fastperiod=12, slowperiod=26, matype=context.matype)[-1]
except:
context.stocks1.remove(stock)
log.info(stock)
continue
position = context.portfolio.positions[stock].amount
if apo > 0 and position == 0:
order_target_percent(stock, percent)
elif apo < 0 and position > 0:
order_target_percent(stock, 0)
for stock in context.stocks2:
if data.can_trade(stock) and not data.is_stale(stock):
prices = data.history(stock, 'price', 50, '1d')
try:
apo = talib.APO(prices, fastperiod=12, slowperiod=26, matype=context.matype)[-1]
except:
context.stocks2.remove(stock)
log.info(stock)
continue
position = context.portfolio.positions[stock].amount
if apo > 0 and position == 0:
order_target_percent(stock, percent)
elif apo < 0 and position > 0:
order_target_percent(stock, 0)
for stock in context.stocks3:
if data.can_trade(stock) and not data.is_stale(stock):
prices = data.history(stock, 'price', 50, '1d')
try:
apo = talib.APO(prices, fastperiod=12, slowperiod=26, matype=context.matype)[-1]
except:
context.stocks3.remove(stock)
log.info(stock)
continue
position = context.portfolio.positions[stock].amount
if apo > 0 and position == 0:
order_target_percent(stock, percent)
elif apo < 0 and position > 0:
order_target_percent(stock, 0)
record(leverage=context.account.leverage)
def getMomentumIndicators(df):
high = df['High']
low = df['Low']
close = df['Close']
open = df['Open']
volume = df['Volume']
df['ADX'] = ta.ADX(high, low, close, timeperiod=14)
df['SMA'] = ta.ADXR(high, low, close, timeperiod=14)
df['APO'] = ta.APO(close, fastperiod=12, slowperiod=26, matype=0)
df['AROONDOWN'], df['AROOONUP'] = ta.AROON(high, low, timeperiod=14)
df['AROONOSC'] = ta.AROONOSC(high, low, timeperiod=14)
df['BOP'] = ta.BOP(open, high, low, close)
df['CCI'] = ta.CCI(high, low, close, timeperiod=14)
df['CMO'] = ta.CMO(close, timeperiod=14)
df['DX'] = ta.DX(high, low, close, timeperiod=14)
df['MACD'], df['MACDSIGNAL'], df['MACDHIST'] = ta.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
df['MFI'] = ta.MFI(high, low, close, volume, timeperiod=14)
df['MINUS_DI'] = ta.MINUS_DI(high, low, close, timeperiod=14)
df['MINUS_DM']= ta.MINUS_DM(high, low, timeperiod=14)
df['MOM'] = ta.MOM(close, timeperiod=10)
df['PLUS_DM'] =ta.PLUS_DM(high, low, timeperiod=14)
df['PPO'] = ta.PPO(close, fastperiod=12, slowperiod=26, matype=0)
df['ROC'] = ta.ROC(close, timeperiod=10)
df['ROCP'] = ta.ROCP(close, timeperiod=10)
df['ROCR'] = ta.ROCR(close, timeperiod=10)
df['ROCR100'] = ta.ROCR100(close, timeperiod=10)
df['RSI'] = ta.RSI(close, timeperiod=14)
df['SLOWK'], df['SLOWD'] = ta.STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
df['FASTK'], df['FASTD'] = ta.STOCHF(high, low, close, fastk_period=5, fastd_period=3, fastd_matype=0)
df['FASTK2'], df['FASTD2'] = ta.STOCHRSI(close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0)
df['TRIX'] = ta.TRIX(close, timeperiod=30)
df['ULTOSC'] = ta.ULTOSC(high, low, close, timeperiod1=7, timeperiod2=14, timeperiod3=28)
df['WILLR'] = ta.WILLR(high, low, close, timeperiod=14)
def test_apo(self):
"""
Test Absolute Price Oscillator (APO).
"""
q = qufilab.apo(self.close, 200, 100, "sma")
t = talib.APO(self.close, 200, 100, 0)
np.testing.assert_allclose(q, t, rtol=self.tolerance)
def extract_features(data):
high = data['High']
low = data['Low']
close = data['Close']
volume = data['Volume']
open_ = data['Open']
data['ADX'] = ta.ADX(high, low, close, timeperiod=19)
data['CCI'] = ta.CCI(high, low, close, timeperiod=19)
data['CMO'] = ta.CMO(close, timeperiod=14)
data['MACD'], X, Y = ta.MACD(close, fastperiod=10, slowperiod=30, signalperiod=9)
data['MFI'] = ta.MFI(high, low, close, volume, timeperiod=19)
data['MOM'] = ta.MOM(close, timeperiod=9)
data['ROCR'] = ta.ROCR(close, timeperiod=12)
data['RSI'] = ta.RSI(close, timeperiod=19)
data['STOCHSLOWK'], data['STOCHSLOWD'] = ta.STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
data['TRIX'] = ta.TRIX(close, timeperiod=30)
data['WILLR'] = ta.WILLR(high, low, close, timeperiod=14)
data['OBV'] = ta.OBV(close, volume)
data['TSF'] = ta.TSF(close, timeperiod=14)
data['NATR'] = ta.NATR(high, low, close)#, timeperiod=14)
data['ULTOSC'] = ta.ULTOSC(high, low, close)
data['AROONOSC'] = ta.AROONOSC(high, low, timeperiod=14)
data['BOP'] = ta.BOP(open_, high, low, close)
data['LINEARREG'] = ta.LINEARREG(close)
data['AP0'] = ta.APO(close, fastperiod=9, slowperiod=23, matype=1)
data['TEMA'] = ta.TRIMA(close, 29)
return data
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 apo(candles: np.ndarray,
fastperiod=12,
slowperiod=26,
matype=0,
sequential=False) -> Union[float, np.ndarray]:
"""
APO - Absolute Price Oscillator
:param candles: np.ndarray
:param fastperiod: int - default: 12
:param slowperiod: int - default: 26
:param matype: int - default: 0
:param sequential: bool - default=False
:return: float | np.ndarray
"""
if not sequential and len(candles) > 240:
candles = candles[-240:]
res = talib.APO(candles[:, 2],
fastperiod=fastperiod,
slowperiod=slowperiod,
matype=matype)
return res if sequential else res[-1]
def apo(candles: np.ndarray,
fast_period: int = 12,
slow_period: int = 26,
matype: int = 0,
source_type: str = "close",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
APO - Absolute Price Oscillator
:param candles: np.ndarray
:param fast_period: int - default: 12
:param slow_period: int - default: 26
:param matype: int - default: 0
: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)
res = talib.APO(source,
fastperiod=fast_period,
slowperiod=slow_period,
matype=matype)
return res if sequential else res[-1]
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 generate_feature(data):
high = data.High.values
low = data.Low.values
close = data.Close.values
feature_df = pd.DataFrame(index=data.index)
feature_df["ADX"] = ADX = talib.ADX(high, low, close, timeperiod=14)
feature_df["ADXR"] = ADXR = talib.ADXR(high, low, close, timeperiod=14)
feature_df["APO"] = APO = talib.APO(close, fastperiod=12, slowperiod=26, matype=0)
feature_df["AROONOSC"] = AROONOSC = talib.AROONOSC(high, low, timeperiod=14)
feature_df["CCI"] = CCI = talib.CCI(high, low, close, timeperiod=14)
feature_df["CMO"] = CMO = talib.CMO(close, timeperiod=14)
feature_df["DX"] = DX = talib.DX(high, low, close, timeperiod=14)
feature_df["MINUS_DI"] = MINUS_DI = talib.MINUS_DI(high, low, close, timeperiod=14)
feature_df["MINUS_DM"] = MINUS_DM = talib.MINUS_DM(high, low, timeperiod=14)
feature_df["MOM"] = MOM = talib.MOM(close, timeperiod=10)
feature_df["PLUS_DI"] = PLUS_DI = talib.PLUS_DI(high, low, close, timeperiod=14)
feature_df["PLUS_DM"] = PLUS_DM = talib.PLUS_DM(high, low, timeperiod=14)
feature_df["PPO"] = PPO = talib.PPO(close, fastperiod=12, slowperiod=26, matype=0)
feature_df["ROC"] = ROC = talib.ROC(close, timeperiod=10)
feature_df["ROCP"] = ROCP = talib.ROCP(close, timeperiod=10)
feature_df["ROCR100"] = ROCR100 = talib.ROCR100(close, timeperiod=10)
feature_df["RSI"] = RSI = talib.RSI(close, timeperiod=14)
feature_df["ULTOSC"] = ULTOSC = talib.ULTOSC(high, low, close, timeperiod1=7, timeperiod2=14, timeperiod3=28)
feature_df["WILLR"] = WILLR = talib.WILLR(high, low, close, timeperiod=14)
feature_df = feature_df.fillna(0.0)
matrix = np.stack((
ADX, ADXR, APO, AROONOSC, CCI, CMO, DX, MINUS_DI, ROCR100, ROC,
MINUS_DM, MOM, PLUS_DI, PLUS_DM, PPO, ROCP, WILLR, ULTOSC, RSI))
matrix = np.nan_to_num(matrix)
matrix = matrix.transpose()
return feature_df, matrix
def apo(close_ts, fastperiod=12, slowperiod=26, matype=0):
import talib
close_np = close_ts.cpu().detach().numpy()
close_df = pd.DataFrame(close_np)
apo = close_df.apply(lambda x: talib.APO(x, fastperiod=12, slowperiod=26, matype=0))
apo_ts = torch.tensor(apo.values, dtype=close_ts.dtype, device=close_ts.device)
return apo_ts
def apo(self, n, array=False):
"""
APO.
"""
result = talib.APO(self.close, n)
if array:
return result
return result[-1]
def apo(self, n: int, array: bool = False) -> Union[float, np.ndarray]:
"""
APO.
"""
result = talib.APO(self.close, n)
if array:
return result
return result[-1]
def APO(close, fastperiod=12, slowperiod=26, matype=0):
# Absolute Price Oscillator
param = {
'fastperiod': fastperiod,
'slowperiod': slowperiod,
'matype': matype,
}
return talib.APO(close, **param)
def apo(df, n=5, m=10, normalize=False, col='Close'):
c = df[col].as_matrix()
_apo = pd.Series(ta.APO(c, fastperiod=n, slowperiod=m),
index=df.index,
name="APO_" + str(n) + "_" + str(m))
if normalize:
_apo = z_score(_apo)
return _apo
def APO(self, fastPeriod=12, slowPeriod=26, matype=0):
real_data = np.array([self.df.close], dtype='f8')
apo = talib.APO(real_data[0], fastperiod=fastPeriod, slowperiod=slowPeriod, matype=matype)
# return go.Scatter(
# x=self.df.index,
# y=apo,
# name='APO'
# )
return apo
def applyAPO(nsedailyquoteDF):
apodf = pd.Series(talib.APO(nsedailyquoteDF['CLOSE'].values,
fastperiod=12,
slowperiod=26,
matype=0),
index=nsedailyquoteDF.TIMESTAMP,
name='APO')
return apodf
def NMA_factor(self, df, fastperiod=12, slowperiod=26, matype=0):
"""
the custom function that compute normalized apo
the equation is (fast ma - slow ma)/slowma
"""
numerator = talib.APO(df.loc[:, self.map_dict['default']].values,
fastperiod, slowperiod, matype)
denominator = talib.MA(df.loc[:, self.map_dict['default']].values,
slowperiod, matype)
return numerator / denominator
def APO(close, fastperiod=12, slowperiod=26, matype=0):
''' Absolute Price Oscillator
分组: Momentum Indicator 动量指标
简介:
real = APO(close, fastperiod=12, slowperiod=26, matype=0)
'''
return talib.APO(close, fastperiod, slowperiod, matype)
def apotrade(context, data):
for stock in context.stocks:
prices = data.history(stock, 'price', 50, '1d')
apo = talib.APO(prices, fastperiod=12, slowperiod=26, matype=0)[-1]
position = context.portfolio.positions[stock].amount
if apo > 0 and position == 0:
order_target_percent(stock, pct)
elif apo < 0 and position > 0:
order_target_percent(stock, 0)
def generate_feature(data):
high = data.High.values
low = data.Low.values
close = data.Close.values
# feature_df = pd.DataFrame(index=data.index)
feature_df = data.copy()
feature_df["ADX"] = ADX = talib.ADX(high, low, close, timeperiod=14)
feature_df["ADXR"] = ADXR = talib.ADXR(high, low, close, timeperiod=14)
feature_df["APO"] = APO = talib.APO(close,
fastperiod=12,
slowperiod=26,
matype=0)
feature_df["AROONOSC"] = AROONOSC = talib.AROONOSC(high,
low,
timeperiod=14)
feature_df["CCI"] = CCI = talib.CCI(high, low, close, timeperiod=14)
feature_df["CMO"] = CMO = talib.CMO(close, timeperiod=14)
feature_df["DX"] = DX = talib.DX(high, low, close, timeperiod=14)
feature_df["MINUS_DI"] = MINUS_DI = talib.MINUS_DI(high,
low,
close,
timeperiod=14)
feature_df["MINUS_DM"] = MINUS_DM = talib.MINUS_DM(high,
low,
timeperiod=14)
feature_df["MOM"] = MOM = talib.MOM(close, timeperiod=10)
feature_df["PLUS_DI"] = PLUS_DI = talib.PLUS_DI(high,
low,
close,
timeperiod=14)
feature_df["PLUS_DM"] = PLUS_DM = talib.PLUS_DM(high, low, timeperiod=14)
feature_df["PPO"] = PPO = talib.PPO(close,
fastperiod=12,
slowperiod=26,
matype=0)
feature_df["ROC"] = ROC = talib.ROC(close, timeperiod=10)
feature_df["ROCP"] = ROCP = talib.ROCP(close, timeperiod=10)
feature_df["ROCR100"] = ROCR100 = talib.ROCR100(close, timeperiod=10)
feature_df["RSI"] = RSI = talib.RSI(close, timeperiod=14)
feature_df["ULTOSC"] = ULTOSC = talib.ULTOSC(high,
low,
close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
feature_df["WILLR"] = WILLR = talib.WILLR(high, low, close, timeperiod=14)
feature_df = feature_df.fillna(0.0)
# Exclude columns you don't want
feature_df = feature_df[feature_df.columns[
~feature_df.columns.isin(['Open', 'High', 'Low', 'Close'])]]
matrix = feature_df.values
return feature_df, matrix
def _get_indicators(security, open_name, close_name, high_name, low_name,
volume_name):
"""
expand the features of the data through technical analysis across 26 different signals
:param security: data which features are going to be expanded
:param open_name: open price column name
:param close_name: close price column name
:param high_name: high price column name
:param low_name: low price column name
:param volume_name: traded volumn column name
:return: expanded and extracted data
"""
open_price = security[open_name].values
close_price = security[close_name].values
low_price = security[low_name].values
high_price = security[high_name].values
volume = security[volume_name].values if volume_name else None
security['MOM'] = talib.MOM(close_price)
security['HT_DCPERIOD'] = talib.HT_DCPERIOD(close_price)
security['HT_DCPHASE'] = talib.HT_DCPHASE(close_price)
security['SINE'], security['LEADSINE'] = talib.HT_SINE(close_price)
security['INPHASE'], security['QUADRATURE'] = talib.HT_PHASOR(
close_price)
security['ADXR'] = talib.ADXR(high_price, low_price, close_price)
security['APO'] = talib.APO(close_price)
security['AROON_UP'], _ = talib.AROON(high_price, low_price)
security['CCI'] = talib.CCI(high_price, low_price, close_price)
security['PLUS_DI'] = talib.PLUS_DI(high_price, low_price, close_price)
security['PPO'] = talib.PPO(close_price)
security['MACD'], security['MACD_SIG'], security[
'MACD_HIST'] = talib.MACD(close_price)
security['CMO'] = talib.CMO(close_price)
security['ROCP'] = talib.ROCP(close_price)
security['FASTK'], security['FASTD'] = talib.STOCHF(
high_price, low_price, close_price)
security['TRIX'] = talib.TRIX(close_price)
security['ULTOSC'] = talib.ULTOSC(high_price, low_price, close_price)
security['WILLR'] = talib.WILLR(high_price, low_price, close_price)
security['NATR'] = talib.NATR(high_price, low_price, close_price)
security['RSI'] = talib.RSI(close_price)
security['EMA'] = talib.EMA(close_price)
security['SAREXT'] = talib.SAREXT(high_price, low_price)
# security['TEMA'] = talib.EMA(close_price)
security['RR'] = security[close_name] / security[close_name].shift(
1).fillna(1)
security['LOG_RR'] = np.log(security['RR'])
if volume_name:
security['MFI'] = talib.MFI(high_price, low_price, close_price,
volume)
# security['AD'] = talib.AD(high_price, low_price, close_price, volume)
# security['OBV'] = talib.OBV(close_price, volume)
security[volume_name] = np.log(security[volume_name])
security.drop([open_name, close_name, high_name, low_name], axis=1)
security = security.dropna().astype(np.float32)
return security
def apo(self,
fast_period: int,
slow_period: int,
matype: int = 0,
array: bool = False) -> Union[float, np.ndarray]:
"""
APO.
"""
result = talib.APO(self.close, fast_period, slow_period, matype)
if array:
return result
return result[-1]
def get_momentum_studies(open, low, high, close, volume, df):
# Momentum studies
# https://mrjbq7.github.io/ta-lib/func_groups/momentum_indicators.html
df['MACD'], df['MACD_SIGN'], df['MACD_HIST'] = talib.MACD(
close, fastperiod=12, slowperiod=26, signalperiod=9)
df['STOCH-SLOW-K'], df['STOCH-SLOW-D'] = talib.STOCH(high,
low,
close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
df['STOCH-FAST-K'], df['STOCH-FAST-D'] = talib.STOCHF(high,
low,
close,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
df['STOCH-RSI-K'], df['STOCH-RSI-D'] = talib.STOCHRSI(close,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
df['AROON-DOWN'], df['AROON-UP'] = talib.AROON(high,
low,
timeperiod=14)
df["MINUS_DI"] = talib.MINUS_DI(high, low, close, timeperiod=14)
df["MINUS_DM"] = talib.MINUS_DM(high, low, timeperiod=14)
df["PLUS_DI"] = talib.PLUS_DI(high, low, close, timeperiod=14)
df["PLUS_DM"] = talib.PLUS_DM(high, low, timeperiod=14)
df["MOM"] = talib.MOM(close, timeperiod=10)
df["MFI"] = talib.MFI(high, low, close, volume, timeperiod=14)
df["ADX"] = talib.ADX(high, low, close, timeperiod=14)
df["ADXR"] = talib.ADXR(high, low, close, timeperiod=14)
df["APO"] = talib.APO(close, fastperiod=12, slowperiod=26, matype=0)
df["AROONOSC"] = talib.AROONOSC(high, low, timeperiod=14)
df["BOP"] = talib.BOP(open, high, low, close)
df["CCI"] = talib.CCI(high, low, close, timeperiod=14)
df["CMO"] = talib.CMO(close, timeperiod=14)
df["DX"] = talib.DX(high, low, close, timeperiod=14)
df["PPO"] = talib.PPO(close, fastperiod=12, slowperiod=26, matype=0)
df["ROC"] = talib.ROC(close, timeperiod=10)
df["RSI"] = talib.RSI(close, timeperiod=14)
df["TRIX"] = talib.TRIX(close, timeperiod=30)
df["ULT"] = talib.ULTOSC(high,
low,
close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
df["WILLR"] = talib.WILLR(high, low, close, timeperiod=14)
def test_apo(self):
result = pandas_ta.apo(self.close)
self.assertIsInstance(result, Series)
self.assertEqual(result.name, 'APO_12_26')
try:
expected = tal.APO(self.close, 12, 26)
pdt.assert_series_equal(result, expected, check_names=False)
except AssertionError as ae:
try:
corr = pandas_ta.utils.df_error_analysis(result, expected, col=CORRELATION)
self.assertGreater(corr, CORRELATION_THRESHOLD)
except Exception as ex:
error_analysis(result, CORRELATION, ex)
def generate_tech_data_default(stock,
open_name,
close_name,
high_name,
low_name,
volume_name='vol'):
open_price = stock[open_name].values
close_price = stock[close_name].values
low_price = stock[low_name].values
high_price = stock[high_name].values
volume = stock[volume_name].values
data = stock.copy()
data['MOM'] = talib.MOM(close_price)
data['HT_DCPERIOD'] = talib.HT_DCPERIOD(close_price)
data['HT_DCPHASE'] = talib.HT_DCPHASE(close_price)
data['sine'], data['leadsine'] = talib.HT_SINE(close_price)
data['inphase'], data['quadrature'] = talib.HT_PHASOR(close_price)
data['ADXR'] = talib.ADXR(high_price, low_price, close_price)
data['APO'] = talib.APO(close_price)
data['AROON_UP'], _ = talib.AROON(high_price, low_price)
data['CCI'] = talib.CCI(high_price, low_price, close_price)
data['PLUS_DI'] = talib.PLUS_DI(high_price, low_price, close_price)
data['PPO'] = talib.PPO(close_price)
data['macd'], data['macd_sig'], data['macd_hist'] = talib.MACD(close_price)
data['CMO'] = talib.CMO(close_price)
data['ROCP'] = talib.ROCP(close_price)
data['fastk'], data['fastd'] = talib.STOCHF(high_price, low_price,
close_price)
data['TRIX'] = talib.TRIX(close_price)
data['ULTOSC'] = talib.ULTOSC(high_price, low_price, close_price)
data['WILLR'] = talib.WILLR(high_price, low_price, close_price)
data['NATR'] = talib.NATR(high_price, low_price, close_price)
data['MFI'] = talib.MFI(high_price, low_price, close_price, volume)
data['RSI'] = talib.RSI(close_price)
data['AD'] = talib.AD(high_price, low_price, close_price, volume)
data['OBV'] = talib.OBV(close_price, volume)
data['EMA'] = talib.EMA(close_price)
data['SAREXT'] = talib.SAREXT(high_price, low_price)
data['TEMA'] = talib.EMA(close_price)
#data = data.drop([open_name, close_name, high_name, low_name, volume_name, 'amount', 'count'], axis=1)
data = drop_columns(data, [
open_name, close_name, high_name, low_name, volume_name, 'amount',
'count'
])
data = data.dropna().astype(np.float32)
return data
def get_additional_factors(open, high, low, close, volume):
# Overlap Studies Functions
mat = get_all_factors(open, high, low, close, volume)
mat = np.column_stack((mat, talib.HT_TRENDLINE(close))) ## close
mat = np.column_stack((mat, talib.KAMA(close, timeperiod=30))) ##close
#Momentum Indicator Functions
mat = np.column_stack((mat, talib.ADX(high, low, close, timeperiod=14)))
mat = np.column_stack((mat, talib.ADXR(high, low, close, timeperiod=14)))
mat = np.column_stack(
(mat, talib.APO(close, fastperiod=12, slowperiod=26, matype=0)))
mat = np.column_stack((mat, talib.AROONOSC(high, low, timeperiod=14)))
mat = np.column_stack((mat, talib.BOP(open, high, low, close)))
mat = np.column_stack((mat, talib.MOM(close, timeperiod=10)))
#Volume Indicator Functions
mat = np.column_stack((mat, talib.AD(high, low, close, volume)))
mat = np.column_stack(
(mat, talib.ADOSC(high,
low,
close,
volume,
fastperiod=3,
slowperiod=10)))
mat = np.column_stack((mat, talib.OBV(close, volume)))
#Volatility Indicator Functions
mat = np.column_stack((mat, talib.NATR(high, low, close, timeperiod=14)))
mat = np.column_stack((mat, talib.TRANGE(high, low, close)))
#Price Transform Functions
mat = np.column_stack((mat, talib.AVGPRICE(open, high, low, close)))
mat = np.column_stack((mat, talib.MEDPRICE(high, low)))
mat = np.column_stack((mat, talib.TYPPRICE(high, low, close)))
mat = np.column_stack((mat, talib.WCLPRICE(high, low, close)))
#Cycle Indicator Functions
mat = np.column_stack((mat, talib.HT_DCPERIOD(close)))
mat = np.column_stack((mat, talib.HT_DCPHASE(close)))
mat = np.column_stack((mat, talib.HT_TRENDMODE(close)))
# 20
return mat
def generateIndicatorAPO(dictDataSpec):
"""
https://www.investopedia.com/articles/technical/02/041002.asp
"""
df = dictDataSpec['df']
sReal = pd.Series(talib.APO(df.Close.values, 12, 26), index=df.index)
sIndicator = sReal.apply(lambda x: np.nan)
print sReal.describe()
ADXThresholdUpper = 50
ADXThresholdLower = 50
NDayHist = 60
NDayHistSL = 20
sIndicator[(sReal > ADXThresholdUpper)&(sReal.shift(1) < ADXThresholdUpper)&(df['Close'] / df['Close'].rolling(NDayHist).min() > 1.1)] = 1
sIndicator[(sReal < ADXThresholdLower)&(sReal.shift(1) > ADXThresholdLower)] = 0
sIndicator[(df['Close']<df['Close'].shift(1).rolling(NDayHistSL).quantile(0.1))] = -1
return sIndicator
def init_state(indata, test=False):
openn = indata['open'].values
close = indata['close'].values
high = indata['high'].values
low = indata['low'].values
volume = indata['volume'].values
diff = np.diff(close)
diff = np.insert(diff, 0, 0)
sma30 = talib.SMA(close, 30)
sma60 = talib.SMA(close, timeperiod=60)
rsi = talib.RSI(close, timeperiod=14)
atr = talib.ATR(high, low, close, timeperiod=14)
trange = talib.TRANGE(high, low, close)
macd, macdsignal, macdhist = talib.MACD(close, 12, 26, 9)
upper, middle, lower = talib.BBANDS(close, 20, 2, 2)
ema = talib.EMA(close, 30)
ma = talib.MA(close, 30)
wma = talib.WMA(close, timeperiod=30)
tema = talib.TEMA(close, 30)
obv = talib.OBV(close, np.asarray(volume, dtype='float'))
adx = talib.ADX(high, low, close, 14)
apo = talib.APO(close, 12, 2, 0)
bop = talib.BOP(openn, high, low, close)
mom = talib.MOM(close,10)
ppo = talib.PPO(close, 12, 26, 0)
slowk, slowd = talib.STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
ad = talib.AD(high, low, close, np.asarray(volume, dtype='float'))
wcl = talib.WCLPRICE(high, low, close)
#--- Preprocess data
xdata = np.column_stack((close, diff, sma30, sma60, rsi, atr, macd, macdsignal, macdhist, lower, middle, upper, ema, ma, wma, adx, apo, bop, mom, ppo, slowk, slowd, trange, wcl))
xdata = np.nan_to_num(xdata)
if test == False:
scaler = preprocessing.StandardScaler()
xdata = np.expand_dims(scaler.fit_transform(xdata), axis=1)
joblib.dump(scaler, 'data/scaler.pkl')
elif test == True:
scaler = joblib.load('data/scaler.pkl')
xdata = np.expand_dims(scaler.fit_transform(xdata), axis=1)
state = xdata[0:1, 0:1, :]
return state, xdata, close
def get_technicals_of_series(self, indivations):
result = indivations
result = np.vstack((result, ta.SMA(indivations, timeperiod=5)))
result = np.vstack((result, ta.SMA(indivations, timeperiod=14)))
result = np.vstack((result, ta.BBANDS(indivations)))
result = np.vstack((result, ta.MAMA(indivations)))
result = np.vstack((result, ta.APO(indivations)))
result = np.vstack((result, ta.CMO(indivations)))
result = np.vstack((result, ta.MACD(indivations)))
result = np.vstack((result, ta.MOM(indivations)))
result = np.vstack((result, ta.ROC(indivations)))
result = np.vstack((result, ta.RSI(indivations)))
result = np.vstack((result, ta.HT_TRENDMODE(indivations)))
result = np.vstack((result, ta.LINEARREG(indivations)))
result = result[:, ~np.isnan(result).any(axis=0)]
result = result.T
print(np.shape(result))
return result