def _compute_states(self):
start = self.current_idx - self.lookback
end = self.current_idx
df = self._data.iloc[start:end, :]
states = {}
states['roc1'] = ta.ROC(df.close, timeperiod=5).iloc[-1]
states['roc2'] = ta.ROC(df.close, timeperiod=20).iloc[-1]
states['rsi1'] = ta.RSI(df.close, timeperiod=14).iloc[-1]
states['rsi2'] = ta.RSI(df.close, timeperiod=28).iloc[-1]
states['cci1'] = ta.CCI(df.high, df.low, df.close,
timeperiod=14).iloc[-1]
states['cci2'] = ta.CCI(df.high, df.low, df.close,
timeperiod=28).iloc[-1]
states['adx1'] = ta.ADX(df.high, df.low, df.close,
timeperiod=14).iloc[-1]
states['adx2'] = ta.ADX(df.high, df.low, df.close,
timeperiod=28).iloc[-1]
states['atr'] = ta.ATR(df.high, df.low, df.close,
timeperiod=14).iloc[-1]
states['aroon1'] = ta.AROONOSC(df.high, df.low, timeperiod=14).iloc[-1]
states['aroon2'] = ta.AROONOSC(df.high, df.low, timeperiod=28).iloc[-1]
states['bop'] = ta.BOP(df.open, df.high, df.low, df.close).iloc[-1]
states['cmo'] = ta.CMO(df.close, timeperiod=14).iloc[-1]
states['close'] = df.close.iloc[-1]
states['date'] = df.index[-1]
states['position'] = self.position
states['entry_price'] = self.entry_price
states['entry_date'] = self.entry_date
return states
def aroonosc(high, low, timeperiod=None):
"""
AROONOSC = Aroon Oscillator (Momentum Indicators)
:param high:
:param low:
:param timeperiod:
:return float:
"""
if timeperiod is None:
timeperiod = 14
# nejdříve vyřízneme potřebnou délku,
# ke které přidáme jedno místo navíc,
# protože výsledek je počítán z plné délky
# 'timepepriod' a hodnotu navrací až v první
# následující iteraci.
high = high[-(timeperiod + 1):]
low = low[-(timeperiod + 1):]
# Převedeme na desetinná čísla s dvojitou přesností.
high = [float(x) for x in high]
low = [float(x) for x in low]
# Převedeme na pole dat.
high = numpy.asarray(high)
low = numpy.asarray(low)
# Výsledek
result = talib.AROONOSC(high, low, timeperiod)
# Převedeme z datového pole na posloupnost.
overall = []
for i in result:
part = i.tolist()
overall.append(part)
# Z datového pole vyřízneme poslední výsledek
# a navracíme jako desetinné číslo.
cutedresult = float(overall[-1])
# Vrátí desetinné číslo.
return cutedresult
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 aroonosc(client,
symbol,
timeframe="6m",
highcol="high",
lowcol="low",
period=14):
"""This will return a dataframe of
Aroon Oscillator
for the given symbol across the given timeframe
Args:
client (pyEX.Client): Client
symbol (string): Ticker
timeframe (string): timeframe to use, for pyEX.chart
highcol (string): column to use to calculate
lowcol (string): column to use to calculate
period (int): period to calculate across
Returns:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
x = t.AROONOSC(df[highcol].values.astype(float),
df[lowcol].values.astype(float), period)
return pd.DataFrame({
highcol: df[highcol].values,
lowcol: df[lowcol].values,
"aroonosc": x
})
def calculate(data):
timestamp = data[len(data)-1][0]
nopen = []
nhigh = []
nlow = []
nclose = []
nvolume = []
for entry in data:
nopen.append(entry[1])
nhigh.append(entry[2])
nlow.append(entry[3])
nclose.append(entry[4])
nvolume.append(entry[5])
nc = np.array(nclose)
hc = np.array(nhigh)
lc = np.array(nlow)
cc = np.array(nclose)
vc = np.array(nvolume)
rsi = talib.RSI(nc, timeperiod=14)
macdsignal = talib.MACD(nc, fastperiod=12, slowperiod=26, signalperiod=9)
sar = talib.SAR(hc, lc, acceleration=0, maximum=0)
adosc = talib.ADOSC(hc, lc, cc, vc, fastperiod=3, slowperiod=10)
aroon = talib.AROONOSC(hc, lc, timeperiod=14)
ult = talib.ULTOSC(hc, lc, cc, timeperiod1=7, timeperiod2=14, timeperiod3=28)
obj, sent = twitter_api.get_sentiment()
database.insert_predictions(timestamp, rsi[-1], macdsignal[0][-1], sar[-1], adosc[-1], aroon[-1], ult[-1], sent, obj)
return timestamp, rsi[-1], macdsignal[0][-1], sar[-1], adosc[-1], aroon[-1], ult[-1], sent, obj
def compAROONOSC(self):
ac = talib.AROONOSC(self.high,self.low,timeperiod=self.lookback)
self.removeNullID(ac)
self.rawFeatures['AROONOSC'] = ac
FEATURE_SIZE_DICT['AROONOSC'] = 1
return
def _set_df(self, df):
for c in AssetColumns:
if str(c) not in df.columns:
raise RuntimeError(
f"{c} column doesn't exist in the dataframe")
df['datetime'] = pd.to_datetime(df['timestamp'], unit='s')
df['month'] = df['datetime'].dt.month
df['weekday'] = df['datetime'].dt.weekday
df['hour'] = df['datetime'].dt.hour
df = df.drop(['datetime', 'timestamp', 'index'], 1)
df['sma'] = talib.SMA(df['close'], self.window)
df['rsi'] = talib.RSI(df['close'], self.window)
df['mom'] = talib.MOM(df['close'], self.window)
df['bop'] = talib.BOP(df['open'], df['high'], df['low'], df['close'])
df['aroonosc'] = talib.AROONOSC(df['high'], df['low'], self.window)
self.min = df.min()
self.max = df.max()
self.df = df
self.row_count = len(self.df.index)
if self.row_count < EP_LEN:
raise RuntimeError(f"not enought rows")
# add balance, asset_held, net_worth at each timestamp
n_features = (len(df.columns) * self.window) + 3
self.observation_space = spaces.Box(-np.inf,
np.inf,
shape=((n_features, )),
dtype=np.float32)
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 get_features(data):
tech_data = pd.DataFrame(index=data.index);
for t in periods_list:
tech_data[f'SMA_{t}'] = talib.SMA(data.close,timeperiod=t)
tech_data[f'MOM_{t}'] = talib.MOM(data.close, timeperiod=t)
tech_data[f'RSI_{t}'] = talib.RSI(data.close, timeperiod=t)
tech_data[f'MA_{t}'] = talib.MA(data.close, timeperiod=t)
tech_data[f'DX_{t}'] = talib.DX(data.high, data.low, data.close, timeperiod=t)
tech_data[f'volume_change_{t}'] = data.volume.pct_change(periods=t)
tech_data[f'volatility_{t}'] = data.close.pct_change(periods=t).std()
tech_data[f'ADX_{t}'] = talib.ADX(data.high, data.low, data.close, timeperiod=t)
tech_data[f'ADXR_{t}'] = talib.ADXR(data.high, data.low, data.close, timeperiod=t)
tech_data[f'AROONOSC_{t}'] = talib.AROONOSC(data.high, data.low, timeperiod=t)
tech_data[f'ROC_{t}'] = talib.ROC(data.close, timeperiod=t)
tech_data[f'BIAS_{t}'] = (data['close'] - data['close'].rolling(t, min_periods=1).mean())/ data['close'].rolling(t, min_periods=1).mean()*100
tech_data[f'BOLL_upper_{t}'], tech_data[f'BOLL_middle_{t}'], tech_data[f'BOLL_lower_{t}'] = talib.BBANDS(
data.close,
timeperiod=t,
nbdevup=2,
nbdevdn=2,
matype=0)
tech_data['SAR'] = talib.SAR(data.high, data.low)
tech_data['AD'] = talib.AD(data.high, data.low, data.close, data.volume)
tech_data['OBV'] = talib.OBV(data.close, data.volume)
tech_data['target'] = data.close.pct_change().shift(-1).apply(lambda x: 1 if x > 0 else -1).fillna(0)
tech_data['time'] = data.time
tech_data = tech_data.set_index('time')
reduce(lambda x, y: cross_over(x, y, tech_data), periods_list)
features = list(set(tech_data.columns) - set(data.columns) - set(['target']))
return tech_data.dropna(), features
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 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 AROONOSC_talib(self, ndays):
"""
:param ndays:
:return:
"""
real = ta.AROONOSC(self.high, self.low, timeperiod=ndays)
return real
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 applyAROONOSC(nsedailyquoteDF):
arronoscdf = pd.Series(talib.AROONOSC(nsedailyquoteDF['HIGH'].values,
nsedailyquoteDF['LOW'].values,
timeperiod=14),
index=nsedailyquoteDF.TIMESTAMP,
name='AROONOSC')
return arronoscdf
def test_aroon(self):
"""
Test Aroon oscillator.
"""
periods = 200
q = qufilab.aroon(self.high, self.low, periods)
t = talib.AROONOSC(self.high, self.low, periods)
np.testing.assert_allclose(q, t, rtol=self.tolerance)
def getAROONOSC(period):
stocks = getStocks()
for count, ticker in enumerate(stocks):
df = data.get(ticker)
df['AROON'] = talib.AROONOSC(df['High'].values, df['Low'].values, period)
# You cannot concatenate a string with an int. You would need to convert your int to a string
aroonMap[ticker + "_" + str(period)] = df['AROON']
def extract_aroon(data):
period = long_period
data['aroon_down'], data['aroon_up'] = ta.AROON(data['high'].values,
data['low'].values,
timeperiod=period)
data['aroon_osc'] = ta.AROONOSC(data['high'].values,
data['low'].values,
timeperiod=period)
return data
def evaluate(self, kite_fetcher, instrument):
period = int(self.period)
df = self.get_small_data(kite_fetcher=kite_fetcher,
instrument=instrument)
result = talib.AROONOSC(high=df['high'],
low=df['low'].values,
timeperiod=period)
return np.round(result.iloc[-1], 2)
def aroonosc(self, n, array=False):
"""
Aroon Oscillator.
"""
result = talib.AROONOSC(self.high, self.low, n)
if array:
return result
return result[-1]
def AROONOSC(self, timeperiod=14):
real_data = np.array([self.df.high, self.df.low], dtype='f8')
aroonosc = talib.AROONOSC(real_data[0], real_data[1], timeperiod=timeperiod)
# return go.Scatter(
# x=self.df.index,
# y=aroonosc,
# name='AROONOSC'
# )
return aroonosc
def aroonosc(self, n: int, array: bool = False) -> Union[float, np.ndarray]:
"""
Aroon Oscillator.
"""
result = talib.AROONOSC(self.high, self.low, n)
if array:
return result
return result[-1]
def aroon(high, low, n):
try:
aroondown, aroonup = talib.AROON(high, low, timeperiod=n)
df = pd.DataFrame()
df['aroondown'] = aroondown
df['aroonup'] = aroonup
df['aroon_oscillator'] = talib.AROONOSC(high, low, timeperiod=n)
return df
except Exception as e:
raise (e)
def aroonosc(self, sym, frequency, *args, **kwargs):
if not self.kbars_ready(sym, frequency):
return []
highs = self.high(sym, frequency)
lows = self.low(sym, frequency)
v = ta.AROONOSC(highs, lows, *args, **kwargs)
return v
def AROONOSC(high, low, timeperiod=14):
''' Aroon Oscillator 阿隆振荡
分组: Momentum Indicator 动量指标
简介:
real = AROONOSC(high, low, timeperiod=14)
'''
return talib.AROONOSC(high, low, timeperiod=14)
def _assemble_state(self):
'''Here we can add other things such as indicators and times'''
self._get_last_N_timebars()
bars = [self.last5m, self.last1h, self.last1d]
state = []
candles = {
j: {k: np.array([])
for k in ['open', 'high', 'low', 'close']}
for j in range(len(bars))
}
for j, bar in enumerate(bars):
for col in ['open', 'high', 'low', 'close']:
candles[j][col] = np.asarray(bar[col])
state += (list(np.asarray(bar[col]))[-10:])
self.state = np.array([])
self.state = np.append(self.state, state)
self.state = np.append(self.state, self.position)
np.append(self.state, np.sign(self.pnl_sum))
self.state = np.append(self.state, self._time_of_day)
self.state = np.append(self.state, self._day_of_week)
for c in candles:
try:
sma1 = talib.SMA(candles[c]['close'], self.lkbk - 1)[-1]
sma2 = talib.SMA(candles[c]['close'], self.lkbk - 8)[-1]
self.state = np.append(self.state, (sma1 - sma2) / sma2)
self.state = np.append(self.state, sma1)
self.state = np.append(
self.state,
talib.RSI(candles[c]['close'], self.lkbk - 1)[-1])
self.state = np.append(
self.state,
talib.MOM(candles[c]['close'], self.lkbk - 1)[-1])
#self.state = np.append(self.state,talib.MACD(candles[c]['close'],fastperiod=11, slowperiod=22, signalperiod=9)[0][0])
self.state = np.append(
self.state,
talib.BOP(candles[c]['open'], candles[c]['high'],
candles[c]['low'], candles[c]['close'])[-1])
#self.state = np.append(self.state,talib.ADXR(candles[c]['high'],
# candles[c]['low'],
# candles[c]['close'],
# self.lkbk-3)[-1])
#self.state = np.append(self.state,talib.STOCH(candles[c]['high'],
# candles[c]['low'],
# candles[c]['close'],5,3,0,3,0)[-1][0])
self.state = np.append(
self.state,
talib.AROONOSC(candles[c]['high'], candles[c]['low'],
self.lkbk - 3)[-1])
except:
print(traceback.format_exc())
#print('-->',self.state)
self.state = (np.array(self.state) - np.mean(self.state)) / np.std(
self.state)
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 add_aroon_s(dataset,param, first_header):
field_name_up = 'aroon_s' + str(param)
df = dataset[first_header].copy()
high = df['High'].values
low = df['Low'].values
aroon = talib.AROONOSC(high, low, timeperiod=14)/100
col = pd.DataFrame(aroon, index=df.index)
dataset[first_header, field_name_up] = col
return dataset
def aroon_oscillator(df, n):
"""
Calculates Aroon oscillator using Ta-Lib library's built-in function.
:param df: pandas.DataFrame
:return: pandas.DataFrame
"""
AOSC = talib.AROONOSC(df['High'], df['Low'], timeperiod=n)
AOSC_series = pd.Series(AOSC, name='AO_' + str(n))
df = df.join(AOSC_series)
return df
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_aroon_osc(self):
result = pandas_ta.aroon(self.high, self.low)
try:
expected = tal.AROONOSC(self.high, self.low)
pdt.assert_series_equal(result.iloc[:, 2], expected)
except AssertionError as ae:
try:
aroond_corr = pandas_ta.utils.df_error_analysis(
result.iloc[:, 2], expected, col=CORRELATION)
self.assertGreater(aroond_corr, CORRELATION_THRESHOLD)
except Exception as ex:
error_analysis(result.iloc[:, 0], CORRELATION, ex)
