def generate_tech_data(p1_df, p2_df):
sample = pd.DataFrame(
{
'p1': p1_df.values.ravel(),
'p2': p2_df.values.ravel()
},
index=p1_df.index)
p1 = p1_df.values.ravel()
p2 = p2_df.values.ravel()
sample['p1' + '_mom'] = talib.MOM(p1)
sample['p1' +
'_macd'], sample['p1' +
'_macd_sig'], sample['p1' +
'_macd_hist'] = talib.MACD(p1)
sample['p1' + '_rsi'] = talib.RSI(p1, timeperiod=10)
sample['p1' + '_cmo'] = talib.CMO(p1)
sample['p2' + '_mom'] = talib.MOM(p2)
sample['p2' +
'_macd'], sample['p2' +
'_macd_sig'], sample['p2' +
'_macd_hist'] = talib.MACD(p2)
sample['p2' + '_rsi'] = talib.RSI(p2, timeperiod=10)
sample['p2' + '_cmo'] = talib.CMO(p2)
spread = p1 / p2
sample['spread'] = spread
sample['diff'] = sample['spread'] / sample['spread'].shift(1)
sample = sample.dropna()
return sample
def computeCMO(self):
scmo = talib.CMO(self.prices, timeperiod=self.slowPeriod)
fcmo = talib.CMO(self.prices, timeperiod=self.fastPeriod)
cmo = fcmo - scmo
cmo = scmo
self.removeNullID(cmo)
self.rawFeatures['CMO'] = cmo
return
def cmo(candles: np.ndarray,
period: int = 14,
source_type: str = "close",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
CMO - Chande Momentum Oscillator
:param candles: np.ndarray
:param period: int - default=14
:param source_type: str - default: "close"
:param sequential: bool - default=False
:return: float | 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.CMO(source, timeperiod=period)
if sequential:
return res
else:
return None if np.isnan(res[-1]) else res[-1]
def generate_feature(data):
high = data.High.values
low = data.Low.values
close = data.Close.values
feature_df = pd.DataFrame(index=data.index)
feature_df["ADX"] = ADX = talib.ADX(high, low, close, timeperiod=14)
feature_df["ADXR"] = ADXR = talib.ADXR(high, low, close, timeperiod=14)
feature_df["APO"] = APO = talib.APO(close, fastperiod=12, slowperiod=26, matype=0)
feature_df["AROONOSC"] = AROONOSC = talib.AROONOSC(high, low, timeperiod=14)
feature_df["CCI"] = CCI = talib.CCI(high, low, close, timeperiod=14)
feature_df["CMO"] = CMO = talib.CMO(close, timeperiod=14)
feature_df["DX"] = DX = talib.DX(high, low, close, timeperiod=14)
feature_df["MINUS_DI"] = MINUS_DI = talib.MINUS_DI(high, low, close, timeperiod=14)
feature_df["MINUS_DM"] = MINUS_DM = talib.MINUS_DM(high, low, timeperiod=14)
feature_df["MOM"] = MOM = talib.MOM(close, timeperiod=10)
feature_df["PLUS_DI"] = PLUS_DI = talib.PLUS_DI(high, low, close, timeperiod=14)
feature_df["PLUS_DM"] = PLUS_DM = talib.PLUS_DM(high, low, timeperiod=14)
feature_df["PPO"] = PPO = talib.PPO(close, fastperiod=12, slowperiod=26, matype=0)
feature_df["ROC"] = ROC = talib.ROC(close, timeperiod=10)
feature_df["ROCP"] = ROCP = talib.ROCP(close, timeperiod=10)
feature_df["ROCR100"] = ROCR100 = talib.ROCR100(close, timeperiod=10)
feature_df["RSI"] = RSI = talib.RSI(close, timeperiod=14)
feature_df["ULTOSC"] = ULTOSC = talib.ULTOSC(high, low, close, timeperiod1=7, timeperiod2=14, timeperiod3=28)
feature_df["WILLR"] = WILLR = talib.WILLR(high, low, close, timeperiod=14)
feature_df = feature_df.fillna(0.0)
matrix = np.stack((
ADX, ADXR, APO, AROONOSC, CCI, CMO, DX, MINUS_DI, ROCR100, ROC,
MINUS_DM, MOM, PLUS_DI, PLUS_DM, PPO, ROCP, WILLR, ULTOSC, RSI))
matrix = np.nan_to_num(matrix)
matrix = matrix.transpose()
return feature_df, matrix
def SourceData(SourceStart, SourceEnd):
Data1 = {
"close " : quandl.get("LBMA/GOLD", start_date=SourceStart, end_date=SourceEnd)["USD (PM)"].dropna(),
}
Data2 = {
"MA " : talib.MA(Data1["close "], timeperiod=30, matype=0),
"EMA " : talib.EMA(Data1["close "], timeperiod=30),
"CMO " : talib.CMO(Data1["close "], timeperiod=14),
"MACD1 " : talib.MACD(Data1["close "], fastperiod=12, slowperiod=26, signalperiod=9)[0],
"MACD2 " : talib.MACD(Data1["close "], fastperiod=12, slowperiod=26, signalperiod=9)[1],
"MACD3 " : talib.MACD(Data1["close "], fastperiod=12, slowperiod=26, signalperiod=9)[2],
}
Data3 = {
"Crude " : quandl.get("EIA/PET_RWTC_D", start_date=SourceStart, end_date=SourceEnd),
"CNY " : quandl.get("FED/RXI_N_B_CH", start_date=SourceStart, end_date=SourceEnd)["Value"],
"Uncer " : quandl.get("FRED/WLEMUINDXD", start_date=SourceStart, end_date=SourceEnd),
"Unccn " : quandl.get("FRED/CHIEPUINDXM", start_date=SourceStart, end_date=SourceEnd),
"VIX " : quandl.get("CHRIS/CBOE_VX1", start_date=SourceStart, end_date=SourceEnd)["Open"],
"AAII " : quandl.get("AAII/AAII_SENTIMENT", start_date=SourceStart, end_date=SourceEnd)["Bullish"],
}
DataDic = {**Data1, **Data2, **Data3}
data_cont = list(DataDic.values())
data_name = list(DataDic.keys())
return (data_cont, data_name)
def compCMO(self):
cmo = talib.CMO(self.close,timeperiod=self.lookback)
self.removeNullID(cmo)
self.rawFeatures['CMO'] = cmo
FEATURE_SIZE_DICT['CMO'] = 1
return
def extract_features(data):
high = data['High']
low = data['Low']
close = data['Close']
volume = data['Volume']
open_ = data['Open']
data['ADX'] = ta.ADX(high, low, close, timeperiod=19)
data['CCI'] = ta.CCI(high, low, close, timeperiod=19)
data['CMO'] = ta.CMO(close, timeperiod=14)
data['MACD'], X, Y = ta.MACD(close, fastperiod=10, slowperiod=30, signalperiod=9)
data['MFI'] = ta.MFI(high, low, close, volume, timeperiod=19)
data['MOM'] = ta.MOM(close, timeperiod=9)
data['ROCR'] = ta.ROCR(close, timeperiod=12)
data['RSI'] = ta.RSI(close, timeperiod=19)
data['STOCHSLOWK'], data['STOCHSLOWD'] = ta.STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
data['TRIX'] = ta.TRIX(close, timeperiod=30)
data['WILLR'] = ta.WILLR(high, low, close, timeperiod=14)
data['OBV'] = ta.OBV(close, volume)
data['TSF'] = ta.TSF(close, timeperiod=14)
data['NATR'] = ta.NATR(high, low, close)#, timeperiod=14)
data['ULTOSC'] = ta.ULTOSC(high, low, close)
data['AROONOSC'] = ta.AROONOSC(high, low, timeperiod=14)
data['BOP'] = ta.BOP(open_, high, low, close)
data['LINEARREG'] = ta.LINEARREG(close)
data['AP0'] = ta.APO(close, fastperiod=9, slowperiod=23, matype=1)
data['TEMA'] = ta.TRIMA(close, 29)
return data
def _compute_states(self):
start = self.current_idx - self.lookback
end = self.current_idx
df = self._data.iloc[start:end, :]
states = {}
states['roc1'] = ta.ROC(df.close, timeperiod=5).iloc[-1]
states['roc2'] = ta.ROC(df.close, timeperiod=20).iloc[-1]
states['rsi1'] = ta.RSI(df.close, timeperiod=14).iloc[-1]
states['rsi2'] = ta.RSI(df.close, timeperiod=28).iloc[-1]
states['cci1'] = ta.CCI(df.high, df.low, df.close,
timeperiod=14).iloc[-1]
states['cci2'] = ta.CCI(df.high, df.low, df.close,
timeperiod=28).iloc[-1]
states['adx1'] = ta.ADX(df.high, df.low, df.close,
timeperiod=14).iloc[-1]
states['adx2'] = ta.ADX(df.high, df.low, df.close,
timeperiod=28).iloc[-1]
states['atr'] = ta.ATR(df.high, df.low, df.close,
timeperiod=14).iloc[-1]
states['aroon1'] = ta.AROONOSC(df.high, df.low, timeperiod=14).iloc[-1]
states['aroon2'] = ta.AROONOSC(df.high, df.low, timeperiod=28).iloc[-1]
states['bop'] = ta.BOP(df.open, df.high, df.low, df.close).iloc[-1]
states['cmo'] = ta.CMO(df.close, timeperiod=14).iloc[-1]
states['close'] = df.close.iloc[-1]
states['date'] = df.index[-1]
states['position'] = self.position
states['entry_price'] = self.entry_price
states['entry_date'] = self.entry_date
return states
def calculations(self):
'''calculations'''
self.df['rsi'] = ta.RSI(self.df['close'], timeperiod=5)
self.df['apo'] = ta.APO(self.df['close'],
fastperiod=10,
slowperiod=5,
matype=0)
self.df['upperband'], self.df['middleband'], self.df[
'lowerband'] = ta.BBANDS(self.df['close'],
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)
self.df['ema'] = ta.EMA(self.df['close'], timeperiod=5)
self.df['ma'] = ta.MA(self.df['close'], timeperiod=5, matype=0)
self.df['sma'] = ta.MA(self.df['close'], timeperiod=5)
self.df['t3'] = ta.T3(self.df['close'], timeperiod=5, vfactor=0)
self.df['wma'] = ta.WMA(self.df['close'], timeperiod=5)
self.df['aroonosc'] = ta.AROONOSC(self.df['high'],
self.df['low'],
timeperiod=5)
self.df['cci'] = ta.CCI(self.df['high'],
self.df['low'],
self.df['close'],
timeperiod=5)
self.df['cmo'] = ta.CMO(self.df['close'], timeperiod=14)
self.df['macd'], self.df['macdsignal'], self.df[
'macdhist'] = ta.MACDEXT(self.df['close'],
fastperiod=12,
fastmatype=0,
slowperiod=26,
slowmatype=0,
signalperiod=9,
signalmatype=0)
self.df['slowk'], self.df['slowd'] = ta.STOCH(self.df['high'],
self.df['low'],
self.df['close'],
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
self.df['fastk'], self.df['fastd'] = ta.STOCHRSI(self.df['close'],
timeperiod=5,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
self.df['ultosc'] = ta.ULTOSC(self.df['high'],
self.df['low'],
self.df['close'],
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
self.df['adosc'] = ta.ADOSC(self.df['high'],
self.df['low'],
self.df['close'],
self.df['volume'],
fastperiod=3,
slowperiod=10)
return self.df
def getMomentumIndicators(df):
high = df['High']
low = df['Low']
close = df['Close']
open = df['Open']
volume = df['Volume']
df['ADX'] = ta.ADX(high, low, close, timeperiod=14)
df['SMA'] = ta.ADXR(high, low, close, timeperiod=14)
df['APO'] = ta.APO(close, fastperiod=12, slowperiod=26, matype=0)
df['AROONDOWN'], df['AROOONUP'] = ta.AROON(high, low, timeperiod=14)
df['AROONOSC'] = ta.AROONOSC(high, low, timeperiod=14)
df['BOP'] = ta.BOP(open, high, low, close)
df['CCI'] = ta.CCI(high, low, close, timeperiod=14)
df['CMO'] = ta.CMO(close, timeperiod=14)
df['DX'] = ta.DX(high, low, close, timeperiod=14)
df['MACD'], df['MACDSIGNAL'], df['MACDHIST'] = ta.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
df['MFI'] = ta.MFI(high, low, close, volume, timeperiod=14)
df['MINUS_DI'] = ta.MINUS_DI(high, low, close, timeperiod=14)
df['MINUS_DM']= ta.MINUS_DM(high, low, timeperiod=14)
df['MOM'] = ta.MOM(close, timeperiod=10)
df['PLUS_DM'] =ta.PLUS_DM(high, low, timeperiod=14)
df['PPO'] = ta.PPO(close, fastperiod=12, slowperiod=26, matype=0)
df['ROC'] = ta.ROC(close, timeperiod=10)
df['ROCP'] = ta.ROCP(close, timeperiod=10)
df['ROCR'] = ta.ROCR(close, timeperiod=10)
df['ROCR100'] = ta.ROCR100(close, timeperiod=10)
df['RSI'] = ta.RSI(close, timeperiod=14)
df['SLOWK'], df['SLOWD'] = ta.STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
df['FASTK'], df['FASTD'] = ta.STOCHF(high, low, close, fastk_period=5, fastd_period=3, fastd_matype=0)
df['FASTK2'], df['FASTD2'] = ta.STOCHRSI(close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0)
df['TRIX'] = ta.TRIX(close, timeperiod=30)
df['ULTOSC'] = ta.ULTOSC(high, low, close, timeperiod1=7, timeperiod2=14, timeperiod3=28)
df['WILLR'] = ta.WILLR(high, low, close, timeperiod=14)
def rebalance(context, data):
history = data.history(assets=context.asset,
fields=['close'],
bar_count=context.cmo_window + 1,
frequency='1d')
date = history.index.values[-1]
close = history['close'].values
# 计算CMO指标
cmo = ta.CMO(close, timeperiod=context.cmo_window)
print(cmo[-1])
current_price = data[context.asset].price
record(price=current_price)
buy_signal_triggered = False
sell_signal_triggered = False
if cmo[-1] < context.over_sell:
buy_signal_triggered = True
elif cmo[-1] > context.over_buy:
sell_signal_triggered = True
current_position = context.portfolio.positions[context.asset].amount
if buy_signal_triggered and current_position == 0:
print(str(date) + '==>Buy')
order_target_percent(context.asset, 1.0)
elif sell_signal_triggered and current_position > 0:
print(str(date) + '==>Sell')
order_target_percent(context.asset, 0.0)
else:
print("No trading")
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 cmo(close_ts, timeperiod=14):
import talib
close_np = close_ts.cpu().detach().numpy()
close_df = pd.DataFrame(close_np)
cmo = close_df.apply(lambda x: talib.CMO(x, timeperiod=14))
CMO_ts = torch.tensor(cmo.values, dtype=close_ts.dtype, device=close_ts.device)
return CMO_ts
def CMO(self, stockhist):
cmo = stockhist.copy()
cmo['cmo'] = tal.CMO(stockhist['close'])
cmo['signal'] = 0
cmo.loc[cmo['cmo'] > 25, 'signal'] = 1
cmo['signal'] = cmo['signal'].fillna(0)
cmo = cmo[['date', 'dailyreturn', 'signal']].copy()
return (cmo)
def cmo(self, n: int, array: bool = False) -> Union[float, np.ndarray]:
"""
CMO.
"""
result = talib.CMO(self.close, n)
if array:
return result
return result[-1]
def cmo(self, n, array=False):
"""
CMO.
"""
result = talib.CMO(self.close, n)
if array:
return result
return result[-1]
def create_technical_indicators(df, indicators=['MA_4'], only_close_colum=False):
if only_close_colum:
if isinstance(df, pd.DataFrame):
close = np.array(df['Close']).reshape(len(df), )
else:
if isinstance(df, pd.DataFrame):
close = np.array(df['Close']).reshape(len(df), )
# OPEN = np.array(df['Open']).reshape(len(df), )
high = np.array(df['High']).reshape(len(df), )
low = np.array(df['Low']).reshape(len(df), )
df_2_return = pd.DataFrame()
for indicator in indicators:
if indicator is None:
pass
else:
if indicator.split('_')[0].strip() == 'MA':
smavg = talib.MA(close, timeperiod=int(indicator.split('_')[1]))
df_2_return[indicator] = smavg
if indicator.split('_')[0].strip() == 'BBANDS':
upperband, middleband, lowerband = talib.BBANDS(close, timeperiod=int(indicator.split('_')[1]))
df_2_return['lowerband'] = lowerband
df_2_return[indicator] = middleband
df_2_return['upperband'] = upperband
if indicator.split('_')[0].strip() == 'MACD':
indicator = indicator + '_12' + '_26' + '_9'
_, macdsignal, macdhist = talib.MACD(close,
fastperiod=int(indicator.split('_')[1]),
slowperiod=int(indicator.split('_')[2]),
signalperiod=int(indicator.split('_')[3]))
df_2_return['macdsignal'] = macdsignal
df_2_return['macdhist'] = macdhist
if indicator.split('_')[0].strip() == 'RSI':
real_RSI = talib.RSI(close, timeperiod=int(indicator.split('_')[1]))
df_2_return[indicator] = real_RSI
if indicator.split('_')[0].strip() == 'CMO':
real_CMO = talib.CMO(close, timeperiod=int(indicator.split('_')[1]))
df_2_return[indicator] = real_CMO
if indicator.split('_')[0].strip() == 'ADX':
if only_close_colum:
pass
else:
real_ADX = talib.ADX(high, low, close, timeperiod=int(indicator.split('_')[1]))
df_2_return[indicator] = real_ADX
if indicator.split('_')[0].strip() == 'STOCH':
if only_close_colum:
pass
else:
slowk, slowd = talib.STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
df_2_return[indicator] = slowd - slowk
return(df_2_return)
def CMO(self, timeperiod=14):
real_data = np.array([self.df.close], dtype='f8')
cmo = talib.CMO(real_data[0], timeperiod=timeperiod)
# return go.Scatter(
# x=self.df.index,
# y=cmo,
# name='CMO'
# )
return cmo
def test_cmo():
"""test TA.CMO"""
cmo = TA.CMO(ohlc, period=9)
talib_cmo = talib.CMO(ohlc["close"], timeperiod=9)
# assert round(talib_cmo[-1], 2) == round(cmo.values[-1], 2)
# assert -35.99 == -35.66
pass # close enough
def cmo(self, sym, frequency, period=14):
if not self.kbars_ready(sym, frequency):
return []
closes = self.close(sym, frequency)
cmo = ta.CMO(closes, timeperiod=period)
return cmo
def CMO(self, stk_no, start='2008/01/01', end='2016/12/31', timeperiod=14):
realstart = day_back(start, timeperiod)
close = self.get_stock_data(stk_no, 'ClosePrice', realstart, end)
if np.isnan(close).all():
return []
cmo = talib.CMO(close, timeperiod)
if np.isnan(cmo).all():
return []
else:
return cmo[timeperiod:]
def get_mo(price_arr, cur_pos, period=20):
if cur_pos <= (period + 1):
return 0
else:
s = cur_pos - (period + 1)
tmp_arr = price_arr[s:cur_pos]
tmp_arr.reverse()
prices = np.array(tmp_arr, dtype=float)
return ta.CMO(prices, timeperiod=period)[-1]
def generate_feature(data):
high = data.High.values
low = data.Low.values
close = data.Close.values
# feature_df = pd.DataFrame(index=data.index)
feature_df = data.copy()
feature_df["ADX"] = ADX = talib.ADX(high, low, close, timeperiod=14)
feature_df["ADXR"] = ADXR = talib.ADXR(high, low, close, timeperiod=14)
feature_df["APO"] = APO = talib.APO(close,
fastperiod=12,
slowperiod=26,
matype=0)
feature_df["AROONOSC"] = AROONOSC = talib.AROONOSC(high,
low,
timeperiod=14)
feature_df["CCI"] = CCI = talib.CCI(high, low, close, timeperiod=14)
feature_df["CMO"] = CMO = talib.CMO(close, timeperiod=14)
feature_df["DX"] = DX = talib.DX(high, low, close, timeperiod=14)
feature_df["MINUS_DI"] = MINUS_DI = talib.MINUS_DI(high,
low,
close,
timeperiod=14)
feature_df["MINUS_DM"] = MINUS_DM = talib.MINUS_DM(high,
low,
timeperiod=14)
feature_df["MOM"] = MOM = talib.MOM(close, timeperiod=10)
feature_df["PLUS_DI"] = PLUS_DI = talib.PLUS_DI(high,
low,
close,
timeperiod=14)
feature_df["PLUS_DM"] = PLUS_DM = talib.PLUS_DM(high, low, timeperiod=14)
feature_df["PPO"] = PPO = talib.PPO(close,
fastperiod=12,
slowperiod=26,
matype=0)
feature_df["ROC"] = ROC = talib.ROC(close, timeperiod=10)
feature_df["ROCP"] = ROCP = talib.ROCP(close, timeperiod=10)
feature_df["ROCR100"] = ROCR100 = talib.ROCR100(close, timeperiod=10)
feature_df["RSI"] = RSI = talib.RSI(close, timeperiod=14)
feature_df["ULTOSC"] = ULTOSC = talib.ULTOSC(high,
low,
close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
feature_df["WILLR"] = WILLR = talib.WILLR(high, low, close, timeperiod=14)
feature_df = feature_df.fillna(0.0)
# Exclude columns you don't want
feature_df = feature_df[feature_df.columns[
~feature_df.columns.isin(['Open', 'High', 'Low', 'Close'])]]
matrix = feature_df.values
return feature_df, matrix
def _get_indicators(security, open_name, close_name, high_name, low_name,
volume_name):
"""
expand the features of the data through technical analysis across 26 different signals
:param security: data which features are going to be expanded
:param open_name: open price column name
:param close_name: close price column name
:param high_name: high price column name
:param low_name: low price column name
:param volume_name: traded volumn column name
:return: expanded and extracted data
"""
open_price = security[open_name].values
close_price = security[close_name].values
low_price = security[low_name].values
high_price = security[high_name].values
volume = security[volume_name].values if volume_name else None
security['MOM'] = talib.MOM(close_price)
security['HT_DCPERIOD'] = talib.HT_DCPERIOD(close_price)
security['HT_DCPHASE'] = talib.HT_DCPHASE(close_price)
security['SINE'], security['LEADSINE'] = talib.HT_SINE(close_price)
security['INPHASE'], security['QUADRATURE'] = talib.HT_PHASOR(
close_price)
security['ADXR'] = talib.ADXR(high_price, low_price, close_price)
security['APO'] = talib.APO(close_price)
security['AROON_UP'], _ = talib.AROON(high_price, low_price)
security['CCI'] = talib.CCI(high_price, low_price, close_price)
security['PLUS_DI'] = talib.PLUS_DI(high_price, low_price, close_price)
security['PPO'] = talib.PPO(close_price)
security['MACD'], security['MACD_SIG'], security[
'MACD_HIST'] = talib.MACD(close_price)
security['CMO'] = talib.CMO(close_price)
security['ROCP'] = talib.ROCP(close_price)
security['FASTK'], security['FASTD'] = talib.STOCHF(
high_price, low_price, close_price)
security['TRIX'] = talib.TRIX(close_price)
security['ULTOSC'] = talib.ULTOSC(high_price, low_price, close_price)
security['WILLR'] = talib.WILLR(high_price, low_price, close_price)
security['NATR'] = talib.NATR(high_price, low_price, close_price)
security['RSI'] = talib.RSI(close_price)
security['EMA'] = talib.EMA(close_price)
security['SAREXT'] = talib.SAREXT(high_price, low_price)
# security['TEMA'] = talib.EMA(close_price)
security['RR'] = security[close_name] / security[close_name].shift(
1).fillna(1)
security['LOG_RR'] = np.log(security['RR'])
if volume_name:
security['MFI'] = talib.MFI(high_price, low_price, close_price,
volume)
# security['AD'] = talib.AD(high_price, low_price, close_price, volume)
# security['OBV'] = talib.OBV(close_price, volume)
security[volume_name] = np.log(security[volume_name])
security.drop([open_name, close_name, high_name, low_name], axis=1)
security = security.dropna().astype(np.float32)
return security
def add_cmo(dataset,param, first_header):
field_name = 'cmo' + str(param)
df = dataset[first_header].copy()
close = df['Close']
cmo = talib.CMO(close, timeperiod=14)/100
col = pd.DataFrame(cmo, index=df.index)
dataset[first_header, field_name] = col
return dataset
def indicators(data):
"""
:param data: data
:return: np array of each indicator
"""
cmo = talib.CMO(np.array(data), timeperiod=10).reshape(-1, 1)
roc = talib.ROC(np.array(data), timeperiod=5).reshape(-1, 1)
rsi = talib.RSI(np.array(data), timeperiod=5).reshape(-1, 1)
wma = talib.WMA(np.array(data), timeperiod=20).reshape(-1, 1)
ppo = talib.PPO(np.array(data), fastperiod=10, slowperiod=20, matype=0).reshape(-1, 1)
return cmo, roc, rsi, wma, ppo
def generate_tech_data(stock):
price = stock.values
name = stock.name
data = pd.DataFrame(stock)
data[name + '_mom'] = talib.MOM(price)
data[name + '_macd'], data[name + '_macd_sig'], data[name + '_macd_hist'] = talib.MACD(price)
data[name + '_rsi'] = talib.RSI(price, timeperiod=10)
data[name + '_cmo'] = talib.CMO(price)
data = data.drop(name, axis=1)
data = data.dropna()
return data
def get_momentum_studies(open, low, high, close, volume, df):
# Momentum studies
# https://mrjbq7.github.io/ta-lib/func_groups/momentum_indicators.html
df['MACD'], df['MACD_SIGN'], df['MACD_HIST'] = talib.MACD(
close, fastperiod=12, slowperiod=26, signalperiod=9)
df['STOCH-SLOW-K'], df['STOCH-SLOW-D'] = talib.STOCH(high,
low,
close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
df['STOCH-FAST-K'], df['STOCH-FAST-D'] = talib.STOCHF(high,
low,
close,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
df['STOCH-RSI-K'], df['STOCH-RSI-D'] = talib.STOCHRSI(close,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
df['AROON-DOWN'], df['AROON-UP'] = talib.AROON(high,
low,
timeperiod=14)
df["MINUS_DI"] = talib.MINUS_DI(high, low, close, timeperiod=14)
df["MINUS_DM"] = talib.MINUS_DM(high, low, timeperiod=14)
df["PLUS_DI"] = talib.PLUS_DI(high, low, close, timeperiod=14)
df["PLUS_DM"] = talib.PLUS_DM(high, low, timeperiod=14)
df["MOM"] = talib.MOM(close, timeperiod=10)
df["MFI"] = talib.MFI(high, low, close, volume, timeperiod=14)
df["ADX"] = talib.ADX(high, low, close, timeperiod=14)
df["ADXR"] = talib.ADXR(high, low, close, timeperiod=14)
df["APO"] = talib.APO(close, fastperiod=12, slowperiod=26, matype=0)
df["AROONOSC"] = talib.AROONOSC(high, low, timeperiod=14)
df["BOP"] = talib.BOP(open, high, low, close)
df["CCI"] = talib.CCI(high, low, close, timeperiod=14)
df["CMO"] = talib.CMO(close, timeperiod=14)
df["DX"] = talib.DX(high, low, close, timeperiod=14)
df["PPO"] = talib.PPO(close, fastperiod=12, slowperiod=26, matype=0)
df["ROC"] = talib.ROC(close, timeperiod=10)
df["RSI"] = talib.RSI(close, timeperiod=14)
df["TRIX"] = talib.TRIX(close, timeperiod=30)
df["ULT"] = talib.ULTOSC(high,
low,
close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
df["WILLR"] = talib.WILLR(high, low, close, timeperiod=14)
def add_CMO(self, timeperiod=14, type='line', color='secondary', **kwargs):
"""Chande Momentum Indicator."""
if not self.has_close:
raise Exception()
utils.kwargs_check(kwargs, VALID_TA_KWARGS)
if 'kind' in kwargs:
type = kwargs['kind']
name = 'CMO({})'.format(str(timeperiod))
self.sec[name] = dict(type=type, color=color)
self.ind[name] = talib.CMO(self.df[self.cl].values, timeperiod)
def test_CMO(self):
class MyCMO(OperatorCMO):
def __init__(self, name, **kwargs):
super(MyCMO, self).__init__(100, name, **kwargs)
self.env.add_operator('cmo', {
'operator': MyCMO,
})
string = 'cmo(14, open)'
gene = self.env.parse_string(string)
self.assertRaises(IndexError, gene.eval, self.env, self.dates[98],
self.dates[-1])
df = gene.eval(self.env, self.dates[99], self.dates[100])
ser0, ser1 = df.iloc[0], df.iloc[1]
o = self.env.get_data_value('open').values
res0, res1, res = [], [], []
for i, val in ser0.iteritems():
res0.append(talib.CMO(o[:100, i], timeperiod=14)[-1] == val)
for i, val in ser1.iteritems():
res1.append(talib.CMO(o[1:100 + 1, i], timeperiod=14)[-1] == val)
res.append(talib.CMO(o[:100 + 1, i], timeperiod=14)[-1] != val)
self.assertTrue(all(res0) and all(res1) and any(res))
