def ADX5_AROON(self, df):
adx5_exLevel = 95
adx5_brLevel = 10
df.drop(df.last_valid_index(), axis=0, inplace=True)
df['PLUS_DI'] = talib.PLUS_DI(df['High'].values, df['Low'].values, df['Close'].values, timeperiod=5)
df['MINUS_DI'] = talib.MINUS_DI(df['High'].values, df['Low'].values, df['Close'].values, timeperiod=5)
df['ADX5'] = talib.ADX(df['High'].values, df['Low'].values, df['Close'].values, timeperiod=5)
df['ARDN'], df['ARUP'] = talib.AROON(df['High'].values, df['Low'].values, timeperiod=10)
df['ADX5SHIFTED'] = df['ADX5'].shift(1)
df['ARDNSHIFTED'] = df['ARDN'].shift(1)
df['ARUPSHIFTED'] = df['ARUP'].shift(1)
columns = ['PLUS_DI', 'MINUS_DI', 'ADX5', 'AROONDN', 'AROONUP', 'ADX5SHIFTED', 'ARDNSHIFTED', 'ARUPSHIFTED']
def adx5_aroon_cond(row, columns):
df1_adx5_cond = (row['ADX5'] > row['ADX5SHIFTED']) & (row['ADX5'] > adx5_brLevel) & (
row['ADX5'] < adx5_exLevel)
arbuy = (row['ARUPSHIFTED'] < 5) & (row['ARUP'] > 85) & (row['ARUP'] > row['ARDN'])
arsell = (row['ARDNSHIFTED'] < 5) & (row['ARDN'] > 85) & (row['ARUP'] < row['ARDN'])
buy = (row['PLUS_DI'] > row['MINUS_DI']) & df1_adx5_cond & arbuy
sell = (row['PLUS_DI'] < row['MINUS_DI']) & df1_adx5_cond & arsell
return buy, sell
df['BUY'], df['SELL'] = zip(*df.apply(lambda row: adx5_aroon_cond(row, columns), axis=1))
df2 = df.tail(self.withinBars)
df2['BUY'].any()
df2['SELL'].any()
#return [df['SELL'].iloc[-1], df['BUY'].iloc[-1], df['normal_time']]
return [df2['SELL'].any(), df2['BUY'].any(), df['normal_time']]
def add_AROON(self,
timeperiod=14,
types=['line', 'line'],
colors=['increasing', 'decreasing'],
**kwargs):
"""Aroon indicators.
Note that the first argument of types and colors refers to Aroon up while
the second argument refers to Aroon down.
"""
if not (self.has_high and self.has_low):
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 = 'AROON({})'.format(str(timeperiod))
uaroon = name + ' [Up]'
daroon = name + ' [Dn]'
self.sec[uaroon] = dict(type=types[0], color=colors[0])
self.sec[daroon] = dict(type=types[1], color=colors[1], on=uaroon)
self.ind[uaroon], self.ind[daroon] = talib.AROON(self.df[self.hi].values,
self.df[self.lo].values,
timeperiod)
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 calculate_indicators(data):
close = data['Close'].values
high = data['High'].values
low = data['Low'].values
data['upB'], data['midB'], data['lowB'] = talib.BBANDS(close,
timeperiod=20,
nbdevup=2,
nbdevdn=2,
matype=0)
data['RSI'] = talib.RSI(close, timeperiod=10)
data['K'], d = talib.STOCH(high,
low,
close,
fastk_period=14,
slowk_period=14,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
macd, macdsignal, data['MACD'] = talib.MACD(close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
data['EMA'] = talib.EMA(close, timeperiod=30)
data['ADX'] = talib.ADX(high, low, close, timeperiod=14)
data['AroonUp'], data['AroonDown'] = talib.AROON(high, low, timeperiod=14)
data['diff'] = data['High'] - data['Low']
return data
def get_stock_data(ticker, start_date, end_date):
print(end_date, type(ticker))
data = nse.get_history(symbol=ticker, start=start_date,
end= end_date)
#data = nse.get_history(symbol= ticker, start=start_date, end= end_date)
data = data.drop(['Prev Close','Symbol', 'Series', 'Deliverable Volume', '%Deliverble', 'Trades', 'Last'], axis = 1)
close = data['Close'].values
high = data['High'].values
low = data['Low'].values
#Computing technical indicators
data['upB'], data['midB'], data['lowB'] = talib.BBANDS(close, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)
data['RSI'] = talib.RSI(close, timeperiod=10)
data['K'], d = talib.STOCH(high, low, close, fastk_period=14, slowk_period=14, slowk_matype=0, slowd_period=3,
slowd_matype=0)
macd, macdsignal, data['MACD'] = talib.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
data['EMA'] = talib.EMA(close, timeperiod=30)
data['ADX'] = talib.ADX(high, low, close, timeperiod=14)
data['AroonUp'], data['AroonDown'] = talib.AROON(high, low, timeperiod=14)
data['diff'] = data['High'] - data['Low']
data = data.dropna()
#saving as a csv file
data.to_csv('stock_prices' + str(ticker) + '.csv', index=False)
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 put(self):
"""Method to check put pattern."""
logger = logging.getLogger("__main__")
candles = self.candles
if hasattr(candles, 'candles_array'):
candel_array = candles.candles_array
close = pd.Series([candle.candle_close for candle in candel_array])
open = pd.Series([candle.candle_open for candle in candel_array])
high = pd.Series([candle.candle_high for candle in candel_array])
low = pd.Series([candle.candle_low for candle in candel_array])
up, lw, MA = talib.BBANDS(close, matype=MA_Type.T3)
rsi14 = talib.RSI(close, timeperiod=14)
sigs = trendy.iterlines(close,
open,
high,
low,
window=1 / 3.0,
charts=False)
# K, D = self.stoc_occilator(candles=candles)
aroon_up, aroon_down = talib.AROON(high, low, timeperiod=3)
# loaded_model = pickle.load(open('finalized_model.sav', 'rb'))
# predicted_price = loaded_model.predict([[up[26] - candles.first_candle.candle_close, lw[26] - candles.first_candle.candle_close, candles.first_candle.candle_height - candles.second_candle.candle_height, rsi14[28], K[28], D[28], aroon_up[29], aroon_down[29]]])
if candles.current_candle.candle_close > candles.current_candle.candle_open: #candles.current_candle.candle_close > MA[27] and candles.current_candle.candle_open < MA[27] and aroon_up[27]==0 and aroon_up[27]==0:
return True
def on_bar(self, bars):
high = self.source_preference + '_high'
low = self.source_preference + '_low'
bars['up'], bars['down'] = talib.AROON(bars[high].values,
bars[low].values, self.period)
return bars
def AROON(DF,N=20):
H = DF['high']
L = DF['low']
AD,AP = ta.AROON(H.values,L.values,N)
AR = AP - AD
VAR = pd.DataFrame({'AROON': AR, 'AROON_UP': AP, 'AROON_DOWN': AD}, index=H.index.values)
return VAR
def add_AROON(
self,
timeperiod=14,
types=["line", "line"],
colors=["increasing", "decreasing"],
**kwargs
):
"""Aroon indicators.
Note that the first argument of types and colors refers to Aroon up while
the second argument refers to Aroon down.
"""
if not (self.has_high and self.has_low):
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 = "AROON({})".format(str(timeperiod))
uaroon = name + " [Up]"
daroon = name + " [Dn]"
self.sec[uaroon] = dict(type=types[0], color=colors[0])
self.sec[daroon] = dict(type=types[1], color=colors[1], on=uaroon)
self.ind[uaroon], self.ind[daroon] = talib.AROON(
self.df[self.hi].values, self.df[self.lo].values, timeperiod
)
def AROON(raw_df, timeperiod=14):
# extract necessary data from raw dataframe (high, low)
# this function returns two columns of data
aroondown, aroonup = ta.AROON(raw_df.High.values, raw_df.Low.values,
timeperiod)
singleMerged = np.stack((aroondown, aroonup), axis=-1)
return singleMerged.tolist()
def aroon():
for i in range(len(ForexTraderSwitch.curr_pair_list)):
high=(ForexTraderSwitch.curr_pair_history_data[i]['highAsk'].values+\
ForexTraderSwitch.curr_pair_history_data[i]['highBid'].values)/2
low=(ForexTraderSwitch.curr_pair_history_data[i]['lowAsk'].values+\
ForexTraderSwitch.curr_pair_history_data[i]['lowBid'].values)/2
#open=(history['openAsk']+history['openBid'])/2
bull, bear = talib.AROON(high, low, timeperiod=14)
#print("Bull %s Bear %s" % (bull[-1], bear[-1]))
ForexTraderSwitch.signal[i, 2] = bear[-2]
ForexTraderSwitch.signal[i, 3] = bear[-1]
ForexTraderSwitch.signal[i, 4] = bull[-2]
ForexTraderSwitch.signal[i, 5] = bull[-1]
if (bull[-1] > 70 and bear[-1] < 30) or (bull[-2] < bear[-2]
and bull[-1] >= bear[-1]):
#trader.create_buy_order(ticker,units)
ForexTraderSwitch.order[i, 2, 1] = 1
elif (bull[-1] < 30
and bear[-1] > 70) or (bull[-2] >= bear[-2]
and bull[-1] < bear[-1]):
#trader.create_sell_order(ticker,units)
ForexTraderSwitch.order[i, 2, 2] = 1
else:
#print('No trade made')
ForexTraderSwitch.order[i, 2, 0] = 1
def AROON(df, n):
aroondown, aroonup = talib.AROON(df['high'].values,
df['low'].values,
timeperiod=n)
aroon_dn = pd.Series(aroondown, index=df.index, name="AROONDN_%s" % str(n))
aroon_up = pd.Series(aroonup, index=df.index, name="AROONUP_%s" % str(n))
return pd.concat([aroon_up, aroon_dn], join='outer', axis=1)
def aroon_analyse(ohlcv, period=14):
high = ohlcv['high']
low = ohlcv['low']
adown, aup = talib.AROON(high, low, period)
df = pd.DataFrame({'aroon_down': adown, 'aroon_up': aup})
'''当 AroonUp大于AroonDown，并且AroonUp大于50，多头开仓；
当 AroonUp小于AroonDown，或者AroonUp小于50，多头平仓；
当 AroonDown大于AroonUp，并且AroonDown大于50，空头开仓；
当 AroonDown小于AroonUp，或者AroonDown小于50，空头平仓；'''
def aroon_row(row):
res = []
if row['aroon_up'] > row['aroon_down'] and row['aroon_up'] > 50:
res.append('UP-STRONG')
elif row['aroon_up'] < row['aroon_down'] or row['aroon_up'] < 50:
res.append('DN-WEAK')
elif row['aroon_up'] < row['aroon_down'] or row['aroon_down'] > 50:
res.append('DN-STRONG')
elif row['aroon_up'] > row['aroon_down'] or row['aroon_down'] < 50:
res.append('UP-WEAK')
else:
res.append('UNKNOWN')
return ' '.join(res)
df['aroon_stage'] = df.apply(aroon_row, axis=1)
return df
def aroon(client,
symbol,
timeframe="6m",
highcol="high",
lowcol="low",
period=14):
"""This will return a dataframe of
Aroon
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)
aroondown, aroonup = t.AROON(df[highcol].values.astype(float),
df[lowcol].values.astype(float), period)
return pd.DataFrame({
highcol: df[highcol].values,
lowcol: df[lowcol].values,
"aroonup": aroonup,
"aroondown": aroondown,
})
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 test_aroon(self):
result = pandas_ta.aroon(self.high, self.low)
self.assertIsInstance(result, DataFrame)
self.assertEqual(result.name, "AROON_14")
try:
expected = tal.AROON(self.high, self.low)
expecteddf = DataFrame({
"AROOND_14": expected[0],
"AROONU_14": expected[1]
})
pdt.assert_frame_equal(result, expecteddf)
except AssertionError as ae:
try:
aroond_corr = pandas_ta.utils.df_error_analysis(
result.iloc[:, 0], expecteddf.iloc[:, 0], col=CORRELATION)
self.assertGreater(aroond_corr, CORRELATION_THRESHOLD)
except Exception as ex:
error_analysis(result.iloc[:, 0], CORRELATION, ex)
try:
aroonu_corr = pandas_ta.utils.df_error_analysis(
result.iloc[:, 1], expecteddf.iloc[:, 1], col=CORRELATION)
self.assertGreater(aroonu_corr, CORRELATION_THRESHOLD)
except Exception as ex:
error_analysis(result.iloc[:, 1],
CORRELATION,
ex,
newline=False)
def SignalDetection(df, tick, *args):
"""
This function downloads prices for desired quotes (those in the parameter)
and then tries to catch signals for selected timeframe.
Stocks for which we catched a signal are stored in variable "validsymbol"
:param p1: dataframe of eod data
:param p2: ticker
:returns: df with signals
"""
close = df["Close"].to_numpy()
high = df["High"].to_numpy()
low = df["Low"].to_numpy()
# Aroon
aroonDOWN, aroonUP = talib.AROON(high=high, low=low,
timeperiod=Aroonval) ####
# RSI
real = talib.RSI(close, timeperiod=14)
df['RSI'] = real
df['Aroon Down'] = aroonDOWN
df['Aroon Up'] = aroonUP
df['signal'] = pd.Series(np.zeros(len(df)))
df['signal_aroon'] = pd.Series(np.zeros(len(df)))
df = df.reset_index()
# Moving averages
df['short_mavg'] = df['Close'].rolling(window=short_window,
min_periods=1,
center=False).mean()
df['long_mavg'] = df['Close'].rolling(window=long_window,
min_periods=1,
center=False).mean()
# When 'Aroon Up' crosses 'Aroon Down' from below
df["signal"][short_window:] = np.where(
df['short_mavg'][short_window:] > df['long_mavg'][short_window:], 1, 0)
df["signal_aroon"][short_window:] = np.where(
df['Aroon Down'][short_window:] < df['Aroon Up'][short_window:], 1, 0)
df['positions'] = df['signal'].diff()
df['positions_aroon'] = df['signal_aroon'].diff()
df['positions_aroon'].value_counts()
# Trend reversal detection
# Aroon alone doesn't give us enough information.
# Too many false signals are given
# We blend moving averages crossing strategy
df['doubleSignal'] = np.where(
(df["Aroon Up"] > df["Aroon Down"]) & (df['positions'] == 1) &
(df["Aroon Down"] < 75) & (df["Aroon Up"] > 55), 1, 0)
df['symbol'] = tick
csvAppend(df)
return df
def aroon(self, n, array=False):
""""""
# Course code
aroon_up, aroon_down = talib.AROON(self.high, self.low, n)
if array:
return aroon_up, aroon_down
else:
return aroon_up[-1], aroon_down[-1]
def aroon(self, n, array=False):
"""
AROON.
"""
aroon_up, aroon_down = talib.AROON(self.high, self.low, n)
if array:
return aroon_up, aroon_down
return aroon_up[-1], aroon_down[-1]
def Aroon(self, timeperiod=14):
aroon = pd.DataFrame()
aroon['down'], aroon['up'] = ta.AROON(self.data.high,
self.data.low,
timeperiod=14)
return 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 aroon(self, n: int, dev: float, array: bool = False):
"""
Aroon indicator.
"""
aroon_up, aroon_down = talib.AROON(self.high, self.low, n)
if array:
return aroon_up, aroon_down
return aroon_up[-1], aroon_down[-1]
def AROON(self, timeperiod=14):
real_data = np.array([self.df.high, self.df.low], dtype='f8')
# aroondown, aroonup = talib.AROON(real_data[0], real_data[1], timeperiod=timeperiod)
# return go.Scatter(
# x=self.df.index,
# y=aroondown,
# name='AROON'
# )
return talib.AROON(real_data[0], real_data[1], timeperiod=timeperiod)
def aroon(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.AROON(highs, lows, *args, **kwargs)
return v
def add_indicators_and_disc(self):
discretizing = 2
self.data["ADL"] = ta.ADX(self.data["High"].values,
self.data["Low"].values,
self.data["Close"].values)
self.data["AROON_1"] = ta.AROON(self.data["High"].values,
self.data["Low"].values)[0]
self.data["AROON_2"] = ta.AROON(self.data["High"].values,
self.data["Low"].values)[1]
self.data["MACD_1"] = ta.MACD(self.data["Close"].values)[0]
self.data["MACD_2"] = ta.MACD(self.data["Close"].values)[1]
self.data["MACD_3"] = ta.MACD(self.data["Close"].values)[2]
self.data["RSI"] = ta.RSI(self.data["Close"].values)
self.data["STOCHASTIC_1"] = ta.STOCH(self.data["High"].values,
self.data["Low"].values,
self.data["Close"].values)[0]
self.data["STOCHASTIC_2"] = ta.STOCH(self.data["High"].values,
self.data["Low"].values,
self.data["Close"].values)[1]
self.data = self.data.dropna().copy()
self.data["1"] = pd.cut(self.data["ADL"], discretizing, labels=False)
self.data["2"] = pd.cut(self.data["AROON_1"],
discretizing,
labels=False)
self.data["3"] = pd.cut(self.data["AROON_2"],
discretizing,
labels=False)
self.data["4"] = pd.cut(self.data["MACD_1"],
discretizing,
labels=False)
self.data["5"] = pd.cut(self.data["MACD_2"],
discretizing,
labels=False)
self.data["6"] = pd.cut(self.data["MACD_3"],
discretizing,
labels=False)
self.data["7"] = pd.cut(self.data["RSI"], discretizing, labels=False)
self.data["8"] = pd.cut(self.data["STOCHASTIC_1"],
discretizing,
labels=False)
self.data["9"] = pd.cut(self.data["STOCHASTIC_2"],
discretizing,
labels=False)
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 _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 AROON(high, low, timeperiod=14):
''' Aroon 阿隆指标
分组: Momentum Indicator 动量指标
简介: 该指标是通过计算自价格达到近期最高值和最低值以来所经过的期间数，
阿隆指标帮助你预测价格趋势到趋势区域（或者反过来，从趋势区域到趋势）的变化。
aroondown, aroonup = AROON(high, low, timeperiod=14)
'''
return talib.AROON(high, low, timeperiod)
def factorMaker17(Data, parameter, pair):
a = int(parameter['a'])
b = int(parameter['b'])
df = Data.copy()
df['tmp'] = df['cp_adj_' + pair[0]] / df['cp_adj_' + pair[1]]
df['tmp'] = df['tmp'].rolling(a).mean()
df['up'], df['down'] = talib.AROON(df['tmp'].values, df['tmp'].values, b)
factor = pd.Series(np.zeros(len(df['tmp'])))
factor[(df['up'] > df['down'])] = 1
factor[(df['up'] < df['down'])] = -1
return factor
