def test_ADXR(self):
class MyADXR(OperatorADXR):
def __init__(self, name, **kwargs):
super(MyADXR, self).__init__(100, name, **kwargs)
self.env.add_operator('adxr', {
'operator': MyADXR,
})
string = 'adxr(14, high, low, close)'
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]
h = self.env.get_data_value('high').values
l = self.env.get_data_value('low').values
c = self.env.get_data_value('close').values
res0, res1, res = [], [], []
for i, val in ser0.iteritems():
res0.append(
talib.ADXR(h[:100, i], l[:100, i], c[:100, i], 14)[-1] == val)
for i, val in ser1.iteritems():
res1.append(
talib.ADXR(h[1:100 + 1, i], l[1:100 + 1,
i], c[1:100 + 1,
i], 14)[-1] == val)
res.append(
talib.ADXR(h[:100 + 1, i], l[:100 + 1,
i], c[:100 + 1,
i], 14)[-1] != val)
self.assertTrue(all(res0) and all(res1) and any(res))
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 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 checkOne(code_str):
df = base.getOneStockData(code_str)
df['MINUS_DM_' + str(adx_timeperiod)] = ta.MINUS_DM(
np.array(df['high']), np.array(df['low']), timeperiod=adx_timeperiod)
df['PLUS_DM_' + str(adx_timeperiod)] = ta.PLUS_DM(
np.array(df['high']), np.array(df['low']), timeperiod=adx_timeperiod)
df['TR_' + str(adx_timeperiod)] = ta.TRANGE(np.array(df['high']),
np.array(df['low']),
np.array(df['close']))
df['MINUS_DI_' + str(adx_timeperiod)] = ta.MINUS_DI(
np.array(df['high']),
np.array(df['low']),
np.array(df['close']),
timeperiod=adx_timeperiod)
df['PLUS_DI_' + str(adx_timeperiod)] = ta.PLUS_DI(
np.array(df['high']),
np.array(df['low']),
np.array(df['close']),
timeperiod=adx_timeperiod)
df['ADX_' + str(adx_timeperiod)] = ta.ADX(np.array(df['high']),
np.array(df['low']),
np.array(df['close']),
timeperiod=adx_timeperiod)
df['ADXR_' + str(adx_timeperiod)] = ta.ADXR(np.array(df['high']),
np.array(df['low']),
np.array(df['close']),
timeperiod=adx_timeperiod)
df.to_csv(ADX_DIR + code_str + '.csv')
def technical_indicators_df(self):
o = self.daily_data['Open'].values
c = self.daily_data['Close'].values
h = self.daily_data['High'].values
l = self.daily_data['Low'].values
v = self.daily_data['Volume'].astype(float).values
# define the technical analysis matrix
ta = pd.DataFrame()
ta['MA5'] = tb.MA(c, timeperiod=5)
ta['MA10'] = tb.MA(c, timeperiod=10)
ta['MA20'] = tb.MA(c, timeperiod=20)
ta['MA60'] = tb.MA(c, timeperiod=60)
ta['MA120'] = tb.MA(c, timeperiod=120)
ta['MA5'] = tb.MA(v, timeperiod=5)
ta['MA10'] = tb.MA(v, timeperiod=10)
ta['MA20'] = tb.MA(v, timeperiod=20)
ta['ADX'] = tb.ADX(h, l, c, timeperiod=14)
ta['ADXR'] = tb.ADXR(h, l, c, timeperiod=14)
ta['MACD'] = tb.MACD(c, fastperiod=12, slowperiod=26, signalperiod=9)[0]
ta['RSI'] = tb.RSI(c, timeperiod=14)
ta['BBANDS_U'] = tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[0]
ta['BBANDS_M'] = tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[1]
ta['BBANDS_L'] = tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[2]
ta['AD'] = tb.AD(h, l, c, v)
ta['ATR'] = tb.ATR(h, l, c, timeperiod=14)
ta['HT_DC'] = tb.HT_DCPERIOD(c)
self.ta = ta
def get_features(data):
tech_data = pd.DataFrame(index=data.index);
for t in periods_list:
tech_data[f'SMA_{t}'] = talib.SMA(data.close,timeperiod=t)
tech_data[f'MOM_{t}'] = talib.MOM(data.close, timeperiod=t)
tech_data[f'RSI_{t}'] = talib.RSI(data.close, timeperiod=t)
tech_data[f'MA_{t}'] = talib.MA(data.close, timeperiod=t)
tech_data[f'DX_{t}'] = talib.DX(data.high, data.low, data.close, timeperiod=t)
tech_data[f'volume_change_{t}'] = data.volume.pct_change(periods=t)
tech_data[f'volatility_{t}'] = data.close.pct_change(periods=t).std()
tech_data[f'ADX_{t}'] = talib.ADX(data.high, data.low, data.close, timeperiod=t)
tech_data[f'ADXR_{t}'] = talib.ADXR(data.high, data.low, data.close, timeperiod=t)
tech_data[f'AROONOSC_{t}'] = talib.AROONOSC(data.high, data.low, timeperiod=t)
tech_data[f'ROC_{t}'] = talib.ROC(data.close, timeperiod=t)
tech_data[f'BIAS_{t}'] = (data['close'] - data['close'].rolling(t, min_periods=1).mean())/ data['close'].rolling(t, min_periods=1).mean()*100
tech_data[f'BOLL_upper_{t}'], tech_data[f'BOLL_middle_{t}'], tech_data[f'BOLL_lower_{t}'] = talib.BBANDS(
data.close,
timeperiod=t,
nbdevup=2,
nbdevdn=2,
matype=0)
tech_data['SAR'] = talib.SAR(data.high, data.low)
tech_data['AD'] = talib.AD(data.high, data.low, data.close, data.volume)
tech_data['OBV'] = talib.OBV(data.close, data.volume)
tech_data['target'] = data.close.pct_change().shift(-1).apply(lambda x: 1 if x > 0 else -1).fillna(0)
tech_data['time'] = data.time
tech_data = tech_data.set_index('time')
reduce(lambda x, y: cross_over(x, y, tech_data), periods_list)
features = list(set(tech_data.columns) - set(data.columns) - set(['target']))
return tech_data.dropna(), features
def dmi(self):
df = self.df
MINUS_DI = talib.MINUS_DI(df.high, df.low, df.close, timeperiod=14)
PLUS_DI = talib.PLUS_DI(df.high, df.low, df.close, timeperiod=14)
ADX = talib.ADX(df.high, df.low, df.close, timeperiod=6)
ADXR = talib.ADXR(df.high, df.low, df.close, timeperiod=6)
return PLUS_DI.tolist(), MINUS_DI.tolist(), ADX.tolist(), ADXR.tolist()
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 ADXR(df, n):
return pd.Series(talib.ADXR(df['high'].values,
df['low'].values,
df['close'].values,
timeperiod=n),
index=df.index,
name='ADXR_%s' % str(n))
def adxr(
client,
symbol,
timeframe="6m",
highcol="high",
lowcol="low",
closecol="close",
period=14,
):
"""This will return a dataframe of average directional movement index rating for the given symbol across
the given timeframe
Args:
client (pyEX.Client): Client
symbol (string): Ticker
timeframe (string): timeframe to use, for pyEX.chart
highcol (string): column to use to calculate
lowcol (string): column to use to calculate
closecol (string): column to use to calculate
period (int): period to calculate across
Returns:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
adx = t.ADXR(df[highcol].values, df[lowcol].values, df[closecol].values, period)
return pd.DataFrame(
{
highcol: df[highcol].values,
lowcol: df[lowcol].values,
closecol: df[closecol].values,
"adx": adx,
}
)
def dmi(self, df, index):
MINUS_DI = talib.MINUS_DI(df.high, df.low, df.close, timeperiod=14)
#DX = talib.DX(df.high,df.low,df.close,timeperiod=14)
PLUS_DI = talib.PLUS_DI(df.high, df.low, df.close, timeperiod=14)
#PLUS_DM = talib.PLUS_DM(df.high,df.low, timeperiod=14)
ADX = talib.ADX(df.high, df.low, df.close, timeperiod=6)
ADXR = talib.ADXR(df.high, df.low, df.close, timeperiod=6)
return MINUS_DI, PLUS_DI, ADX, ADXR
def TA_ADXR(high, low, close, timeperiod=14) -> np.ndarray:
"""
名称：平均趋向指数的趋向指数
简介：使用ADXR指标，指标判断ADX趋势。
ADXR - Average Directional Movement Index Rating
"""
real = talib.ADXR(high, low, close, timeperiod=timeperiod)
return np.c_[real]
def adxr(self, n, array=False):
"""
ADXR.
"""
result = talib.ADXR(self.high, self.low, self.close, n)
if array:
return result
return result[-1]
def adxr(self, n: int, array: bool = False) -> Union[float, np.ndarray]:
"""
ADXR.
"""
result = talib.ADXR(self.high, self.low, self.close, n)
if array:
return result
return result[-1]
def onFifteenBar(self, bar):
"""收到15分钟K线"""
# 撤销之前发出的尚未成交的委托（包括限价单和停止单）
for orderID in self.orderList:
self.cancelOrder(orderID)
self.orderList = []
# 保存K线数据
am = self.am
am.updateBar(bar)
if not am.inited:
return
# 计算指标数值
std0 = np.std(am.close[-self.aPeriod - 1:-1])
std1 = np.std(am.close[-self.aPeriod:])
if std1 == 0:
return
volatility = (std1 - std0) / std1
if volatility < 0.1:
volatility = 0.1
period = int(self.aPeriod * (1 + volatility))
fast_ma = talib.MA(am.close, period)
middle_ma = talib.MA(am.close, 2 * period)
slow_ma = talib.MA(am.close, 3 * period)
adxr = talib.ADXR(am.high, am.low, am.close, self.aPeriod)
pdi = talib.PLUS_DI(am.high, am.low, am.close, self.aPeriod)
mdi = talib.MINUS_DI(am.high, am.low, am.close, self.aPeriod)
self.fastMa = fast_ma[-1]
self.middleMa = middle_ma[-1]
self.slowMa = slow_ma[-1]
self.adxr = adxr[-1]
self.pdi = pdi[-1]
self.mdi = mdi[-1]
# 判断是否要进行交易
# 多头
if self.adxr > 30 and self.pdi > self.mdi and self.fastMa > self.middleMa > self.slowMa:
self.targetPos = self.fixedSize
# 空头
if self.adxr > 30 and self.mdi > self.pdi and self.fastMa < self.middleMa < self.slowMa:
self.targetPos = -self.fixedSize
# 同步数据到数据库
self.saveSyncData()
# 发出状态更新事件
self.putEvent()
def applyADXR(nsedailyquoteDF):
adxrdf = pd.Series(talib.ADXR(nsedailyquoteDF['HIGH'].values,
nsedailyquoteDF['LOW'].values,
nsedailyquoteDF['CLOSE'].values,
timeperiod=14),
index=nsedailyquoteDF.TIMESTAMP,
name='ADXR')
return adxrdf
def ADXR(self, indicatorSpan=14):
real_data = np.array([self.df.high, self.df.low, self.df.close], dtype='f8')
adxr = talib.ADXR(real_data[0], real_data[1], real_data[2], timeperiod=indicatorSpan)
# return go.Scatter(
# x=self.df.index,
# y=adxr,
# name='ADXR'
# )
return adxr
def adxr(self, sym, frequency, period=14):
if not self.kbars_ready(sym, frequency):
return []
highs = self.high(sym, frequency)
lows = self.low(sym, frequency)
closes = self.close(sym, frequency)
return ta.ADXR(highs, lows, closes, timeperiod=period)
def calculateMOM(dataset):
timeperiod = 14
dataset.loc[:,'mom_adx'] = talib.ADX(dataset['high'].values, dataset['low'].values, dataset['close'].values, timeperiod=timeperiod)
dataset.loc[:,'mom_adxr'] = talib.ADXR(dataset['high'].values, dataset['low'].values, dataset['close'].values, timeperiod=timeperiod)
dataset.loc[:,'mom_mdi'] = talib.MINUS_DI(dataset['high'].values, dataset['low'].values, dataset['close'].values, timeperiod=timeperiod)
dataset.loc[:,'mom_mdm'] = talib.MINUS_DM(dataset['high'].values, dataset['low'].values, timeperiod=timeperiod)
dataset.loc[:,'mom_pdi'] = talib.MINUS_DI(dataset['high'].values, dataset['low'].values, dataset['close'].values, timeperiod=timeperiod)
dataset.loc[:,'mom_pdm'] = talib.MINUS_DM(dataset['high'].values, dataset['low'].values, timeperiod=timeperiod)
return dataset
def technical_indicators_df(self, daily_data):
"""
Assemble a dataframe of technical indicator series for a single stock
"""
o = daily_data['Open'].values
c = daily_data['Close'].values
h = daily_data['High'].values
l = daily_data['Low'].values
v = daily_data['Volume'].astype(float).values
# define the technical analysis matrix
# Most data series are normalized by their series' mean
ta = pd.DataFrame()
ta['MA5'] = tb.MA(c, timeperiod=5) / tb.MA(c, timeperiod=5).mean()
ta['MA10'] = tb.MA(c, timeperiod=10) / tb.MA(c, timeperiod=10).mean()
ta['MA20'] = tb.MA(c, timeperiod=20) / tb.MA(c, timeperiod=20).mean()
ta['MA60'] = tb.MA(c, timeperiod=60) / tb.MA(c, timeperiod=60).mean()
ta['MA120'] = tb.MA(c, timeperiod=120) / tb.MA(c,
timeperiod=120).mean()
ta['MA5'] = tb.MA(v, timeperiod=5) / tb.MA(v, timeperiod=5).mean()
ta['MA10'] = tb.MA(v, timeperiod=10) / tb.MA(v, timeperiod=10).mean()
ta['MA20'] = tb.MA(v, timeperiod=20) / tb.MA(v, timeperiod=20).mean()
ta['ADX'] = tb.ADX(h, l, c, timeperiod=14) / tb.ADX(
h, l, c, timeperiod=14).mean()
ta['ADXR'] = tb.ADXR(h, l, c, timeperiod=14) / tb.ADXR(
h, l, c, timeperiod=14).mean()
ta['MACD'] = tb.MACD(c, fastperiod=12, slowperiod=26, signalperiod=9)[0] / \
tb.MACD(c, fastperiod=12, slowperiod=26, signalperiod=9)[0].mean()
ta['RSI'] = tb.RSI(c, timeperiod=14) / tb.RSI(c, timeperiod=14).mean()
ta['BBANDS_U'] = tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[0] / \
tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[0].mean()
ta['BBANDS_M'] = tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[1] / \
tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[1].mean()
ta['BBANDS_L'] = tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[2] / \
tb.BBANDS(c, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)[2].mean()
ta['AD'] = tb.AD(h, l, c, v) / tb.AD(h, l, c, v).mean()
ta['ATR'] = tb.ATR(h, l, c, timeperiod=14) / tb.ATR(
h, l, c, timeperiod=14).mean()
ta['HT_DC'] = tb.HT_DCPERIOD(c) / tb.HT_DCPERIOD(c).mean()
ta["High/Open"] = h / o
ta["Low/Open"] = l / o
ta["Close/Open"] = c / o
self.ta = ta
def ADXR(high, low, close, timeperiod=14):
''' Average Directional Movement Index Rating 平均趋向指数的趋向指数
分组: Momentum Indicator 动量指标
简介: 使用ADXR指标，指标判断ADX趋势。
real = ADXR(high, low, close, timeperiod=14)
'''
return talib.ADXR(high, low, close, timeperiod)
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_adx(self, n: int):
if n <= len(self.closes):
adx = nan_to_zero(
talib.ADX(np.array(self.highs), np.array(self.lows),
np.array(self.closes), n)).tolist()
adxr = nan_to_zero(
talib.ADXR(np.array(self.highs), np.array(self.lows),
np.array(self.closes), n)).tolist()
self.adx = Adx(n, adx, adxr)
return True
return False
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 ADXR(quote):
'''
Calculate the ADXR and its trend (trend calculated by use SMA)
'''
high = numpy.array(quote.High)
low = numpy.array(quote.Low)
close = numpy.array(quote.Close)
#ADXR is (adx + adx_previous)/2, but not in talib, where ADXR is unknown.
#https://www.linnsoft.com/techind/adxr-avg-directional-movement-rating
adxr = helper.RemoveNaN(ta.ADXR(high, low, close))
#adxr_trend = helper.RemoveNaN(ta.SMA(adxr))
#print("ADXR is ", adxr)
#print("ADXR Trend is ", adxr_trend)
return adxr
def dmi_ana(self, df, index):
MINUS_DI = talib.MINUS_DI(df.high, df.low, df.close, timeperiod=14)
#DX = talib.DX(df.high,df.low,df.close,timeperiod=14)
PLUS_DI = talib.PLUS_DI(df.high, df.low, df.close, timeperiod=14)
#PLUS_DM = talib.PLUS_DM(df.high,df.low, timeperiod=14)
ADX = talib.ADX(df.high, df.low, df.close, timeperiod=6)
ADXR = talib.ADXR(df.high, df.low, df.close, timeperiod=6)
adxlist = ADX.values.tolist()
#plt.plot(ADX.values.tolist(),'g',ADXR.values.tolist(),'b',PLUS_DI.values.tolist(),'y',MINUS_DI.values.tolist(),'k'
#plt.show()
#if adxlist[index]<adxlist[index-2]:return 0
if adxlist[index] < 25: return 0
return 1
def default_idts(self):
"""
Assemble a dataframe of technical indicator series for a single stock
"""
o = self.data['open'].values
c = self.data['close'].values
h = self.data['high'].values
l = self.data['low'].values
v = self.data['volume'].astype(float).values
# define the technical analysis matrix
# Most data series are normalized by their series' mean
ta = pd.DataFrame()
ta['MA5'] = tb.MA(c, timeperiod=5)
ta['MA10'] = tb.MA(c, timeperiod=10)
ta['MA20'] = tb.MA(c, timeperiod=20)
ta['MA60'] = tb.MA(c, timeperiod=60)
ta['MA120'] = tb.MA(c, timeperiod=120)
ta['MA5'] = tb.MA(v, timeperiod=5)
ta['MA10'] = tb.MA(v, timeperiod=10)
ta['MA20'] = tb.MA(v, timeperiod=20)
ta['ADX'] = tb.ADX(h, l, c, timeperiod=14)
ta['ADXR'] = tb.ADXR(h, l, c, timeperiod=14)
ta['MACD'] = tb.MACD(c, fastperiod=12, slowperiod=26,
signalperiod=9)[0]
ta['RSI'] = tb.RSI(c, timeperiod=14)
ta['BBANDS_U'] = tb.BBANDS(c,
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)[0]
ta['BBANDS_M'] = tb.BBANDS(c,
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)[1]
ta['BBANDS_L'] = tb.BBANDS(c,
timeperiod=5,
nbdevup=2,
nbdevdn=2,
matype=0)[2]
ta['AD'] = tb.AD(h, l, c, v)
ta['ATR'] = tb.ATR(h, l, c, timeperiod=14)
ta['HT_DC'] = tb.HT_DCPERIOD(c)
ta["High/Open"] = h / o
ta["Low/Open"] = l / o
ta["Close/Open"] = c / o
ta.index = self.data.index
return ta
def add_ADXR(self, timeperiod=14, type='line', color='secondary', **kwargs):
"""Average Directional Movement Index Rating."""
if not (self.has_high and self.has_low and self.has_close):
raise Exception()
utils.kwargs_check(kwargs, VALID_TA_KWARGS)
if 'kind' in kwargs:
type = kwargs['kind']
name = 'ADXR({})'.format(str(timeperiod))
self.sec[name] = dict(type=type, color=color)
self.ind[name] = talib.ADXR(self.df[self.hi].values,
self.df[self.lo].values,
self.df[self.cl].values, timeperiod)
def getDMI( data ): high = data["high"] low = data["low"] close = data["close"] DI1 = talib.PLUS_DI(high, low, close, timeperiod=14) DI2 = talib.MINUS_DI(high, low, close, timeperiod=14) ADX = talib.ADX(high, low, close, timeperiod=14) ADXR =talib.ADXR(high, low, close, timeperiod=14) data['DMI_DI+'] = DI1 data['DMI_DI-'] = DI2 data['ADX'] = ADX data['ADXR'] = ADXR return data