def add_cycle_indicators(data_list):
for data in data_list:
#HT_DCPERIOD - Hilbert Transform - Dominant Cycle Period
real = talib.HT_DCPERIOD(data.Close)
data['HT_DCPERIOD'] = real
#HT_DCPHASE - Hilbert Transform - Dominant Cycle Phase
real = talib.HT_DCPHASE(data.Close)
data['HT_DCPHASE'] = real
#HT_PHASOR - Hilbert Transform - Phasor Components
inphase, quadrature = talib.HT_PHASOR(data.Close)
data['HT_PHASOR_inphase'] = inphase
data['HT_PHASOR_quadrature'] = quadrature
#HT_SINE - Hilbert Transform - SineWave
sine, leadsine = talib.HT_SINE(data.Close)
data['HT_SINE_sine'] = sine
data['HT_SINE_leadsine'] = leadsine
#HT_TRENDMODE - Hilbert Transform - Trend vs Cycle Mode
integer = talib.HT_TRENDMODE(data.Close)
data['HT_TRENDMODE'] = integer
return data_list
def eval(self, environment, gene, date1, date2):
date1_ = environment.shift_date(date1, -(self.window - 1), -1)
df = gene.next_value(environment, date1_, date2)
res = pd.DataFrame(np.nan,
index=df.ix[date1:date2].index,
columns=df.columns)
for i, j in product(range(res.shape[0]), range(res.shape[1])):
res.iloc[i, j] = talib.HT_TRENDMODE(df.values[i:i + self.window,
j])[-1]
return res
def HT_TRENDMODE(close):
''' Hilbert Transform - Trend vs Cycle Mode 希尔伯特变换-趋势与周期模式
分组: Cycle Indicators 周期指标
简介:
integer = HT_TRENDMODE(close)
'''
return talib.HT_TRENDMODE(close)
def getCycleIndicators(df):
high = df['High']
low = df['Low']
close = df['Close']
open = df['Open']
volume = df['Volume']
df['DCPERIOD'] = ta.HT_DCPERIOD(close)
df['DCPHASE'] = ta.HT_DCPHASE(close)
df['INPHASE'], df['QUADRATURE'] = ta.HT_PHASOR(close)
df['SINE'], df['LEADSINE'] = ta.HT_SINE(close)
df['TRENDMODE'] = ta.HT_TRENDMODE(close)
def sexy(k):
if len(k) > 3:
vm = wrap(k)
print("--talib--")
print(list(talib.HT_DCPERIOD(vm)))
print(list(talib.HT_DCPHASE(vm)))
print(list(talib.HT_PHASOR(vm)))
print(list(talib.HT_SINE(vm)))
print(list(talib.HT_TRENDMODE(vm)))
print("--talib--")
else:
pass
def computeHilbertTransformSignals(ticker, pd):
closeField = config.ticker2ReturnFieldMap[ticker]
close = numpy.array(pd[closeField])
pd['Hilbert.DCPeriod'] = talib.HT_DCPERIOD(close)
pd['Hilbert.DCPhase'] = talib.HT_DCPHASE(close)
pd['Hilbert.inphase'], pd['Hilbert.quadrature'] = talib.HT_PHASOR(close)
pd['Hilbert.sine'], pd['Hilbert.leadsine'] = talib.HT_SINE(close)
pd['Hilbert.TrendMode'] = talib.HT_TRENDMODE(close)
fields = [
'Hilbert.DCPeriod', 'Hilbert.DCPhase', 'Hilbert.inphase',
'Hilbert.quadrature', 'Hilbert.sine', 'Hilbert.leadsine',
'Hilbert.TrendMode'
]
return pd, fields
def ht_trendmode(candles: np.ndarray, source_type: str = "close", sequential: bool = False) -> Union[float, np.ndarray]:
"""
HT_TRENDMODE - Hilbert Transform - Trend vs Cycle Mode
:param candles: np.ndarray
:param source_type: str - default: "close"
:param sequential: bool - default: False
:return: int | np.ndarray
"""
candles = slice_candles(candles, sequential)
source = get_candle_source(candles, source_type=source_type)
res = talib.HT_TRENDMODE(source)
return res if sequential else res[-1]
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 getCustomData(df):
open = df['Open']
high = df['High']
low = df['Low']
close = df['Close']
volume = df['Volume']
df['TRENDMODE'] = ta.HT_TRENDMODE(close)
df['RSI'] = ta.RSI(close,timeperiod=14)
df['RSI2'] = ta.RSI(close,timeperiod=7)
df['RSI3'] = ta.RSI(close,timeperiod=28)
df['ADX1'] = ta.ADX(high,low,close,timeperiod=7)
df['ADX2'] = ta.ADX(high,low,close,timeperiod=28)
df['ADX'] = ta.ADX(high,low,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['ULTOSC'] = ta.ULTOSC(high, low, close, timeperiod1=7, timeperiod2=14, timeperiod3=28)
df['WILLR'] = ta.WILLR(high, low, close, timeperiod=14)
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
def ht_trendmode(client, symbol, timeframe="6m", col="close"):
"""This will return a dataframe of
Hilbert Transform - Trend vs Cycle Mode
for the given symbol across
the given timeframe
Args:
client (pyEX.Client); Client
symbol (string); Ticker
timeframe (string); timeframe to use, for pyEX.chart
col (string); column to use to calculate
Returns:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
x = t.HT_TRENDMODE(df[col].values)
return pd.DataFrame({col: df[col].values, "ht_trendmode": x})
def test_HT_TRENDMODE(self):
class MyHT_TRENDMODE(OperatorHT_TRENDMODE):
def __init__(self, name, **kwargs):
super(MyHT_TRENDMODE, self).__init__(100, name, **kwargs)
self.env.add_operator('ht_trendmode', {
'operator': MyHT_TRENDMODE,
})
string = 'ht_trendmode(open)'
gene = self.env.parse_string(string)
self.assertRaises(IndexError, gene.eval, self.env, self.dates[98],
self.dates[-1])
ser = gene.eval(self.env, self.dates[99], self.dates[99]).iloc[0]
data = self.env.get_data_value('open')
res = []
for i, val in ser.iteritems():
res.append(talib.HT_TRENDMODE(data[i].values[:100])[-1] == val)
self.assertTrue(all(res))
def get_cycle_indicators(df_price):
df_local = df_price.copy()
df_nonna_idxs = df_local[~df_local.Close.isna()].Close.index
np_adj_close = df_local.Adj_Close.values
np_close = df_local.Close.values
np_open = df_local.Open.values
np_high = df_local.High.values
np_low = df_local.Low.values
np_volume = df_local.Volume.values
np_nan_indices = np.isnan(np_close)
#HT_DCPERIOD-Hilbert Transform - Dominant Cycle Period
HT_DCPERIOD = pd.Series(ta.HT_DCPERIOD(np_adj_close[~np_nan_indices]),
index=df_nonna_idxs)
df_local['HT_DCPERIOD'] = HT_DCPERIOD
#HT_DCPHASE-Hilbert Transform - Dominant Cycle Phase
HT_DCPHASE = pd.Series(ta.HT_DCPHASE(np_adj_close[~np_nan_indices]),
index=df_nonna_idxs)
df_local['HT_DCPHASE'] = HT_DCPHASE
#HT_PHASOR-Hilbert Transform - Phasor Components
HT_PHASOR = ta.HT_PHASOR(np_adj_close[~np_nan_indices])
df_local['HT_PHASOR_INPHASE'] = pd.Series(HT_PHASOR[0],
index=df_nonna_idxs)
df_local['HT_PHASOR_QUADRATURE'] = pd.Series(HT_PHASOR[1],
index=df_nonna_idxs)
#HT_SINE - Hilbert Transform - SineWave
HT_SINE = ta.HT_SINE(np_adj_close[~np_nan_indices])
df_local['HT_SINE_SINE'] = pd.Series(HT_SINE[0], index=df_nonna_idxs)
df_local['HT_SINE_LEADSINE'] = pd.Series(HT_SINE[1], index=df_nonna_idxs)
#HT_TRENDMODE-Hilbert Transform - Trend vs Cycle Mode
HT_TRENDMODE = pd.Series(ta.HT_TRENDMODE(np_adj_close[~np_nan_indices]),
index=df_nonna_idxs)
df_local['HT_TRENDMODE'] = HT_TRENDMODE
return df_local
def handle_cycle_indicators(args, axes, i, klines_df, close_times,
display_count):
# talib
if args.HT_DCPERIOD:
name = 'HT_DCPERIOD'
real = talib.HT_DCPERIOD(klines_df["close"])
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "y:", label=name)
if args.HT_DCPHASE:
name = 'HT_DCPHASE'
real = talib.HT_DCPHASE(klines_df["close"])
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "y:", label=name)
if args.HT_PHASOR:
name = 'HT_PHASOR'
real = talib.HT_PHASOR(klines_df["close"])
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "y:", label=name)
if args.HT_SINE:
name = 'HT_SINE'
real = talib.HT_SINE(klines_df["close"])
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "y:", label=name)
if args.HT_TRENDMODE:
name = 'HT_TRENDMODE'
real = talib.HT_TRENDMODE(klines_df["close"])
i += 1
axes[i].set_ylabel(name)
axes[i].grid(True)
axes[i].plot(close_times, real[-display_count:], "y:", label=name)
def generate_tech_data(stock, open_name, close_name, high_name, low_name):
open_price = stock[open_name].values
close_price = stock[close_name].values
low_price = stock[low_name].values
high_price = stock[high_name].values
data = pd.DataFrame(stock)
data['MOM'] = talib.MOM(close_price)
data['SMA'] = talib.SMA(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['HT_TRENDMODE'] = talib.HT_TRENDMODE(close_price)
data['SAREXT'] = talib.SAREXT(high_price, low_price)
data['ADX'] = talib.ADX(high_price, low_price, close_price)
data['ADXR'] = talib.ADX(high_price, low_price, close_price)
data['APO'] = talib.APO(close_price)
data['AROON_UP'], data['AROON_DOWN'] = talib.AROON(high_price, low_price)
data['AROONOSC'] = talib.AROONOSC(high_price, low_price)
data['BOP'] = talib.BOP(open_price, high_price, low_price, close_price)
data['CCI'] = talib.CCI(high_price, low_price, close_price)
data['PLUS_DI'] = talib.PLUS_DI(high_price, low_price, close_price)
data['PLUS_DM'] = talib.PLUS_DM(high_price, low_price)
data['PPO'] = talib.PPO(close_price)
data['macd'], data['macd_sig'], data['macd_hist'] = talib.MACD(close_price)
data['RSI'] = talib.RSI(close_price)
data['CMO'] = talib.CMO(close_price)
data['ROC'] = talib.ROC(close_price)
data['ROCP'] = talib.ROCP(close_price)
data['ROCR'] = talib.ROCR(close_price)
data['slowk'], data['slowd'] = talib.STOCH(high_price, low_price,
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['TRANGE'] = talib.TRANGE(high_price, low_price, close_price)
data = data.drop([open_name, close_name, high_name, low_name], axis=1)
data = data.dropna()
return data
def ht_trendmode(candles: np.ndarray,
source_type: str = "close",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
HT_TRENDMODE - Hilbert Transform - Trend vs Cycle Mode
:param candles: np.ndarray
:param source_type: str - default: "close"
:param sequential: bool - default=False
:return: int | np.ndarray
"""
warmup_candles_num = get_config('env.data.warmup_candles_num', 240)
if not sequential and len(candles) > warmup_candles_num:
candles = candles[-warmup_candles_num:]
source = get_candle_source(candles, source_type=source_type)
res = talib.HT_TRENDMODE(source)
return res if sequential else res[-1]
def get_df(filename):
tech = pd.read_csv(filename,index_col=0)
dclose = np.array(tech.close)
volume = np.array(tech.volume)
tech['RSI'] = ta.RSI(np.array(tech.close))
tech['OBV'] = ta.OBV(np.array(tech.close),np.array(tech.volume))
tech['NATR'] = ta.NATR(np.array(tech.high),np.array(tech.low),np.array(tech.close))
tech['upper'],tech['middle'],tech['lower'] = ta.BBANDS(np.array(tech.close), timeperiod=10, nbdevup=2, nbdevdn=2, matype=0)
tech['DEMA'] = ta.DEMA(dclose, timeperiod=30)
tech['EMA'] = ta.EMA(dclose, timeperiod=30)
tech['HT_TRENDLINE'] = ta.HT_TRENDLINE(dclose)
tech['KAMA'] = ta.KAMA(dclose, timeperiod=30)
tech['MA'] = ta.MA(dclose, timeperiod=30, matype=0)
# tech['mama'], tech['fama'] = ta.MAMA(dclose, fastlimit=0, slowlimit=0)
tech['MIDPOINT'] = ta.MIDPOINT(dclose, timeperiod=14)
tech['SMA'] = ta.SMA(dclose, timeperiod=30)
tech['T3'] = ta.T3(dclose, timeperiod=5, vfactor=0)
tech['TEMA'] = ta.TEMA(dclose, timeperiod=30)
tech['TRIMA'] = ta.TRIMA(dclose, timeperiod=30)
tech['WMA'] = ta.WMA(dclose, timeperiod=30)
tech['APO'] = ta.APO(dclose, fastperiod=12, slowperiod=26, matype=0)
tech['CMO'] = ta.CMO(dclose, timeperiod=14)
tech['macd'], tech['macdsignal'], tech['macdhist'] = ta.MACD(dclose, fastperiod=12, slowperiod=26, signalperiod=9)
tech['MOM'] = ta.MOM(dclose, timeperiod=10)
tech['PPO'] = ta.PPO(dclose, fastperiod=12, slowperiod=26, matype=0)
tech['ROC'] = ta.ROC(dclose, timeperiod=10)
tech['ROCR'] = ta.ROCR(dclose, timeperiod=10)
tech['ROCP'] = ta.ROCP(dclose, timeperiod=10)
tech['ROCR100'] = ta.ROCR100(dclose, timeperiod=10)
tech['RSI'] = ta.RSI(dclose, timeperiod=14)
tech['fastk'], tech['fastd'] = ta.STOCHRSI(dclose, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0)
tech['TRIX'] = ta.TRIX(dclose, timeperiod=30)
tech['OBV'] = ta.OBV(dclose,volume)
tech['HT_DCPHASE'] = ta.HT_DCPHASE(dclose)
tech['inphase'], tech['quadrature'] = ta.HT_PHASOR(dclose)
tech['sine'], tech['leadsine'] = ta.HT_SINE(dclose)
tech['HT_TRENDMODE'] = ta.HT_TRENDMODE(dclose)
df = tech.fillna(method='bfill')
return df
def cycle_process(event):
print(event.widget.get())
cycle = event.widget.get()
upperband, middleband, lowerband = ta.BBANDS(close,
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)
fig, axes = plt.subplots(2, 1, sharex=True)
ax1, ax2 = axes[0], axes[1]
axes[0].plot(close, 'rd-', markersize=3)
axes[0].plot(upperband, 'y-')
axes[0].plot(middleband, 'b-')
axes[0].plot(lowerband, 'y-')
axes[0].set_title(cycle, fontproperties='SimHei')
if momentum == '希尔伯特变换——主要的循环周期':
real = ta.HT_DCPERIOD(close)
axes[1].plot(real, 'r-')
elif momentum == '希尔伯特变换,占主导地位的周期阶段':
real = ta.HT_DCPHASE(close)
axes[1].plot(real, 'r-')
elif momentum == '希尔伯特变换——相量组件':
inphase, quadrature = ta.HT_PHASOR(close)
axes[1].plot(inphase, 'r-')
axes[1].plot(quadrature, 'g-')
elif momentum == '希尔伯特变换——正弦曲线':
sine, leadsine = ta.HT_SINE(close)
axes[1].plot(sine, 'r-')
axes[1].plot(leadsine, 'g-')
elif momentum == '希尔伯特变换——趋势和周期模式':
integer = ta.HT_TRENDMODE(close)
axes[1].plot(integer, 'r-')
plt.show()
def calc_features(df):
open = df['op']
high = df['hi']
low = df['lo']
close = df['cl']
volume = df['volume']
orig_columns = df.columns
hilo = (df['hi'] + df['lo']) / 2
df['BBANDS_upperband'], df['BBANDS_middleband'], df[
'BBANDS_lowerband'] = talib.BBANDS(close,
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)
df['BBANDS_upperband'] -= hilo
df['BBANDS_middleband'] -= hilo
df['BBANDS_lowerband'] -= hilo
df['DEMA'] = talib.DEMA(close, timeperiod=30) - hilo
df['EMA'] = talib.EMA(close, timeperiod=30) - hilo
df['HT_TRENDLINE'] = talib.HT_TRENDLINE(close) - hilo
df['KAMA'] = talib.KAMA(close, timeperiod=30) - hilo
df['MA'] = talib.MA(close, timeperiod=30, matype=0) - hilo
df['MIDPOINT'] = talib.MIDPOINT(close, timeperiod=14) - hilo
df['SMA'] = talib.SMA(close, timeperiod=30) - hilo
df['T3'] = talib.T3(close, timeperiod=5, vfactor=0) - hilo
df['TEMA'] = talib.TEMA(close, timeperiod=30) - hilo
df['TRIMA'] = talib.TRIMA(close, timeperiod=30) - hilo
df['WMA'] = talib.WMA(close, timeperiod=30) - hilo
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['AROON_aroondown'], df['AROON_aroonup'] = talib.AROON(high,
low,
timeperiod=14)
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['DX'] = talib.DX(high, low, close, timeperiod=14)
df['MACD_macd'], df['MACD_macdsignal'], df['MACD_macdhist'] = talib.MACD(
close, fastperiod=12, slowperiod=26, signalperiod=9)
# skip MACDEXT MACDFIX たぶん同じなので
df['MFI'] = talib.MFI(high, low, close, volume, timeperiod=14)
df['MINUS_DI'] = talib.MINUS_DI(high, low, close, timeperiod=14)
df['MINUS_DM'] = talib.MINUS_DM(high, low, timeperiod=14)
df['MOM'] = talib.MOM(close, timeperiod=10)
df['PLUS_DI'] = talib.PLUS_DI(high, low, close, timeperiod=14)
df['PLUS_DM'] = talib.PLUS_DM(high, low, timeperiod=14)
df['RSI'] = talib.RSI(close, timeperiod=14)
df['STOCH_slowk'], df['STOCH_slowd'] = talib.STOCH(high,
low,
close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
df['STOCHF_fastk'], df['STOCHF_fastd'] = talib.STOCHF(high,
low,
close,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
df['STOCHRSI_fastk'], df['STOCHRSI_fastd'] = talib.STOCHRSI(close,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
df['TRIX'] = talib.TRIX(close, timeperiod=30)
df['ULTOSC'] = talib.ULTOSC(high,
low,
close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
df['WILLR'] = talib.WILLR(high, low, close, timeperiod=14)
df['AD'] = talib.AD(high, low, close, volume)
df['ADOSC'] = talib.ADOSC(high,
low,
close,
volume,
fastperiod=3,
slowperiod=10)
df['OBV'] = talib.OBV(close, volume)
df['ATR'] = talib.ATR(high, low, close, timeperiod=14)
df['NATR'] = talib.NATR(high, low, close, timeperiod=14)
df['TRANGE'] = talib.TRANGE(high, low, close)
df['HT_DCPERIOD'] = talib.HT_DCPERIOD(close)
df['HT_DCPHASE'] = talib.HT_DCPHASE(close)
df['HT_PHASOR_inphase'], df['HT_PHASOR_quadrature'] = talib.HT_PHASOR(
close)
df['HT_SINE_sine'], df['HT_SINE_leadsine'] = talib.HT_SINE(close)
df['HT_TRENDMODE'] = talib.HT_TRENDMODE(close)
df['BETA'] = talib.BETA(high, low, timeperiod=5)
df['CORREL'] = talib.CORREL(high, low, timeperiod=30)
df['LINEARREG'] = talib.LINEARREG(close, timeperiod=14) - close
df['LINEARREG_ANGLE'] = talib.LINEARREG_ANGLE(close, timeperiod=14)
df['LINEARREG_INTERCEPT'] = talib.LINEARREG_INTERCEPT(
close, timeperiod=14) - close
df['LINEARREG_SLOPE'] = talib.LINEARREG_SLOPE(close, timeperiod=14)
df['STDDEV'] = talib.STDDEV(close, timeperiod=5, nbdev=1)
return df
def HT_TRENDMODE(Series):
res = talib.HT_TRENDMODE(Series.values)
return pd.Series(res, index=Series.index)
def calculate(self, para):
self.t = self.inputdata[:, 0]
self.op = self.inputdata[:, 1]
self.high = self.inputdata[:, 2]
self.low = self.inputdata[:, 3]
self.close = self.inputdata[:, 4]
#adjusted close
self.close1 = self.inputdata[:, 5]
self.volume = self.inputdata[:, 6]
#Overlap study
#Overlap Studies
#Overlap Studies
if para is 'BBANDS': #Bollinger Bands
upperband, middleband, lowerband = ta.BBANDS(self.close,
timeperiod=self.tp,
nbdevup=2,
nbdevdn=2,
matype=0)
self.output = [upperband, middleband, lowerband]
elif para is 'DEMA': #Double Exponential Moving Average
self.output = ta.DEMA(self.close, timeperiod=self.tp)
elif para is 'EMA': #Exponential Moving Average
self.output = ta.EMA(self.close, timeperiod=self.tp)
elif para is 'HT_TRENDLINE': #Hilbert Transform - Instantaneous Trendline
self.output = ta.HT_TRENDLINE(self.close)
elif para is 'KAMA': #Kaufman Adaptive Moving Average
self.output = ta.KAMA(self.close, timeperiod=self.tp)
elif para is 'MA': #Moving average
self.output = ta.MA(self.close, timeperiod=self.tp, matype=0)
elif para is 'MAMA': #MESA Adaptive Moving Average
mama, fama = ta.MAMA(self.close, fastlimit=0, slowlimit=0)
elif para is 'MAVP': #Moving average with variable period
self.output = ta.MAVP(self.close,
periods=10,
minperiod=self.tp,
maxperiod=self.tp1,
matype=0)
elif para is 'MIDPOINT': #MidPoint over period
self.output = ta.MIDPOINT(self.close, timeperiod=self.tp)
elif para is 'MIDPRICE': #Midpoint Price over period
self.output = ta.MIDPRICE(self.high, self.low, timeperiod=self.tp)
elif para is 'SAR': #Parabolic SAR
self.output = ta.SAR(self.high,
self.low,
acceleration=0,
maximum=0)
elif para is 'SAREXT': #Parabolic SAR - Extended
self.output = ta.SAREXT(self.high,
self.low,
startvalue=0,
offsetonreverse=0,
accelerationinitlong=0,
accelerationlong=0,
accelerationmaxlong=0,
accelerationinitshort=0,
accelerationshort=0,
accelerationmaxshort=0)
elif para is 'SMA': #Simple Moving Average
self.output = ta.SMA(self.close, timeperiod=self.tp)
elif para is 'T3': #Triple Exponential Moving Average (T3)
self.output = ta.T3(self.close, timeperiod=self.tp, vfactor=0)
elif para is 'TEMA': #Triple Exponential Moving Average
self.output = ta.TEMA(self.close, timeperiod=self.tp)
elif para is 'TRIMA': #Triangular Moving Average
self.output = ta.TRIMA(self.close, timeperiod=self.tp)
elif para is 'WMA': #Weighted Moving Average
self.output = ta.WMA(self.close, timeperiod=self.tp)
#Momentum Indicators
elif para is 'ADX': #Average Directional Movement Index
self.output = ta.ADX(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'ADXR': #Average Directional Movement Index Rating
self.output = ta.ADXR(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'APO': #Absolute Price Oscillator
self.output = ta.APO(self.close,
fastperiod=12,
slowperiod=26,
matype=0)
elif para is 'AROON': #Aroon
aroondown, aroonup = ta.AROON(self.high,
self.low,
timeperiod=self.tp)
self.output = [aroondown, aroonup]
elif para is 'AROONOSC': #Aroon Oscillator
self.output = ta.AROONOSC(self.high, self.low, timeperiod=self.tp)
elif para is 'BOP': #Balance Of Power
self.output = ta.BOP(self.op, self.high, self.low, self.close)
elif para is 'CCI': #Commodity Channel Index
self.output = ta.CCI(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'CMO': #Chande Momentum Oscillator
self.output = ta.CMO(self.close, timeperiod=self.tp)
elif para is 'DX': #Directional Movement Index
self.output = ta.DX(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'MACD': #Moving Average Convergence/Divergence
macd, macdsignal, macdhist = ta.MACD(self.close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
self.output = [macd, macdsignal, macdhist]
elif para is 'MACDEXT': #MACD with controllable MA type
macd, macdsignal, macdhist = ta.MACDEXT(self.close,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
self.output = [macd, macdsignal, macdhist]
elif para is 'MACDFIX': #Moving Average Convergence/Divergence Fix 12/26
macd, macdsignal, macdhist = ta.MACDFIX(self.close, signalperiod=9)
self.output = [macd, macdsignal, macdhist]
elif para is 'MFI': #Money Flow Index
self.output = ta.MFI(self.high,
self.low,
self.close,
self.volume,
timeperiod=self.tp)
elif para is 'MINUS_DI': #Minus Directional Indicator
self.output = ta.MINUS_DI(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'MINUS_DM': #Minus Directional Movement
self.output = ta.MINUS_DM(self.high, self.low, timeperiod=self.tp)
elif para is 'MOM': #Momentum
self.output = ta.MOM(self.close, timeperiod=10)
elif para is 'PLUS_DI': #Plus Directional Indicator
self.output = ta.PLUS_DI(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'PLUS_DM': #Plus Directional Movement
self.output = ta.PLUS_DM(self.high, self.low, timeperiod=self.tp)
elif para is 'PPO': #Percentage Price Oscillator
self.output = ta.PPO(self.close,
fastperiod=12,
slowperiod=26,
matype=0)
elif para is 'ROC': #Rate of change : ((price/prevPrice)-1)*100
self.output = ta.ROC(self.close, timeperiod=10)
elif para is 'ROCP': #Rate of change Percentage: (price-prevPrice)/prevPrice
self.output = ta.ROCP(self.close, timeperiod=10)
elif para is 'ROCR': #Rate of change ratio: (price/prevPrice)
self.output = ta.ROCR(self.close, timeperiod=10)
elif para is 'ROCR100': #Rate of change ratio 100 scale: (price/prevPrice)*100
self.output = ta.ROCR100(self.close, timeperiod=10)
elif para is 'RSI': #Relative Strength Index
self.output = ta.RSI(self.close, timeperiod=self.tp)
elif para is 'STOCH': #Stochastic
slowk, slowd = ta.STOCH(self.high,
self.low,
self.close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
self.output = [slowk, slowd]
elif para is 'STOCHF': #Stochastic Fast
fastk, fastd = ta.STOCHF(self.high,
self.low,
self.close,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
self.output = [fastk, fastd]
elif para is 'STOCHRSI': #Stochastic Relative Strength Index
fastk, fastd = ta.STOCHRSI(self.close,
timeperiod=self.tp,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
self.output = [fastk, fastd]
elif para is 'TRIX': #1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
self.output = ta.TRIX(self.close, timeperiod=self.tp)
elif para is 'ULTOSC': #Ultimate Oscillator
self.output = ta.ULTOSC(self.high,
self.low,
self.close,
timeperiod1=self.tp,
timeperiod2=self.tp1,
timeperiod3=self.tp2)
elif para is 'WILLR': #Williams' %R
self.output = ta.WILLR(self.high,
self.low,
self.close,
timeperiod=self.tp)
# Volume Indicators : #
elif para is 'AD': #Chaikin A/D Line
self.output = ta.AD(self.high, self.low, self.close, self.volume)
elif para is 'ADOSC': #Chaikin A/D Oscillator
self.output = ta.ADOSC(self.high,
self.low,
self.close,
self.volume,
fastperiod=3,
slowperiod=10)
elif para is 'OBV': #On Balance Volume
self.output = ta.OBV(self.close, self.volume)
# Volatility Indicators: #
elif para is 'ATR': #Average True Range
self.output = ta.ATR(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'NATR': #Normalized Average True Range
self.output = ta.NATR(self.high,
self.low,
self.close,
timeperiod=self.tp)
elif para is 'TRANGE': #True Range
self.output = ta.TRANGE(self.high, self.low, self.close)
#Price Transform : #
elif para is 'AVGPRICE': #Average Price
self.output = ta.AVGPRICE(self.op, self.high, self.low, self.close)
elif para is 'MEDPRICE': #Median Price
self.output = ta.MEDPRICE(self.high, self.low)
elif para is 'TYPPRICE': #Typical Price
self.output = ta.TYPPRICE(self.high, self.low, self.close)
elif para is 'WCLPRICE': #Weighted Close Price
self.output = ta.WCLPRICE(self.high, self.low, self.close)
#Cycle Indicators : #
elif para is 'HT_DCPERIOD': #Hilbert Transform - Dominant Cycle Period
self.output = ta.HT_DCPERIOD(self.close)
elif para is 'HT_DCPHASE': #Hilbert Transform - Dominant Cycle Phase
self.output = ta.HT_DCPHASE(self.close)
elif para is 'HT_PHASOR': #Hilbert Transform - Phasor Components
inphase, quadrature = ta.HT_PHASOR(self.close)
self.output = [inphase, quadrature]
elif para is 'HT_SINE': #Hilbert Transform - SineWave #2
sine, leadsine = ta.HT_SINE(self.close)
self.output = [sine, leadsine]
elif para is 'HT_TRENDMODE': #Hilbert Transform - Trend vs Cycle Mode
self.integer = ta.HT_TRENDMODE(self.close)
#Pattern Recognition : #
elif para is 'CDL2CROWS': #Two Crows
self.integer = ta.CDL2CROWS(self.op, self.high, self.low,
self.close)
elif para is 'CDL3BLACKCROWS': #Three Black Crows
self.integer = ta.CDL3BLACKCROWS(self.op, self.high, self.low,
self.close)
elif para is 'CDL3INSIDE': #Three Inside Up/Down
self.integer = ta.CDL3INSIDE(self.op, self.high, self.low,
self.close)
elif para is 'CDL3LINESTRIKE': #Three-Line Strike
self.integer = ta.CDL3LINESTRIKE(self.op, self.high, self.low,
self.close)
elif para is 'CDL3OUTSIDE': #Three Outside Up/Down
self.integer = ta.CDL3OUTSIDE(self.op, self.high, self.low,
self.close)
elif para is 'CDL3STARSINSOUTH': #Three Stars In The South
self.integer = ta.CDL3STARSINSOUTH(self.op, self.high, self.low,
self.close)
elif para is 'CDL3WHITESOLDIERS': #Three Advancing White Soldiers
self.integer = ta.CDL3WHITESOLDIERS(self.op, self.high, self.low,
self.close)
elif para is 'CDLABANDONEDBABY': #Abandoned Baby
self.integer = ta.CDLABANDONEDBABY(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLBELTHOLD': #Belt-hold
self.integer = ta.CDLBELTHOLD(self.op, self.high, self.low,
self.close)
elif para is 'CDLBREAKAWAY': #Breakaway
self.integer = ta.CDLBREAKAWAY(self.op, self.high, self.low,
self.close)
elif para is 'CDLCLOSINGMARUBOZU': #Closing Marubozu
self.integer = ta.CDLCLOSINGMARUBOZU(self.op, self.high, self.low,
self.close)
elif para is 'CDLCONCEALBABYSWALL': #Concealing Baby Swallow
self.integer = ta.CDLCONCEALBABYSWALL(self.op, self.high, self.low,
self.close)
elif para is 'CDLCOUNTERATTACK': #Counterattack
self.integer = ta.CDLCOUNTERATTACK(self.op, self.high, self.low,
self.close)
elif para is 'CDLDARKCLOUDCOVER': #Dark Cloud Cover
self.integer = ta.CDLDARKCLOUDCOVER(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLDOJI': #Doji
self.integer = ta.CDLDOJI(self.op, self.high, self.low, self.close)
elif para is 'CDLDOJISTAR': #Doji Star
self.integer = ta.CDLDOJISTAR(self.op, self.high, self.low,
self.close)
elif para is 'CDLDRAGONFLYDOJI': #Dragonfly Doji
self.integer = ta.CDLDRAGONFLYDOJI(self.op, self.high, self.low,
self.close)
elif para is 'CDLENGULFING': #Engulfing Pattern
self.integer = ta.CDLENGULFING(self.op, self.high, self.low,
self.close)
elif para is 'CDLEVENINGDOJISTAR': #Evening Doji Star
self.integer = ta.CDLEVENINGDOJISTAR(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLEVENINGSTAR': #Evening Star
self.integer = ta.CDLEVENINGSTAR(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLGAPSIDESIDEWHITE': #Up/Down-gap side-by-side white lines
self.integer = ta.CDLGAPSIDESIDEWHITE(self.op, self.high, self.low,
self.close)
elif para is 'CDLGRAVESTONEDOJI': #Gravestone Doji
self.integer = ta.CDLGRAVESTONEDOJI(self.op, self.high, self.low,
self.close)
elif para is 'CDLHAMMER': #Hammer
self.integer = ta.CDLHAMMER(self.op, self.high, self.low,
self.close)
elif para is 'CDLHANGINGMAN': #Hanging Man
self.integer = ta.CDLHANGINGMAN(self.op, self.high, self.low,
self.close)
elif para is 'CDLHARAMI': #Harami Pattern
self.integer = ta.CDLHARAMI(self.op, self.high, self.low,
self.close)
elif para is 'CDLHARAMICROSS': #Harami Cross Pattern
self.integer = ta.CDLHARAMICROSS(self.op, self.high, self.low,
self.close)
elif para is 'CDLHIGHWAVE': #High-Wave Candle
self.integer = ta.CDLHIGHWAVE(self.op, self.high, self.low,
self.close)
elif para is 'CDLHIKKAKE': #Hikkake Pattern
self.integer = ta.CDLHIKKAKE(self.op, self.high, self.low,
self.close)
elif para is 'CDLHIKKAKEMOD': #Modified Hikkake Pattern
self.integer = ta.CDLHIKKAKEMOD(self.op, self.high, self.low,
self.close)
elif para is 'CDLHOMINGPIGEON': #Homing Pigeon
self.integer = ta.CDLHOMINGPIGEON(self.op, self.high, self.low,
self.close)
elif para is 'CDLIDENTICAL3CROWS': #Identical Three Crows
self.integer = ta.CDLIDENTICAL3CROWS(self.op, self.high, self.low,
self.close)
elif para is 'CDLINNECK': #In-Neck Pattern
self.integer = ta.CDLINNECK(self.op, self.high, self.low,
self.close)
elif para is 'CDLINVERTEDHAMMER': #Inverted Hammer
self.integer = ta.CDLINVERTEDHAMMER(self.op, self.high, self.low,
self.close)
elif para is 'CDLKICKING': #Kicking
self.integer = ta.CDLKICKING(self.op, self.high, self.low,
self.close)
elif para is 'CDLKICKINGBYLENGTH': #Kicking - bull/bear determined by the longer marubozu
self.integer = ta.CDLKICKINGBYLENGTH(self.op, self.high, self.low,
self.close)
elif para is 'CDLLADDERBOTTOM': #Ladder Bottom
self.integer = ta.CDLLADDERBOTTOM(self.op, self.high, self.low,
self.close)
elif para is 'CDLLONGLEGGEDDOJI': #Long Legged Doji
self.integer = ta.CDLLONGLEGGEDDOJI(self.op, self.high, self.low,
self.close)
elif para is 'CDLLONGLINE': #Long Line Candle
self.integer = ta.CDLLONGLINE(self.op, self.high, self.low,
self.close)
elif para is 'CDLMARUBOZU': #Marubozu
self.integer = ta.CDLMARUBOZU(self.op, self.high, self.low,
self.close)
elif para is 'CDLMATCHINGLOW': #Matching Low
self.integer = ta.CDLMATCHINGLOW(self.op, self.high, self.low,
self.close)
elif para is 'CDLMATHOLD': #Mat Hold
self.integer = ta.CDLMATHOLD(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLMORNINGDOJISTAR': #Morning Doji Star
self.integer = ta.CDLMORNINGDOJISTAR(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLMORNINGSTAR': #Morning Star
self.integer = ta.CDLMORNINGSTAR(self.op,
self.high,
self.low,
self.close,
penetration=0)
elif para is 'CDLONNECK': #On-Neck Pattern
self.integer = ta.CDLONNECK(self.op, self.high, self.low,
self.close)
elif para is 'CDLPIERCING': #Piercing Pattern
self.integer = ta.CDLPIERCING(self.op, self.high, self.low,
self.close)
elif para is 'CDLRICKSHAWMAN': #Rickshaw Man
self.integer = ta.CDLRICKSHAWMAN(self.op, self.high, self.low,
self.close)
elif para is 'CDLRISEFALL3METHODS': #Rising/Falling Three Methods
self.integer = ta.CDLRISEFALL3METHODS(self.op, self.high, self.low,
self.close)
elif para is 'CDLSEPARATINGLINES': #Separating Lines
self.integer = ta.CDLSEPARATINGLINES(self.op, self.high, self.low,
self.close)
elif para is 'CDLSHOOTINGSTAR': #Shooting Star
self.integer = ta.CDLSHOOTINGSTAR(self.op, self.high, self.low,
self.close)
elif para is 'CDLSHORTLINE': #Short Line Candle
self.integer = ta.CDLSHORTLINE(self.op, self.high, self.low,
self.close)
elif para is 'CDLSPINNINGTOP': #Spinning Top
self.integer = ta.CDLSPINNINGTOP(self.op, self.high, self.low,
self.close)
elif para is 'CDLSTALLEDPATTERN': #Stalled Pattern
self.integer = ta.CDLSTALLEDPATTERN(self.op, self.high, self.low,
self.close)
elif para is 'CDLSTICKSANDWICH': #Stick Sandwich
self.integer = ta.CDLSTICKSANDWICH(self.op, self.high, self.low,
self.close)
elif para is 'CDLTAKURI': #Takuri (Dragonfly Doji with very long lower shadow)
self.integer = ta.CDLTAKURI(self.op, self.high, self.low,
self.close)
elif para is 'CDLTASUKIGAP': #Tasuki Gap
self.integer = ta.CDLTASUKIGAP(self.op, self.high, self.low,
self.close)
elif para is 'CDLTHRUSTING': #Thrusting Pattern
self.integer = ta.CDLTHRUSTING(self.op, self.high, self.low,
self.close)
elif para is 'CDLTRISTAR': #Tristar Pattern
self.integer = ta.CDLTRISTAR(self.op, self.high, self.low,
self.close)
elif para is 'CDLUNIQUE3RIVER': #Unique 3 River
self.integer = ta.CDLUNIQUE3RIVER(self.op, self.high, self.low,
self.close)
elif para is 'CDLUPSIDEGAP2CROWS': #Upside Gap Two Crows
self.integer = ta.CDLUPSIDEGAP2CROWS(self.op, self.high, self.low,
self.close)
elif para is 'CDLXSIDEGAP3METHODS': #Upside/Downside Gap Three Methods
self.integer = ta.CDLXSIDEGAP3METHODS(self.op, self.high, self.low,
self.close)
#Statistic Functions : #
elif para is 'BETA': #Beta
self.output = ta.BETA(self.high, self.low, timeperiod=5)
elif para is 'CORREL': #Pearson's Correlation Coefficient (r)
self.output = ta.CORREL(self.high, self.low, timeperiod=self.tp)
elif para is 'LINEARREG': #Linear Regression
self.output = ta.LINEARREG(self.close, timeperiod=self.tp)
elif para is 'LINEARREG_ANGLE': #Linear Regression Angle
self.output = ta.LINEARREG_ANGLE(self.close, timeperiod=self.tp)
elif para is 'LINEARREG_INTERCEPT': #Linear Regression Intercept
self.output = ta.LINEARREG_INTERCEPT(self.close,
timeperiod=self.tp)
elif para is 'LINEARREG_SLOPE': #Linear Regression Slope
self.output = ta.LINEARREG_SLOPE(self.close, timeperiod=self.tp)
elif para is 'STDDEV': #Standard Deviation
self.output = ta.STDDEV(self.close, timeperiod=5, nbdev=1)
elif para is 'TSF': #Time Series Forecast
self.output = ta.TSF(self.close, timeperiod=self.tp)
elif para is 'VAR': #Variance
self.output = ta.VAR(self.close, timeperiod=5, nbdev=1)
else:
print('You issued command:' + para)
def get_datasets(asset, currency, granularity, datapoints):
"""Fetch the API and precess the desired pair
Arguments:
asset {str} -- First pair
currency {str} -- Second pair
granularity {str ['day', 'hour']} -- Granularity
datapoints {int [100 - 2000]} -- [description]
Returns:
pandas.Dataframe -- The OHLCV and indicators dataframe
"""
df_train_path = 'datasets/bot_train_{}_{}_{}.csv'.format(
asset + currency, datapoints, granularity)
df_rollout_path = 'datasets/bot_rollout_{}_{}_{}.csv'.format(
asset + currency, datapoints, granularity)
emojis = [
':moneybag:', ':yen:', ':dollar:', ':pound:', ':euro:',
':credit_card:', ':money_with_wings:', ':gem:'
]
if not os.path.exists(df_rollout_path):
headers = {
'User-Agent':
'Mozilla/5.0',
'authorization':
'Apikey 3d7d3e9e6006669ac00584978342451c95c3c78421268ff7aeef69995f9a09ce'
}
# OHLC
# url = 'https://min-api.cryptocompare.com/data/histo{}?fsym={}&tsym={}&e=Binance&limit={}'.format(granularity, asset, currency, datapoints)
url = 'https://min-api.cryptocompare.com/data/histo{}?fsym={}&tsym={}&limit={}'.format(
granularity, asset, currency, datapoints)
# print(emoji.emojize(':dizzy: :large_blue_diamond: :gem: :bar_chart: :crystal_ball: :chart_with_downwards_trend: :chart_with_upwards_trend: :large_orange_diamond: loading...', use_aliases=True))
print(
colored(
emoji.emojize('> ' + random.choice(emojis) + ' downloading ' +
asset + '/' + currency,
use_aliases=True), 'green'))
# print(colored('> downloading ' + asset + '/' + currency, 'green'))
response = requests.get(url, headers=headers)
json_response = response.json()
status = json_response['Response']
if status == "Error":
print(colored('=== {} ==='.format(json_response['Message']),
'red'))
raise AssertionError()
result = json_response['Data']
df = pd.DataFrame(result)
print(df.tail())
df['Date'] = pd.to_datetime(df['time'], utc=True, unit='s')
df.drop('time', axis=1, inplace=True)
# indicators
# https://github.com/mrjbq7/ta-lib/blob/master/docs/func.md
open_price, high, low, close = np.array(df['open']), np.array(
df['high']), np.array(df['low']), np.array(df['close'])
volume = np.array(df['volumefrom'])
# cycle indicators
df.loc[:, 'HT_DCPERIOD'] = talib.HT_DCPERIOD(close)
df.loc[:, 'HT_DCPHASE'] = talib.HT_DCPHASE(close)
df.loc[:,
'HT_PHASOR_inphase'], df.loc[:,
'HT_PHASOR_quadrature'] = talib.HT_PHASOR(
close)
df.loc[:, 'HT_SINE_sine'], df.loc[:,
'HT_SINE_leadsine'] = talib.HT_SINE(
close)
df.loc[:, 'HT_TRENDMODE'] = talib.HT_TRENDMODE(close)
# momemtum indicators
df.loc[:, 'ADX'] = talib.ADX(high, low, close, timeperiod=12)
df.loc[:, 'ADXR'] = talib.ADXR(high, low, close, timeperiod=13)
df.loc[:, 'APO'] = talib.APO(close,
fastperiod=5,
slowperiod=10,
matype=0)
df.loc[:,
'AROON_down'], df.loc[:,
'AROON_up'] = talib.AROON(high,
low,
timeperiod=15)
df.loc[:, 'AROONOSC'] = talib.AROONOSC(high, low, timeperiod=13)
df.loc[:, 'BOP'] = talib.BOP(open_price, high, low, close)
df.loc[:, 'CCI'] = talib.CCI(high, low, close, timeperiod=13)
df.loc[:, 'CMO'] = talib.CMO(close, timeperiod=14)
df.loc[:, 'DX'] = talib.DX(high, low, close, timeperiod=10)
df['MACD'], df['MACD_signal'], df['MACD_hist'] = talib.MACD(
close, fastperiod=5, slowperiod=10, signalperiod=20)
df.loc[:, 'MFI'] = talib.MFI(high, low, close, volume, timeperiod=12)
df.loc[:, 'MINUS_DI'] = talib.MINUS_DI(high, low, close, timeperiod=10)
df.loc[:, 'MINUS_DM'] = talib.MINUS_DM(high, low, timeperiod=14)
df.loc[:, 'MOM'] = talib.MOM(close, timeperiod=20)
df.loc[:, 'PPO'] = talib.PPO(close,
fastperiod=17,
slowperiod=35,
matype=2)
df.loc[:, 'ROC'] = talib.ROC(close, timeperiod=12)
df.loc[:, 'RSI'] = talib.RSI(close, timeperiod=25)
df.loc[:, 'STOCH_k'], df.loc[:,
'STOCH_d'] = talib.STOCH(high,
low,
close,
fastk_period=35,
slowk_period=12,
slowk_matype=0,
slowd_period=7,
slowd_matype=0)
df.loc[:,
'STOCHF_k'], df.loc[:,
'STOCHF_d'] = talib.STOCHF(high,
low,
close,
fastk_period=28,
fastd_period=14,
fastd_matype=0)
df.loc[:, 'STOCHRSI_K'], df.loc[:, 'STOCHRSI_D'] = talib.STOCHRSI(
close,
timeperiod=35,
fastk_period=12,
fastd_period=10,
fastd_matype=1)
df.loc[:, 'TRIX'] = talib.TRIX(close, timeperiod=30)
df.loc[:, 'ULTOSC'] = talib.ULTOSC(high,
low,
close,
timeperiod1=14,
timeperiod2=28,
timeperiod3=35)
df.loc[:, 'WILLR'] = talib.WILLR(high, low, close, timeperiod=35)
# overlap studies
df.loc[:,
'BBANDS_upper'], df.loc[:,
'BBANDS_middle'], df.loc[:,
'BBANDS_lower'] = talib.BBANDS(
close,
timeperiod=
12,
nbdevup=2,
nbdevdn=2,
matype=0)
df.loc[:, 'DEMA'] = talib.DEMA(close, timeperiod=30)
df.loc[:, 'EMA'] = talib.EMA(close, timeperiod=7)
df.loc[:, 'HT_TRENDLINE'] = talib.HT_TRENDLINE(close)
df.loc[:, 'KAMA'] = talib.KAMA(close, timeperiod=5)
df.loc[:, 'MA'] = talib.MA(close, timeperiod=5, matype=0)
df.loc[:, 'MIDPOINT'] = talib.MIDPOINT(close, timeperiod=20)
df.loc[:, 'WMA'] = talib.WMA(close, timeperiod=15)
df.loc[:, 'SMA'] = talib.SMA(close)
# pattern recoginition
df.loc[:, 'CDL2CROWS'] = talib.CDL2CROWS(open_price, high, low, close)
df.loc[:, 'CDL3BLACKCROWS'] = talib.CDL3BLACKCROWS(
open_price, high, low, close)
df.loc[:, 'CDL3INSIDE'] = talib.CDL3INSIDE(open_price, high, low,
close)
df.loc[:, 'CDL3LINESTRIKE'] = talib.CDL3LINESTRIKE(
open_price, high, low, close)
# price transform
df.loc[:, 'WCLPRICE'] = talib.WCLPRICE(high, low, close)
# statistic funcitons
df.loc[:, 'BETA'] = talib.BETA(high, low, timeperiod=20)
df.loc[:, 'CORREL'] = talib.CORREL(high, low, timeperiod=20)
df.loc[:, 'STDDEV'] = talib.STDDEV(close, timeperiod=20, nbdev=1)
df.loc[:, 'TSF'] = talib.TSF(close, timeperiod=20)
df.loc[:, 'VAR'] = talib.VAR(close, timeperiod=20, nbdev=1)
# volatility indicators
df.loc[:, 'ATR'] = talib.ATR(high, low, close, timeperiod=7)
df.loc[:, 'NATR'] = talib.NATR(high, low, close, timeperiod=20)
df.loc[:, 'TRANGE'] = talib.TRANGE(high, low, close)
# volume indicators
df.loc[:, 'AD'] = talib.AD(high, low, close, volume)
df.loc[:, 'ADOSC'] = talib.ADOSC(high,
low,
close,
volume,
fastperiod=10,
slowperiod=20)
df.loc[:, 'OBV'] = talib.OBV(close, volume)
# df.fillna(df.mean(), inplace=True)
df.dropna(inplace=True)
df.set_index('Date', inplace=True)
print(colored('> caching' + asset + '/' + currency + ':)', 'cyan'))
train_size = round(
len(df) *
DF_TRAIN_SIZE) # 75% to train -> test with different value
df_train = df[:train_size]
df_rollout = df[train_size:]
df_train.to_csv(df_train_path)
df_rollout.to_csv(df_rollout_path)
df_train = pd.read_csv(
df_train_path) # re-read to avoid indexing issue w/ Ray
df_rollout = pd.read_csv(df_rollout_path)
else:
print(
colored(
emoji.emojize('> ' + random.choice(emojis) + ' feching ' +
asset + '/' + currency + ' from cache',
use_aliases=True), 'magenta'))
# print(colored('> feching ' + asset + '/' + currency + ' from cache :)', 'magenta'))
df_train = pd.read_csv(df_train_path)
df_rollout = pd.read_csv(df_rollout_path)
# df_train.set_index('Date', inplace=True)
# df_rollout.set_index('Date', inplace=True)
return df_train, df_rollout
def TALIB_HT_TRENDMODE(close):
'''00346,1,1'''
return talib.HT_TRENDMODE(close)
def add_ta_features(df, ta_settings):
"""Add technial analysis features from typical financial dataset that
typically include columns such as "open", "high", "low", "price" and
"volume".
http://mrjbq7.github.io/ta-lib/
Args:
df(pandas.DataFrame): original DataFrame.
ta_settings(dict): configuration.
Returns:
pandas.DataFrame: DataFrame with new features included.
"""
open = df['open']
high = df['high']
low = df['low']
close = df['price']
volume = df['volume']
if ta_settings['overlap']:
df['ta_overlap_bbands_upper'], df['ta_overlap_bbands_middle'], df[
'ta_overlap_bbands_lower'] = ta.BBANDS(close,
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)
df['ta_overlap_dema'] = ta.DEMA(
close, timeperiod=15) # NOTE: Changed to avoid a lot of Nan values
df['ta_overlap_ema'] = ta.EMA(close, timeperiod=30)
df['ta_overlap_kama'] = ta.KAMA(close, timeperiod=30)
df['ta_overlap_ma'] = ta.MA(close, timeperiod=30, matype=0)
df['ta_overlap_mama_mama'], df['ta_overlap_mama_fama'] = ta.MAMA(close)
period = np.random.randint(10, 20, size=len(close)).astype(float)
df['ta_overlap_mavp'] = ta.MAVP(close,
period,
minperiod=2,
maxperiod=30,
matype=0)
df['ta_overlap_midpoint'] = ta.MIDPOINT(close, timeperiod=14)
df['ta_overlap_midprice'] = ta.MIDPRICE(high, low, timeperiod=14)
df['ta_overlap_sar'] = ta.SAR(high, low, acceleration=0, maximum=0)
df['ta_overlap_sarext'] = ta.SAREXT(high,
low,
startvalue=0,
offsetonreverse=0,
accelerationinitlong=0,
accelerationlong=0,
accelerationmaxlong=0,
accelerationinitshort=0,
accelerationshort=0,
accelerationmaxshort=0)
df['ta_overlap_sma'] = ta.SMA(close, timeperiod=30)
df['ta_overlap_t3'] = ta.T3(close, timeperiod=5, vfactor=0)
df['ta_overlap_tema'] = ta.TEMA(
close, timeperiod=12) # NOTE: Changed to avoid a lot of Nan values
df['ta_overlap_trima'] = ta.TRIMA(close, timeperiod=30)
df['ta_overlap_wma'] = ta.WMA(close, timeperiod=30)
# NOTE: Commented to avoid a lot of Nan values
# df['ta_overlap_ht_trendline'] = ta.HT_TRENDLINE(close)
if ta_settings['momentum']:
df['ta_momentum_adx'] = ta.ADX(high, low, close, timeperiod=14)
df['ta_momentum_adxr'] = ta.ADXR(high, low, close, timeperiod=14)
df['ta_momentum_apo'] = ta.APO(close,
fastperiod=12,
slowperiod=26,
matype=0)
df['ta_momentum_aroondown'], df['ta_momentum_aroonup'] = ta.AROON(
high, low, timeperiod=14)
df['ta_momentum_aroonosc'] = ta.AROONOSC(high, low, timeperiod=14)
df['ta_momentum_bop'] = ta.BOP(open, high, low, close)
df['ta_momentum_cci'] = ta.CCI(high, low, close, timeperiod=14)
df['ta_momentum_cmo'] = ta.CMO(close, timeperiod=14)
df['ta_momentum_dx'] = ta.DX(high, low, close, timeperiod=14)
df['ta_momentum_macd_macd'], df['ta_momentum_macd_signal'], df[
'ta_momentum_macd_hist'] = ta.MACD(close,
fastperiod=12,
slowperiod=26,
signalperiod=9)
df['ta_momentum_macdext_macd'], df['ta_momentum_macdext_signal'], df[
'ta_momentum_macdext_hist'] = ta.MACDEXT(close,
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
df['ta_momentum_macdfix_macd'], df['ta_momentum_macdfix_signal'], df[
'ta_momentum_macdfix_hist'] = ta.MACDFIX(close, signalperiod=9)
df['ta_momentum_mfi'] = ta.MFI(high, low, close, volume, timeperiod=14)
df['ta_momentum_minus_di'] = ta.MINUS_DI(high,
low,
close,
timeperiod=14)
df['ta_momentum_minus_dm'] = ta.MINUS_DM(high, low, timeperiod=14)
df['ta_momentum_mom'] = ta.MOM(close, timeperiod=10)
df['ta_momentum_plus_di'] = ta.PLUS_DI(high, low, close, timeperiod=14)
df['ta_momentum_plus_dm'] = ta.PLUS_DM(high, low, timeperiod=14)
df['ta_momentum_ppo'] = ta.PPO(close,
fastperiod=12,
slowperiod=26,
matype=0)
df['ta_momentum_roc'] = ta.ROC(close, timeperiod=10)
df['ta_momentum_rocp'] = ta.ROCP(close, timeperiod=10)
df['ta_momentum_rocr'] = ta.ROCR(close, timeperiod=10)
df['ta_momentum_rocr100'] = ta.ROCR100(close, timeperiod=10)
df['ta_momentum_rsi'] = ta.RSI(close, timeperiod=14)
df['ta_momentum_slowk'], df['ta_momentum_slowd'] = ta.STOCH(
high,
low,
close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
df['ta_momentum_fastk'], df['ta_momentum_fastd'] = ta.STOCHF(
high, low, close, fastk_period=5, fastd_period=3, fastd_matype=0)
df['ta_momentum_fastk'], df['ta_momentum_fastd'] = ta.STOCHRSI(
close,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
df['ta_momentum_trix'] = ta.TRIX(
close, timeperiod=12) # NOTE: Changed to avoid a lot of Nan values
df['ta_momentum_ultosc'] = ta.ULTOSC(high,
low,
close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
df['ta_momentum_willr'] = ta.WILLR(high, low, close, timeperiod=14)
if ta_settings['volume']:
df['ta_volume_ad'] = ta.AD(high, low, close, volume)
df['ta_volume_adosc'] = ta.ADOSC(high,
low,
close,
volume,
fastperiod=3,
slowperiod=10)
df['ta_volume_obv'] = ta.OBV(close, volume)
if ta_settings['volatility']:
df['ta_volatility_atr'] = ta.ATR(high, low, close, timeperiod=14)
df['ta_volatility_natr'] = ta.NATR(high, low, close, timeperiod=14)
df['ta_volatility_trange'] = ta.TRANGE(high, low, close)
if ta_settings['price']:
df['ta_price_avgprice'] = ta.AVGPRICE(open, high, low, close)
df['ta_price_medprice'] = ta.MEDPRICE(high, low)
df['ta_price_typprice'] = ta.TYPPRICE(high, low, close)
df['ta_price_wclprice'] = ta.WCLPRICE(high, low, close)
if ta_settings['cycle']:
df['ta_cycle_ht_dcperiod'] = ta.HT_DCPERIOD(close)
df['ta_cycle_ht_phasor_inphase'], df[
'ta_cycle_ht_phasor_quadrature'] = ta.HT_PHASOR(close)
df['ta_cycle_ht_trendmode'] = ta.HT_TRENDMODE(close)
# NOTE: Commented to avoid a lot of Nan values
# df['ta_cycle_ht_dcphase'] = ta.HT_DCPHASE(close)
# df['ta_cycle_ht_sine_sine'], df['ta_cycle_ht_sine_leadsine'] = ta.HT_SINE(close)
if ta_settings['pattern']:
df['ta_pattern_cdl2crows'] = ta.CDL2CROWS(open, high, low, close)
df['ta_pattern_cdl3blackrows'] = ta.CDL3BLACKCROWS(
open, high, low, close)
df['ta_pattern_cdl3inside'] = ta.CDL3INSIDE(open, high, low, close)
df['ta_pattern_cdl3linestrike'] = ta.CDL3LINESTRIKE(
open, high, low, close)
df['ta_pattern_cdl3outside'] = ta.CDL3OUTSIDE(open, high, low, close)
df['ta_pattern_cdl3starsinsouth'] = ta.CDL3STARSINSOUTH(
open, high, low, close)
df['ta_pattern_cdl3whitesoldiers'] = ta.CDL3WHITESOLDIERS(
open, high, low, close)
df['ta_pattern_cdlabandonedbaby'] = ta.CDLABANDONEDBABY(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdladvanceblock'] = ta.CDLADVANCEBLOCK(
open, high, low, close)
df['ta_pattern_cdlbelthold'] = ta.CDLBELTHOLD(open, high, low, close)
df['ta_pattern_cdlbreakaway'] = ta.CDLBREAKAWAY(open, high, low, close)
df['ta_pattern_cdlclosingmarubozu'] = ta.CDLCLOSINGMARUBOZU(
open, high, low, close)
df['ta_pattern_cdlconcealbabyswall'] = ta.CDLCONCEALBABYSWALL(
open, high, low, close)
df['ta_pattern_cdlcounterattack'] = ta.CDLCOUNTERATTACK(
open, high, low, close)
df['ta_pattern_cdldarkcloudcover'] = ta.CDLDARKCLOUDCOVER(
open, high, low, close, penetration=0)
df['ta_pattern_cdldoji'] = ta.CDLDOJI(open, high, low, close)
df['ta_pattern_cdldojistar'] = ta.CDLDOJISTAR(open, high, low, close)
df['ta_pattern_cdldragonflydoji'] = ta.CDLDRAGONFLYDOJI(
open, high, low, close)
df['ta_pattern_cdlengulfing'] = ta.CDLENGULFING(open, high, low, close)
df['ta_pattern_cdleveningdojistar'] = ta.CDLEVENINGDOJISTAR(
open, high, low, close, penetration=0)
df['ta_pattern_cdleveningstar'] = ta.CDLEVENINGSTAR(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdlgapsidesidewhite'] = ta.CDLGAPSIDESIDEWHITE(
open, high, low, close)
df['ta_pattern_cdlgravestonedoji'] = ta.CDLGRAVESTONEDOJI(
open, high, low, close)
df['ta_pattern_cdlhammer'] = ta.CDLHAMMER(open, high, low, close)
df['ta_pattern_cdlhangingman'] = ta.CDLHANGINGMAN(
open, high, low, close)
df['ta_pattern_cdlharami'] = ta.CDLHARAMI(open, high, low, close)
df['ta_pattern_cdlharamicross'] = ta.CDLHARAMICROSS(
open, high, low, close)
df['ta_pattern_cdlhighwave'] = ta.CDLHIGHWAVE(open, high, low, close)
df['ta_pattern_cdlhikkake'] = ta.CDLHIKKAKE(open, high, low, close)
df['ta_pattern_cdlhikkakemod'] = ta.CDLHIKKAKEMOD(
open, high, low, close)
df['ta_pattern_cdlhomingpigeon'] = ta.CDLHOMINGPIGEON(
open, high, low, close)
df['ta_pattern_cdlidentical3crows'] = ta.CDLIDENTICAL3CROWS(
open, high, low, close)
df['ta_pattern_cdlinneck'] = ta.CDLINNECK(open, high, low, close)
df['ta_pattern_cdlinvertedhammer'] = ta.CDLINVERTEDHAMMER(
open, high, low, close)
df['ta_pattern_cdlkicking'] = ta.CDLKICKING(open, high, low, close)
df['ta_pattern_cdlkickingbylength'] = ta.CDLKICKINGBYLENGTH(
open, high, low, close)
df['ta_pattern_cdlladderbottom'] = ta.CDLLADDERBOTTOM(
open, high, low, close)
df['ta_pattern_cdllongleggeddoji'] = ta.CDLLONGLEGGEDDOJI(
open, high, low, close)
df['ta_pattern_cdllongline'] = ta.CDLLONGLINE(open, high, low, close)
df['ta_pattern_cdlmarubozu'] = ta.CDLMARUBOZU(open, high, low, close)
df['ta_pattern_cdlmatchinglow'] = ta.CDLMATCHINGLOW(
open, high, low, close)
df['ta_pattern_cdlmathold'] = ta.CDLMATHOLD(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdlmorningdojistar'] = ta.CDLMORNINGDOJISTAR(
open, high, low, close, penetration=0)
df['ta_pattern_cdlmorningstar'] = ta.CDLMORNINGSTAR(open,
high,
low,
close,
penetration=0)
df['ta_pattern_cdllonneck'] = ta.CDLONNECK(open, high, low, close)
df['ta_pattern_cdlpiercing'] = ta.CDLPIERCING(open, high, low, close)
df['ta_pattern_cdlrickshawman'] = ta.CDLRICKSHAWMAN(
open, high, low, close)
df['ta_pattern_cdlrisefall3methods'] = ta.CDLRISEFALL3METHODS(
open, high, low, close)
df['ta_pattern_cdlseparatinglines'] = ta.CDLSEPARATINGLINES(
open, high, low, close)
df['ta_pattern_cdlshootingstar'] = ta.CDLSHOOTINGSTAR(
open, high, low, close)
df['ta_pattern_cdlshortline'] = ta.CDLSHORTLINE(open, high, low, close)
df['ta_pattern_cdlspinningtop'] = ta.CDLSPINNINGTOP(
open, high, low, close)
df['ta_pattern_cdlstalledpattern'] = ta.CDLSTALLEDPATTERN(
open, high, low, close)
df['ta_pattern_cdlsticksandwich'] = ta.CDLSTICKSANDWICH(
open, high, low, close)
df['ta_pattern_cdltakuri'] = ta.CDLTAKURI(open, high, low, close)
df['ta_pattern_cdltasukigap'] = ta.CDLTASUKIGAP(open, high, low, close)
df['ta_pattern_cdlthrusting'] = ta.CDLTHRUSTING(open, high, low, close)
df['ta_pattern_cdltristar'] = ta.CDLTRISTAR(open, high, low, close)
df['ta_pattern_cdlunique3river'] = ta.CDLUNIQUE3RIVER(
open, high, low, close)
df['ta_pattern_cdlupsidegap2crows'] = ta.CDLUPSIDEGAP2CROWS(
open, high, low, close)
df['ta_pattern_cdlxsidegap3methods'] = ta.CDLXSIDEGAP3METHODS(
open, high, low, close)
if ta_settings['statistic']:
df['ta_statistic_beta'] = ta.BETA(high, low, timeperiod=5)
df['ta_statistic_correl'] = ta.CORREL(high, low, timeperiod=30)
df['ta_statistic_linearreg'] = ta.LINEARREG(close, timeperiod=14)
df['ta_statistic_linearreg_angle'] = ta.LINEARREG_ANGLE(close,
timeperiod=14)
df['ta_statistic_linearreg_intercept'] = ta.LINEARREG_INTERCEPT(
close, timeperiod=14)
df['ta_statistic_linearreg_slope'] = ta.LINEARREG_SLOPE(close,
timeperiod=14)
df['ta_statistic_stddev'] = ta.STDDEV(close, timeperiod=5, nbdev=1)
df['ta_statistic_tsf'] = ta.TSF(close, timeperiod=14)
df['ta_statistic_var'] = ta.VAR(close, timeperiod=5, nbdev=1)
if ta_settings['math_transforms']:
df['ta_math_transforms_atan'] = ta.ATAN(close)
df['ta_math_transforms_ceil'] = ta.CEIL(close)
df['ta_math_transforms_cos'] = ta.COS(close)
df['ta_math_transforms_floor'] = ta.FLOOR(close)
df['ta_math_transforms_ln'] = ta.LN(close)
df['ta_math_transforms_log10'] = ta.LOG10(close)
df['ta_math_transforms_sin'] = ta.SIN(close)
df['ta_math_transforms_sqrt'] = ta.SQRT(close)
df['ta_math_transforms_tan'] = ta.TAN(close)
if ta_settings['math_operators']:
df['ta_math_operators_add'] = ta.ADD(high, low)
df['ta_math_operators_div'] = ta.DIV(high, low)
df['ta_math_operators_min'], df['ta_math_operators_max'] = ta.MINMAX(
close, timeperiod=30)
df['ta_math_operators_minidx'], df[
'ta_math_operators_maxidx'] = ta.MINMAXINDEX(close, timeperiod=30)
df['ta_math_operators_mult'] = ta.MULT(high, low)
df['ta_math_operators_sub'] = ta.SUB(high, low)
df['ta_math_operators_sum'] = ta.SUM(close, timeperiod=30)
return df
def get_talib_stock_daily(
stock_code,
s,
e,
append_ori_close=False,
norms=['volume', 'amount', 'ht_dcphase', 'obv', 'adosc', 'ad', 'cci']):
"""获取经过talib处理后的股票日线数据"""
stock_data = QA.QA_fetch_stock_day_adv(stock_code, s, e)
stock_df = stock_data.to_qfq().data
if append_ori_close:
stock_df['o_close'] = stock_data.data['close']
# stock_df['high_qfq'] = stock_data.to_qfq().data['high']
# stock_df['low_hfq'] = stock_data.to_hfq().data['low']
close = np.array(stock_df['close'])
high = np.array(stock_df['high'])
low = np.array(stock_df['low'])
_open = np.array(stock_df['open'])
_volume = np.array(stock_df['volume'])
stock_df['dema'] = talib.DEMA(close)
stock_df['ema'] = talib.EMA(close)
stock_df['ht_tradeline'] = talib.HT_TRENDLINE(close)
stock_df['kama'] = talib.KAMA(close)
stock_df['ma'] = talib.MA(close)
stock_df['mama'], stock_df['fama'] = talib.MAMA(close)
# MAVP
stock_df['midpoint'] = talib.MIDPOINT(close)
stock_df['midprice'] = talib.MIDPRICE(high, low)
stock_df['sar'] = talib.SAR(high, low)
stock_df['sarext'] = talib.SAREXT(high, low)
stock_df['sma'] = talib.SMA(close)
stock_df['t3'] = talib.T3(close)
stock_df['tema'] = talib.TEMA(close)
stock_df['trima'] = talib.TRIMA(close)
stock_df['wma'] = talib.WMA(close)
stock_df['adx'] = talib.ADX(high, low, close)
stock_df['adxr'] = talib.ADXR(high, low, close)
stock_df['apo'] = talib.APO(close)
stock_df['aroondown'], stock_df['aroonup'] = talib.AROON(high, low)
stock_df['aroonosc'] = talib.AROONOSC(high, low)
stock_df['bop'] = talib.BOP(_open, high, low, close)
stock_df['cci'] = talib.CCI(high, low, close)
stock_df['cmo'] = talib.CMO(close)
stock_df['dx'] = talib.DX(high, low, close)
# MACD
stock_df['macd'], stock_df['macdsignal'], stock_df[
'macdhist'] = talib.MACDEXT(close)
# MACDFIX
stock_df['mfi'] = talib.MFI(high, low, close, _volume)
stock_df['minus_di'] = talib.MINUS_DI(high, low, close)
stock_df['minus_dm'] = talib.MINUS_DM(high, low)
stock_df['mom'] = talib.MOM(close)
stock_df['plus_di'] = talib.PLUS_DI(high, low, close)
stock_df['plus_dm'] = talib.PLUS_DM(high, low)
stock_df['ppo'] = talib.PPO(close)
stock_df['roc'] = talib.ROC(close)
stock_df['rocp'] = talib.ROCP(close)
stock_df['rocr'] = talib.ROCR(close)
stock_df['rocr100'] = talib.ROCR100(close)
stock_df['rsi'] = talib.RSI(close)
stock_df['slowk'], stock_df['slowd'] = talib.STOCH(high, low, close)
stock_df['fastk'], stock_df['fastd'] = talib.STOCHF(high, low, close)
# STOCHRSI - Stochastic Relative Strength Index
stock_df['trix'] = talib.TRIX(close)
stock_df['ultosc'] = talib.ULTOSC(high, low, close)
stock_df['willr'] = talib.WILLR(high, low, close)
stock_df['ad'] = talib.AD(high, low, close, _volume)
stock_df['adosc'] = talib.ADOSC(high, low, close, _volume)
stock_df['obv'] = talib.OBV(close, _volume)
stock_df['ht_dcperiod'] = talib.HT_DCPERIOD(close)
stock_df['ht_dcphase'] = talib.HT_DCPHASE(close)
stock_df['inphase'], stock_df['quadrature'] = talib.HT_PHASOR(close)
stock_df['sine'], stock_df['leadsine'] = talib.HT_PHASOR(close)
stock_df['ht_trendmode'] = talib.HT_TRENDMODE(close)
stock_df['avgprice'] = talib.AVGPRICE(_open, high, low, close)
stock_df['medprice'] = talib.MEDPRICE(high, low)
stock_df['typprice'] = talib.TYPPRICE(high, low, close)
stock_df['wclprice'] = talib.WCLPRICE(high, low, close)
stock_df['atr'] = talib.ATR(high, low, close)
stock_df['natr'] = talib.NATR(high, low, close)
stock_df['trange'] = talib.TRANGE(high, low, close)
stock_df['beta'] = talib.BETA(high, low)
stock_df['correl'] = talib.CORREL(high, low)
stock_df['linearreg'] = talib.LINEARREG(close)
stock_df['linearreg_angle'] = talib.LINEARREG_ANGLE(close)
stock_df['linearreg_intercept'] = talib.LINEARREG_INTERCEPT(close)
stock_df['linearreg_slope'] = talib.LINEARREG_SLOPE(close)
stock_df['stddev'] = talib.STDDEV(close)
stock_df['tsf'] = talib.TSF(close)
stock_df['var'] = talib.VAR(close)
stock_df = stock_df.reset_index().set_index('date')
if norms:
x = stock_df[norms].values # returns a numpy array
x_scaled = MinMaxScaler().fit_transform(x)
stock_df = stock_df.drop(columns=norms).join(
pd.DataFrame(x_scaled, columns=norms, index=stock_df.index))
# stock_df = stock_df.drop(columns=['code', 'open', 'high', 'low'])
stock_df = stock_df.dropna()
stock_df = stock_df.drop(columns=['code'])
return stock_df
np.array(df['Adj Close']))
df['Upside_Gap_Two_Crows'] = ta.CDLUPSIDEGAP2CROWS(np.array(df['Open']),
np.array(df['High']),
np.array(df['Low']),
np.array(df['Adj Close']))
df['Upside_Downside_Gap_Three_Methods'] = ta.CDLXSIDEGAP3METHODS(
np.array(df['Open']), np.array(df['High']), np.array(df['Low']),
np.array(df['Adj Close']))
# Cycle Indicator Functions
df['HT_DCPERIOD'] = ta.HT_DCPERIOD(np.array(df['Adj Close'].shift(1)))
df['HT_DCPHASE'] = ta.HT_DCPHASE(np.array(df['Adj Close'].shift(1)))
df['inphase'], df['quadrature'] = ta.HT_PHASOR(
np.array(df['Adj Close'].shift(1)))
df['sine'], df['leadsine'] = ta.HT_SINE(np.array(df['Adj Close'].shift(1)))
df['HT_TRENDMODE'] = ta.HT_TRENDMODE(np.array(df['Adj Close'].shift(1)))
df['ATR1'] = abs(np.array(df['High'].shift(1)) - np.array(df['Low'].shift(1)))
df['ATR2'] = abs(
np.array(df['High'].shift(1)) - np.array(df['Adj Close'].shift(1)))
df['ATR3'] = abs(
np.array(df['Low'].shift(1)) - np.array(df['Adj Close'].shift(1)))
df['AverageTrueRange'] = df[['ATR1', 'ATR2', 'ATR3']].max(axis=1)
# df['EMA']=pd.Series(pd.ewma(df['Adj Close'], span = n, min_periods = n - 1))
# Statistic Functions
df['Beta'] = ta.BETA(np.array(df['High'].shift(1)),
np.array(df['Low'].shift(1)),
timeperiod=n)
df['CORREL'] = ta.CORREL(np.array(df['High'].shift(1)),
lbls_train = lbls.iloc[0:round(len(lbls) * 0.8), :]
lbls_test = lbls.iloc[round(len(lbls) * 0.8):, :]
cols = list(stock)[0:13]
#Preprocess data
stock = stock[cols].astype(str)
for i in cols:
for j in range(0, len(stock)):
stock[i][j] = stock[i][j].replace(",", "")
stock = stock.astype(float)
periods = np.double(np.array(list(range(0, len(stock['Close'])))))
HT = talib.HT_TRENDMODE(stock['Close'])
rsi = talib.RSI(stock['Close'], timeperiod=5)
wma = talib.WMA(stock['Close'], timeperiod=20)
mavp = talib.MAVP(stock['Close'], periods, minperiod=2, maxperiod=30, matype=0)
upperband, middleband, lowerband = talib.BBANDS(stock['Close'],
timeperiod=10,
nbdevup=2,
nbdevdn=2,
matype=0)
Roc = talib.ROC(stock['Close'], timeperiod=5)
Atr = talib.ATR(stock['High'], stock['Low'], stock['Close'], timeperiod=10)
div = stock.Close - wma
voldiff = stock.Volume.diff()
VolROC = (stock.Volume - stock.Volume.shift(1)) / stock.Volume
opendiff = stock.Open - stock.Open.shift(1)
def third_making_indicator_5min(dataframe):
Open_lis = np.array(dataframe['open'], dtype='float')
High_lis = np.array(dataframe['high'], dtype='float')
Low_lis = np.array(dataframe['low'], dtype='float')
Clz_lis = np.array(dataframe['weigted_price'], dtype='float')
Vol_lis = np.array(dataframe['volume'], dtype='float')
##지표##
SMA_3_C = ta.SMA(Clz_lis, timeperiod=3)
SMA_5_H = ta.SMA(High_lis, timeperiod=5)
SMA_5_L = ta.SMA(Low_lis, timeperiod=5)
SMA_5_C = ta.SMA(Clz_lis, timeperiod=5)
SMA_10_H = ta.SMA(High_lis, timeperiod=10)
SMA_10_L = ta.SMA(Low_lis, timeperiod=10)
SMA_10_C = ta.SMA(Clz_lis, timeperiod=10)
RSI_2_C = ta.RSI(Clz_lis, timeperiod=2)
RSI_3_C = ta.RSI(Clz_lis, timeperiod=3)
RSI_5_C = ta.RSI(Clz_lis, timeperiod=5)
RSI_7_H = ta.RSI(High_lis, timeperiod=7)
RSI_7_L = ta.RSI(Low_lis, timeperiod=7)
RSI_7_C = ta.RSI(Clz_lis, timeperiod=7)
RSI_14_H = ta.RSI(High_lis, timeperiod=14)
RSI_14_L = ta.RSI(Low_lis, timeperiod=14)
RSI_14_C = ta.RSI(Clz_lis, timeperiod=14)
ADX = ta.ADX(High_lis, Low_lis, Clz_lis, timeperiod=14)
ADXR = ta.ADXR(High_lis, Low_lis, Clz_lis, timeperiod=14)
Aroondown, Aroonup = ta.AROON(High_lis, Low_lis, timeperiod=14)
Aroonosc = ta.AROONOSC(High_lis, Low_lis, timeperiod=14)
BOP = ta.BOP(Open_lis, High_lis, Low_lis, Clz_lis)
CMO = ta.CMO(Clz_lis, timeperiod=14)
DX = ta.DX(High_lis, Low_lis, Clz_lis, timeperiod=14)
MFI = ta.MFI(High_lis, Low_lis, Clz_lis, Vol_lis, timeperiod=14)
MINUS_DI = ta.MINUS_DI(High_lis, Low_lis, Clz_lis, timeperiod=14)
PLUSDI = ta.PLUS_DI(High_lis, Low_lis, Clz_lis, timeperiod=14)
PPO = ta.PPO(Clz_lis, fastperiod=12, slowperiod=26, matype=0)
ROC = ta.ROC(Clz_lis, timeperiod=10)
ROCP = ta.ROCP(Clz_lis, timeperiod=10)
ROCR = ta.ROCR(Clz_lis, timeperiod=10)
ROCR100 = ta.ROCR100(Clz_lis, timeperiod=10)
Slowk, Slowd = ta.STOCH(High_lis,
Low_lis,
Clz_lis,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
STOCHF_f, STOCHF_d = ta.STOCHF(High_lis,
Low_lis,
Clz_lis,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
Fastk, Fastd = ta.STOCHRSI(Clz_lis,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
TRIX = ta.TRIX(Clz_lis, timeperiod=30)
ULTOSC = ta.ULTOSC(High_lis,
Low_lis,
Clz_lis,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
WILLR = ta.WILLR(High_lis, Low_lis, Clz_lis, timeperiod=14)
ADOSC = ta.ADOSC(High_lis,
Low_lis,
Clz_lis,
Vol_lis,
fastperiod=3,
slowperiod=10)
NATR = ta.NATR(High_lis, Low_lis, Clz_lis, timeperiod=14)
HT_DCPERIOD = ta.HT_DCPERIOD(Clz_lis)
sine, leadsine = ta.HT_SINE(Clz_lis)
integer = ta.HT_TRENDMODE(Clz_lis)
########
#append
dataframe['SMA_3_C'] = SMA_3_C
dataframe['SMA_5_H'] = SMA_5_H
dataframe['SMA_5_L'] = SMA_5_L
dataframe['SMA_5_C'] = SMA_5_C
dataframe['SMA_10_H'] = SMA_10_H
dataframe['SMA_10_L'] = SMA_10_L
dataframe['SMA_10_C'] = SMA_10_C
dataframe['RSI_2_C'] = (RSI_2_C / 100.) * 2 - 1.
dataframe['RSI_3_C'] = (RSI_3_C / 100.) * 2 - 1.
dataframe['RSI_5_C'] = (RSI_5_C / 100.) * 2 - 1.
dataframe['RSI_7_H'] = (RSI_7_H / 100.) * 2 - 1.
dataframe['RSI_7_L'] = (RSI_7_L / 100.) * 2 - 1.
dataframe['RSI_7_C'] = (RSI_7_C / 100.) * 2 - 1.
dataframe['RSI_14_H'] = (RSI_14_H / 100.) * 2 - 1.
dataframe['RSI_14_L'] = (RSI_14_L / 100.) * 2 - 1.
dataframe['RSI_14_C'] = (RSI_14_C / 100.) * 2 - 1.
dataframe['ADX'] = (ADX / 100.) * 2 - 1.
dataframe['ADXR'] = (ADXR / 100.) * 2 - 1.
dataframe['Aroondown'] = (Aroondown / 100.) * 2 - 1
dataframe['Aroonup'] = (Aroonup / 100.) * 2 - 1
dataframe['Aroonosc'] = Aroonosc / 100.
dataframe['BOP'] = BOP
dataframe['CMO'] = CMO / 100.
dataframe['DX'] = (DX / 100.) * 2 - 1
dataframe['MFI'] = (MFI / 100.) * 2 - 1
dataframe['MINUS_DI'] = (MINUS_DI / 100.) * 2 - 1
dataframe['PLUSDI'] = (PLUSDI / 100.) * 2 - 1
dataframe['PPO'] = PPO / 10.
dataframe['ROC'] = ROC / 10.
dataframe['ROCP'] = ROCP * 10
dataframe['ROCR'] = (ROCR - 1.0) * 100.
dataframe['ROCR100'] = ((ROCR100 / 100.) - 1.0) * 100.
dataframe['Slowk'] = (Slowk / 100.) * 2 - 1
dataframe['Slowd'] = (Slowd / 100.) * 2 - 1
dataframe['STOCHF_f'] = (STOCHF_f / 100.) * 2 - 1
dataframe['STOCHF_d'] = (STOCHF_d / 100.) * 2 - 1
dataframe['Fastk'] = (Fastk / 100.) * 2 - 1
dataframe['Fastd'] = (Fastd / 100.) * 2 - 1
dataframe['TRIX'] = TRIX * 10.
dataframe['ULTOSC'] = (ULTOSC / 100.) * 2 - 1
dataframe['WILLR'] = (WILLR / 100.) * 2 + 1
dataframe['ADOSC'] = ADOSC / 100.
dataframe['NATR'] = NATR * 2 - 1
dataframe['HT_DCPERIOD'] = (HT_DCPERIOD / 100.) * 2 - 1
dataframe['sine'] = sine
dataframe['leadsine'] = leadsine
dataframe['integer'] = integer
return dataframe
def HT_TRENDMODE(data, **kwargs):
_check_talib_presence()
prices = _extract_series(data)
return talib.HT_TRENDMODE(prices, **kwargs)
def create_tas(bars,
verbose=False,
ohlcv_cols=['Adj_High', 'Adj_Low', 'Adj_Open', 'Adj_Close', 'Adj_Volume'],
return_df=False,
cl=True,
tp=True):
"""
This is basically set up for daily stock data. Other time frames would need adapting probably.
:param bars: resampled pandas dataframe with open, high, low, close, volume, and typical_price columns
:param verbose: boolean, if true, prints more debug
:param ohlcv_cols: list of strings; the column names for high, low, open, close, and volume
:param cl: use the close price to make TAs
:param tp: calcuclate typical price and use it for TAs
:returns: pandas dataframe with TA signals calculated (modifies dataframe in place)
"""
h, l, o, c, v = ohlcv_cols
if 'typical_price' not in bars.columns and tp:
bars['typical_price'] = bars[[h, l, c]].mean(axis=1)
# bollinger bands
# strange bug, if values are small, need to multiply to larger value for some reason
mult = 1
last_close = bars.iloc[0][c]
lc_m = last_close * mult
while lc_m < 1:
mult *= 10
lc_m = last_close * mult
if verbose:
print('using multiplier of', mult)
if tp:
mult_tp = bars['typical_price'].values * mult
mult_close = bars[c].values * mult
mult_open = bars[o].values * mult
mult_high = bars[h].values * mult
mult_low = bars[l].values * mult
# for IB data, the volume is integer, but needs to be float for talib
volume = bars[v].astype('float').values
# ADR - average daily range
# http://forextraininggroup.com/using-adr-average-daily-range-find-short-term-trading-opportunities/
# TODO
### overlap studies
# bollinger bands -- should probably put these into another indicator
if cl:
upper_cl, middle_cl, lower_cl = talib.BBANDS(mult_close,
timeperiod=10,
nbdevup=2,
nbdevdn=2)
bars['bband_u_cl'] = upper_cl / mult
bars['bband_m_cl'] = middle_cl / mult
bars['bband_l_cl'] = lower_cl / mult
bars['bband_u_cl_diff'] = bars['bband_u_cl'] - bars[c]
bars['bband_m_cl_diff'] = bars['bband_m_cl'] - bars[c]
bars['bband_l_cl_diff'] = bars['bband_l_cl'] - bars[c]
bars['bband_u_cl_diff_hi'] = bars['bband_u_cl'] - bars[h]
bars['bband_l_cl_diff_lo'] = bars['bband_l_cl'] - bars[l]
# bars['bband_u_cl'].fillna(method='bfill', inplace=True)
# bars['bband_m_cl'].fillna(method='bfill', inplace=True)
# bars['bband_l_cl'].fillna(method='bfill', inplace=True)
if tp:
upper_tp, middle_tp, lower_tp = talib.BBANDS(mult_tp,
timeperiod=10,
nbdevup=2,
nbdevdn=2)
bars['bband_u_tp'] = upper_tp / mult
bars['bband_m_tp'] = middle_tp / mult
bars['bband_l_tp'] = lower_tp / mult
bars['bband_u_tp_diff'] = bars['bband_u_tp'] - bars['typical_price']
bars['bband_m_tp_diff'] = bars['bband_m_tp'] - bars['typical_price']
bars['bband_l_tp_diff'] = bars['bband_l_tp'] - bars['typical_price']
bars['bband_u_tp_diff_hi'] = bars['bband_u_tp'] - bars[h]
bars['bband_l_tp_diff_lo'] = bars['bband_l_tp'] - bars[l]
# think this is already taken care of at the end...check
# bars['bband_u_tp'].fillna(method='bfill', inplace=True)
# bars['bband_m_tp'].fillna(method='bfill', inplace=True)
# bars['bband_l_tp'].fillna(method='bfill', inplace=True)
# Double Exponential Moving Average
if cl:
bars['dema_cl'] = talib.DEMA(mult_close, timeperiod=30) / mult
bars['dema_cl_diff'] = bars['dema_cl'] - bars[c]
if tp:
bars['dema_tp'] = talib.DEMA(mult_tp, timeperiod=30) / mult
bars['dema_tp_diff'] = bars['dema_tp'] - bars['typical_price']
# exponential moving Average
if cl:
bars['ema_cl'] = talib.EMA(mult_close, timeperiod=30) / mult
bars['ema_cl_diff'] = bars['ema_cl'] - bars[c]
if tp:
bars['ema_tp'] = talib.EMA(mult_tp, timeperiod=30) / mult
bars['ema_tp_diff'] = bars['ema_tp'] - bars['typical_price']
# Hilbert Transform - Instantaneous Trendline - like a mva but a bit different, should probably take slope or
# use in another indicator
if cl:
bars['ht_tl_cl'] = talib.HT_TRENDLINE(mult_close) / mult
bars['ht_tl_cl_diff'] = bars['ht_tl_cl'] - bars[c]
if tp:
bars['ht_tl_tp'] = talib.HT_TRENDLINE(mult_tp) / mult
bars['ht_tl_tp_diff'] = bars['ht_tl_tp'] - bars['typical_price']
# KAMA - Kaufman's Adaptative Moving Average -- need to take slope or something
if cl:
bars['kama_cl'] = talib.KAMA(mult_close, timeperiod=30) / mult
bars['kama_cl_diff'] = bars['kama_cl'] - bars[c]
if tp:
bars['kama_tp'] = talib.KAMA(mult_tp, timeperiod=30) / mult
bars['kama_tp_diff'] = bars['kama_tp'] - bars['typical_price']
# MESA Adaptive Moving Average -- getting TA_BAD_PARAM error
# mama_cl, fama_cl = talib.MAMA(mult_close, fastlimit=100, slowlimit=100) / mult
# mama_tp, fama_tp = talib.MAMA(mult_tp, fastlimit=100, slowlimit=100) / mult
# mama_cl_osc = (mama_cl - fama_cl) / mama_cl
# mama_tp_osc = (mama_tp - fama_tp) / mama_tp
# bars['mama_cl'] = mama_cl
# bars['mama_tp'] = mama_tp
# bars['fama_cl'] = fama_cl
# bars['fama_tp'] = fama_tp
# bars['mama_cl_osc'] = mama_cl_osc
# bars['mama_tp_osc'] = mama_tp_osc
# Moving average with variable period
if cl:
bars['mavp_cl'] = talib.MAVP(mult_close, np.arange(mult_close.shape[0]).astype(np.float64), minperiod=2, maxperiod=30, matype=0) / mult
bars['mavp_cl_diff'] = bars['mavp_cl'] - bars[c]
if tp:
bars['mavp_tp'] = talib.MAVP(mult_tp, np.arange(mult_tp.shape[0]).astype(np.float64), minperiod=2, maxperiod=30, matype=0) / mult
bars['mavp_tp_diff'] = bars['mavp_tp'] - bars['typical_price']
# midpoint over period
if cl:
bars['midp_cl'] = talib.MIDPOINT(mult_close, timeperiod=14) / mult
bars['midp_cl_diff'] = bars['midp_cl'] - bars[c]
if tp:
bars['midp_tp'] = talib.MIDPOINT(mult_tp, timeperiod=14) / mult
bars['midp_tp_diff'] = bars['midp_tp'] - bars['typical_price']
# midpoint price over period
bars['midpr'] = talib.MIDPRICE(mult_high, mult_low, timeperiod=14) / mult
if cl:
bars['midpr_diff_cl'] = bars['midpr'] - bars[c]
if tp:
bars['midpr_diff_tp'] = bars['midpr'] - bars['typical_price']
# parabolic sar
bars['sar'] = talib.SAR(mult_high, mult_low, acceleration=0.02, maximum=0.2) / mult
if cl:
bars['sar_diff_cl'] = bars['sar'] - bars[c]
if tp:
bars['sar_diff_tp'] = bars['sar'] - bars['typical_price']
# need to make an oscillator for this
# simple moving average
# 10, 20, 30, 40 day
if cl:
bars['sma_10_cl'] = talib.SMA(mult_close, timeperiod=10) / mult
bars['sma_20_cl'] = talib.SMA(mult_close, timeperiod=20) / mult
bars['sma_30_cl'] = talib.SMA(mult_close, timeperiod=30) / mult
bars['sma_40_cl'] = talib.SMA(mult_close, timeperiod=40) / mult
if tp:
bars['sma_10_tp'] = talib.SMA(mult_tp, timeperiod=10) / mult
bars['sma_20_tp'] = talib.SMA(mult_tp, timeperiod=20) / mult
bars['sma_30_tp'] = talib.SMA(mult_tp, timeperiod=30) / mult
bars['sma_40_tp'] = talib.SMA(mult_tp, timeperiod=40) / mult
# triple exponential moving average
if cl:
bars['tema_cl'] = talib.TEMA(mult_close, timeperiod=30) / mult
bars['tema_cl_diff'] = bars['tema_cl'] - bars[c]
if tp:
bars['tema_tp'] = talib.TEMA(mult_tp, timeperiod=30) / mult
bars['tema_tp_diff'] = bars['tema_tp'] - bars['typical_price']
# triangular ma
if cl:
bars['trima_cl'] = talib.TRIMA(mult_close, timeperiod=30) / mult
bars['trima_cl_diff'] = bars['trima_cl'] - bars[c]
if tp:
bars['trima_tp'] = talib.TRIMA(mult_tp, timeperiod=30) / mult
bars['trima_tp_diff'] = bars['trima_tp'] - bars['typical_price']
# weighted moving average
if cl:
bars['wma_cl'] = talib.WMA(mult_close, timeperiod=30) / mult
bars['wma_cl_diff'] = bars['wma_cl'] - bars[c]
if tp:
bars['wma_tp'] = talib.WMA(mult_tp, timeperiod=30) / mult
bars['wma_tp_diff'] = bars['wma_tp'] - bars['typical_price']
#### momentum indicators -- for now left out some of those with unstable periods (maybe update and included them, not sure)
# Average Directional Movement Index - 0 to 100 I think
bars['adx_14'] = talib.ADX(mult_high, mult_low, mult_close, timeperiod=14)
bars['adx_5'] = talib.ADX(mult_high, mult_low, mult_close, timeperiod=5)
# Average Directional Movement Index Rating
bars['adxr'] = talib.ADXR(mult_high, mult_low, mult_close, timeperiod=14)
# Absolute Price Oscillator
# values around -100 to +100
if cl:
bars['apo_cl'] = talib.APO(mult_close, fastperiod=12, slowperiod=26, matype=0)
if tp:
bars['apo_tp'] = talib.APO(mult_tp, fastperiod=12, slowperiod=26, matype=0)
# Aroon and Aroon Oscillator 0-100, so don't need to renormalize
arup, ardn = talib.AROON(mult_high, mult_low, timeperiod=14)
bars['arup'] = arup
bars['ardn'] = ardn
# linearly related to aroon, just aroon up - aroon down
bars['aroonosc'] = talib.AROONOSC(mult_high, mult_low, timeperiod=14)
# balance of power - ratio of values so don't need to re-normalize
bars['bop'] = talib.BOP(mult_open, mult_high, mult_low, mult_close)
# Commodity Channel Index
# around -100 to + 100
bars['cci'] = talib.CCI(mult_high, mult_low, mult_close, timeperiod=14)
# Chande Momentum Oscillator
if cl:
bars['cmo_cl'] = talib.CMO(mult_close, timeperiod=14)
if tp:
bars['cmo_tp'] = talib.CMO(mult_tp, timeperiod=14)
# dx - Directional Movement Index
bars['dx'] = talib.DX(mult_high, mult_low, mult_close, timeperiod=14)
# Moving Average Convergence/Divergence
# https://www.quantopian.com/posts/how-does-the-talib-compute-macd-why-the-value-is-different
# macd diff btw fast and slow EMA
if cl:
macd_cl, macdsignal_cl, macdhist_cl = talib.MACD(mult_close, fastperiod=12, slowperiod=26, signalperiod=9)
bars['macd_cl'] = macd_cl / mult
bars['macdsignal_cl'] = macdsignal_cl / mult
bars['macdhist_cl'] = macdhist_cl / mult
if tp:
macd_tp, macdsignal_tp, macdhist_tp = talib.MACD(mult_tp, fastperiod=12, slowperiod=26, signalperiod=9)
bars['macd_tp'] = macd_tp / mult
bars['macdsignal_tp'] = macdsignal_tp / mult
bars['macdhist_tp'] = macdhist_tp / mult
# mfi - Money Flow Index
bars['mfi'] = talib.MFI(mult_high, mult_low, mult_close, volume, timeperiod=14)
# minus di - Minus Directional Indicator
bars['mdi'] = talib.MINUS_DI(mult_high, mult_low, mult_close, timeperiod=14)
# Minus Directional Movement
bars['mdm'] = talib.MINUS_DM(mult_high, mult_low, timeperiod=14)
# note: too small of a timeperiod will result in junk data...I think. or at least very discretized
if cl:
bars['mom_cl'] = talib.MOM(mult_close, timeperiod=14) / mult
# bars['mom_cl'].fillna(method='bfill', inplace=True)
if tp:
bars['mom_tp'] = talib.MOM(mult_tp, timeperiod=14) / mult
# bars['mom_tp'].fillna(method='bfill', inplace=True)
# plus di - Plus Directional Indicator
bars['pldi'] = talib.PLUS_DI(mult_high, mult_low, mult_close, timeperiod=14)
# Plus Directional Movement
bars['pldm'] = talib.PLUS_DM(mult_high, mult_low, timeperiod=14)
# percentage price Oscillator
# matype explanation: https://www.quantopian.com/posts/moving-averages
if cl:
bars['ppo_cl'] = talib.PPO(mult_close, fastperiod=12, slowperiod=26, matype=1)
if bars['ppo_cl'].isnull().all():
bars['ppo_cl_signal'] = 0
else:
bars['ppo_cl_signal'] = talib.EMA(bars['ppo_cl'].bfill().values, timeperiod=9)
if tp:
bars['ppo_tp'] = talib.PPO(mult_tp, fastperiod=12, slowperiod=26, matype=1)
# rate of change -- really only need one
# if cl:
# bars['roc_cl'] = talib.ROC(mult_close, timeperiod=10)
#
# if tp:
# bars['roc_tp'] = talib.ROC(mult_tp, timeperiod=10)
# rocp - Rate of change Percentage: (price-prevPrice)/prevPrice
if cl:
bars['rocp_cl'] = talib.ROCP(mult_close, timeperiod=10)
if tp:
bars['rocp_tp'] = talib.ROCP(mult_tp, timeperiod=10)
# rocr - Rate of change ratio: (price/prevPrice)
# bars['rocr_cl'] = talib.ROCR(mult_close, timeperiod=10)
# bars['rocr_tp'] = talib.ROCR(mult_tp, timeperiod=10)
#
# # Rate of change ratio 100 scale: (price/prevPrice)*100
# bars['rocr_cl_100'] = talib.ROCR100(mult_close, timeperiod=10)
# bars['rocr_tp_100'] = talib.ROCR100(mult_tp, timeperiod=10)
# Relative Strength Index
if cl:
bars['rsi_cl_14'] = talib.RSI(mult_close, timeperiod=14)
bars['rsi_cl_5'] = talib.RSI(mult_close, timeperiod=5)
if tp:
bars['rsi_tp'] = talib.RSI(mult_tp, timeperiod=14)
# stochastic oscillator - % of price diffs, so no need to rescale
slowk, slowd = talib.STOCH(mult_high, mult_low, mult_close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
fastk, fastd = talib.STOCHF(mult_high, mult_low, mult_close, fastk_period=5, fastd_period=3, fastd_matype=0)
bars['slowk'] = slowk
bars['slowd'] = slowd
bars['fastk'] = fastk
bars['fastd'] = fastd
# Stochastic Relative Strength Index
if cl:
fastk_cl, fastd_cl = talib.STOCHRSI(mult_close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0)
bars['strsi_cl_k'] = fastk_cl
bars['strsi_cl_d'] = fastd_cl
if tp:
fastk_tp, fastd_tp = talib.STOCHRSI(mult_tp, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0)
bars['strsi_tp_k'] = fastk_tp
bars['strsi_tp_d'] = fastd_tp
# trix - 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA
if cl:
if bars.shape[0] > 90:
bars['trix_cl_30'] = talib.TRIX(mult_close, timeperiod=30)
bars['trix_cl_30_signal'] = talib.EMA(bars['trix_cl_30'].bfill().values, timeperiod=20)
bars['trix_cl_14'] = talib.TRIX(mult_close, timeperiod=14)
if bars['trix_cl_14'].isnull().all():
bars['trix_cl_14_signal'] = 0
else:
bars['trix_cl_14_signal'] = talib.EMA(bars['trix_cl_14'].bfill().values, timeperiod=9)
bars['trix_cl_12'] = talib.TRIX(mult_close, timeperiod=12)
if bars['trix_cl_12'].isnull().all():
bars['trix_cl_12_signal'] = 0
else:
bars['trix_cl_12_signal'] = talib.EMA(bars['trix_cl_12'].bfill().values, timeperiod=9)
bars['trix_cl_5'] = talib.TRIX(mult_close, timeperiod=5)
if bars['trix_cl_5'].isnull().all():
bars['trix_cl_5_signal'] = 0
else:
bars['trix_cl_5_signal'] = talib.EMA(bars['trix_cl_5'].bfill().values, timeperiod=3)
if tp:
bars['trix_tp'] = talib.TRIX(mult_tp, timeperiod=30)
# ultimate Oscillator - between 0 and 100
bars['ultosc'] = talib.ULTOSC(mult_high, mult_low, mult_close, timeperiod1=7, timeperiod2=14, timeperiod3=28)
# williams % r -- 0 to 100
bars['willr'] = talib.WILLR(mult_high, mult_low, mult_close, timeperiod=14)
### volume indicators
# Chaikin A/D Line
bars['ad'] = talib.AD(mult_high, mult_low, mult_close, volume)
# Chaikin A/D Oscillator
bars['adosc'] = talib.ADOSC(mult_high, mult_low, mult_close, volume, fastperiod=3, slowperiod=10)
# on balance volume
if cl:
bars['obv_cl'] = talib.OBV(mult_close, volume)
if bars['obv_cl'].isnull().all():
bars['obv_cl_ema_14'] = 0
else:
bars['obv_cl_ema_14'] = talib.EMA(bars['obv_cl'].values, timeperiod=14)
if tp:
bars['obv_tp'] = talib.OBV(mult_tp, volume)
### volatility indicators
# average true range
# Large or increasing ranges suggest traders prepared to continue to bid up or sell down a stock through the course of the day. Decreasing range suggests waning interest.
# https://en.wikipedia.org/wiki/Average_true_range
bars['atr_65'] = talib.ATR(mult_high, mult_low, mult_close, timeperiod=65)
bars['atr_20'] = talib.ATR(mult_high, mult_low, mult_close, timeperiod=20)
bars['atr_14'] = talib.ATR(mult_high, mult_low, mult_close, timeperiod=14)
bars['atr_5'] = talib.ATR(mult_high, mult_low, mult_close, timeperiod=5)
# Normalized Average True Range
bars['natr_14'] = talib.NATR(mult_high, mult_low, mult_close, timeperiod=14)
bars['natr_5'] = talib.NATR(mult_high, mult_low, mult_close, timeperiod=5)
# true range
bars['trange'] = talib.TRANGE(mult_high, mult_low, mult_close) / mult
### Cycle indicators
# Hilbert Transform - Dominant Cycle Period
if cl:
bars['ht_dcp_cl'] = talib.HT_DCPERIOD(mult_close)
if tp:
bars['ht_dcp_tp'] = talib.HT_DCPERIOD(mult_tp)
# Hilbert Transform - Dominant Cycle Phase
if cl:
bars['ht_dcph_cl'] = talib.HT_DCPHASE(mult_close)
if tp:
bars['ht_dcph_tp'] = talib.HT_DCPHASE(mult_tp)
# Hilbert Transform - Phasor Components
if cl:
inphase_cl, quadrature_cl = talib.HT_PHASOR(mult_close)
bars['ht_ph_cl'] = inphase_cl
bars['ht_q_cl'] = quadrature_cl
if tp:
inphase_tp, quadrature_tp = talib.HT_PHASOR(mult_tp)
bars['ht_ph_tp'] = inphase_tp
bars['ht_q_tp'] = quadrature_tp
# Hilbert Transform - SineWave
if cl:
sine_cl, leadsine_cl = talib.HT_SINE(mult_close)
bars['ht_s_cl'] = sine_cl
bars['ht_ls_cl'] = leadsine_cl
if tp:
sine_tp, leadsine_tp = talib.HT_SINE(mult_tp)
bars['ht_s_tp'] = sine_tp
bars['ht_ls_tp'] = leadsine_tp
# Hilbert Transform - Trend vs Cycle Mode
if cl:
bars['ht_tr_cl'] = talib.HT_TRENDMODE(mult_close)
if tp:
bars['ht_tr_tp'] = talib.HT_TRENDMODE(mult_tp)
bars.fillna(method='bfill', inplace=True)
if return_df:
return bars
