def test_PLUS_DM(self):
class MyPLUS_DM(OperatorPLUS_DM):
def __init__(self, name, **kwargs):
super(MyPLUS_DM, self).__init__(100, name, **kwargs)
self.env.add_operator('minus_dm', {
'operator': MyPLUS_DM,
})
string = 'minus_dm(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
res0, res1, res = [], [], []
for i, val in ser0.iteritems():
res0.append(
talib.PLUS_DM(h[:100, i], l[:100,
i], timeperiod=14)[-1] == val)
for i, val in ser1.iteritems():
res0.append(
talib.PLUS_DM(h[1:100 + 1, i], l[1:100 + 1,
i], timeperiod=14)[-1] == val)
res.append(
talib.PLUS_DM(h[:100 + 1, i], l[:100 + 1,
i], timeperiod=14)[-1] != val)
self.assertTrue(all(res0) and all(res1) and any(res))
def AverageTrueRangeStopLoss(self, timeperiod=14, multiplier=2):
"""
Average True Range
Is a lagging indicator, used to provide insights into volatility.
Parameters
----------
timeperiod : Integer, optional
DESCRIPTION. The default is 14.
Returns
-------
TYPE
DESCRIPTION.
"""
stopLoss = ta.ATR(self.data.high, self.data.low, self.data.close,
timeperiod)
plus_dm = ta.PLUS_DM(self.data.high, self.data.low, timeperiod)
minus_dm = ta.MINUS_DM(self.data.high, self.data.low, timeperiod)
if plus_dm > minus_dm:
stopLoss = self.data.close - multiplier * stopLoss
else:
stopLoss = self.data.close + multiplier * stopLoss
stopLoss.dropna(inplace=True)
return stopLoss
def plus_dm(client,
symbol,
timeframe="6m",
highcol="high",
lowcol="low",
period=14):
"""This will return a dataframe of
Plus Directional Movement
for the given symbol across the given timeframe
Args:
client (pyEX.Client): Client
symbol (string): Ticker
timeframe (string): timeframe to use, for pyEX.chart
highcol (string): column to use to calculate
lowcol (string): column to use to calculate
period (int): period to calculate across
Returns:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
x = t.PLUS_DM(df[highcol].values.astype(float),
df[lowcol].values.astype(float), period)
return pd.DataFrame({
highcol: df[highcol].values,
lowcol: df[lowcol].values,
"plus_dm": x
})
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 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 adxAlert(self, period):
closes = []
highs = []
lows = []
for x in self.lastValues:
closes.append(float(x[4]))
highs.append(float(x[2]))
lows.append(float(x[3]))
nparraycloses = np.asarray(closes)
nparrayhighs = np.asarray(highs)
nparraylows = np.asarray(lows)
diminus = talib.MINUS_DM(nparrayhighs, nparraylows, timeperiod=14)
diplus = talib.PLUS_DM(nparrayhighs, nparraylows, timeperiod=14)
adx = talib.ADX(nparrayhighs,
nparraylows,
nparraycloses,
timeperiod=14)
if (adx[-1] > 25) and (diplus[-1] > adx[-1]) and (
diplus[-1] > diminus[-1]) and (self.data['adx' + period] == 0):
self.data['adx' + period] = 1
return 1, self.getLastPrice()
if (adx[-1] > 25) and (diminus[-1] > adx[-1]) and (
diminus[-1] > diplus[-1]) and (self.data['adx' + period] == 0):
self.data['adx' + period] = -1
return -1, self.getLastPrice()
if adx[-1] < 25:
self.data['adx' + period] = 0
return 0, 0
def test_dm(self):
result = pandas_ta.dm(self.high, self.low)
self.assertIsInstance(result, DataFrame)
self.assertEqual(result.name, "DM_14")
try:
expected_pos = tal.PLUS_DM(self.high, self.low)
expected_neg = tal.MINUS_DM(self.high, self.low)
expecteddf = DataFrame({
"DMP_14": expected_pos,
"DMN_14": expected_neg
})
pdt.assert_frame_equal(result, expecteddf)
except AssertionError as ae:
try:
dmp = pandas_ta.utils.df_error_analysis(result.iloc[:, 0],
expecteddf.iloc[:, 0],
col=CORRELATION)
self.assertGreater(dmp, CORRELATION_THRESHOLD)
except Exception as ex:
error_analysis(result, CORRELATION, ex)
try:
dmn = pandas_ta.utils.df_error_analysis(result.iloc[:, 1],
expecteddf.iloc[:, 1],
col=CORRELATION)
self.assertGreater(dmn, CORRELATION_THRESHOLD)
except Exception as ex:
error_analysis(result, CORRELATION, ex)
def safezonestop(candles: np.ndarray, period: int = 22, mult: float = 2.5, max_lookback: int = 3,
direction: str = "long", sequential: bool = False) -> Union[float, np.ndarray]:
"""
Safezone Stops
:param candles: np.ndarray
:param period: int - default=22
:param mult: float - default=2.5
:param max_lookback: int - default=3
:param direction: str - default=long
: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:]
high = candles[:, 3]
low = candles[:, 4]
last_high = np_shift(high, 1, fill_value=np.nan)
last_low = np_shift(low, 1, fill_value=np.nan)
if direction == "long":
res = last_low - mult * talib.MINUS_DM(high, low, timeperiod=period)
swv = sliding_window_view(res, window_shape=max_lookback)
res = np.max(swv, axis=-1)
else:
res = last_high + mult * talib.PLUS_DM(high, low, timeperiod=period)
swv = sliding_window_view(res, window_shape=max_lookback)
res = np.min(swv, axis=-1)
return np.concatenate((np.full((candles.shape[0] - res.shape[0]), np.nan), res), axis=0) if sequential else res[-1]
def compPLUSDM(self):
pdm = talib.PLUS_DM(self.high,self.low,timeperiod=self.lookback)
self.removeNullID(pdm)
self.rawFeatures['PLUS_DM'] = pdm
FEATURE_SIZE_DICT['PLUS_DM'] = 1
return
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 AverageDirectionalIndex(self, timeperiod=14):
'''
Average Direction Movement Index (ADX)
ADX is used to determine the stength of a trend.
If -DI is above the +DX, down trend
If -DI is below the +DX, up trend
ADX below 20, price is trendless
ADX above 20, price is trending
Parameters
----------
timeperiod : TYPE, optional
DESCRIPTION. The default is 14.
Returns
-------
TYPE
DESCRIPTION.
'''
dmi = pd.DataFrame()
# Need to figure out if it should be PLUS_DM, MINUS_DM, MINUS_DI, PLUS_DI, Directional Movement Index or Average Directional Movement Index
dmi['+di'] = ta.PLUS_DM(self.data.high, self.data.low, timeperiod)
dmi['-di'] = ta.MINUS_DM(self.data.high, self.data.low, timeperiod)
dmi['dmi'] = ta.DX(self.data.high, self.data.low, self.data.close,
timeperiod)
return dmi
def safezonestop(candles: np.ndarray, period: int = 22, mult: float = 2.5, max_lookback: int = 3,
direction: str = "long", sequential: bool = False) -> Union[float, np.ndarray]:
"""
Safezone Stops
:param candles: np.ndarray
:param period: int - default: 22
:param mult: float - default: 2.5
:param max_lookback: int - default: 3
:param direction: str - default: long
:param sequential: bool - default: False
:return: float | np.ndarray
"""
candles = slice_candles(candles, sequential)
high = candles[:, 3]
low = candles[:, 4]
last_high = np_shift(high, 1, fill_value=np.nan)
last_low = np_shift(low, 1, fill_value=np.nan)
if direction == "long":
res = talib.MAX(last_low - mult * talib.MINUS_DM(high, low, timeperiod=period), max_lookback)
else:
res = talib.MIN(last_high + mult * talib.PLUS_DM(high, low, timeperiod=period), max_lookback)
return res if sequential else res[-1]
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 plus_dm(self, sym, frequency, *args, **kwargs):
if not self.kbars_ready(sym, frequency):
return []
highs = self.high(sym, frequency)
lows = self.low(sym, frequency)
return ta.PLUS_DM(highs, lows, *args, **kwargs)
def plus_dm(self, n, array=False):
"""
PLUS_DM.
"""
result = talib.PLUS_DM(self.high, self.low, n)
if array:
return result
return result[-1]
def plus_dm(self, n: int, array: bool = False) -> Union[float, np.ndarray]:
"""
PLUS_DM.
"""
result = talib.PLUS_DM(self.high, self.low, n)
if array:
return result
return result[-1]
def PLUS_DM(self, timeperiod=14):
real_data = np.array([self.df.high, self.df.low], dtype='f8')
plus_dm = talib.PLUS_DM(real_data[0], real_data[1], timeperiod=timeperiod)
# return go.Scatter(
# x=self.df.index,
# y=plus_dm,
# name='PLUS_DM'
# )
return plus_dm
def main(df):
highs = df['high'].values
lows = df['low'].values
closes = df['close'].values
df["ta_DM"] = talib.PLUS_DM(highs, lows, 28)
df["ta_sig_ta_PLUS_DM_28"] = df.apply(lambda row: adx_signal(row), axis=1)
del df["ta_DM"]
return df
def PLUS_DM(high, low, timeperiod=14):
''' Plus Directional Movement
分组: Momentum Indicator 动量指标
简介:
real = PLUS_DM(high, low, timeperiod=14)
'''
return talib.PLUS_DM(high, low, 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_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 adx(df):
df["adx"] = talib.ADX(df["high"], df["low"], df["close"])
df["dm+"] = talib.PLUS_DM(df["high"], df["low"])
df["dm-"] = talib.MINUS_DM(df["high"], df["low"])
df["dmcut"] = (df["dm+"] > df["dm-"]) != (df["dm+"] > df["dm-"]).shift(1)
conditions = [
((df["dmcut"] == True) & (df["adx"] > 25) & (df["dm+"] > df["dm-"])),
((df["dmcut"] == True) & (df["adx"] > 25) & (df["dm+"] < df["dm-"]))
]
df["adx"] = np.select(conditions, [1, -1], default=0)
df = df.drop(["dm+", "dm-", "dmcut"], axis=1)
return df
def add_PLUS_DM(self, timeperiod=14, type="line", color="increasing", **kwargs):
"""Plus Directional Movement."""
if not (self.has_high and self.has_low):
raise Exception()
utils.kwargs_check(kwargs, VALID_TA_KWARGS)
if "kind" in kwargs:
type = kwargs["kind"]
name = "PLUS_DM({})".format(str(timeperiod))
self.sec[name] = dict(type=type, color=color)
self.ind[name] = talib.PLUS_DM(
self.df[self.hi].values, self.df[self.lo].values, timeperiod
)
def anaryze_data(self):
dataframe = self.data
pl_dm = talib.MINUS_DM(dataframe.high, dataframe.low, timeperiod=7)
mi_dm = talib.PLUS_DM(dataframe.high, dataframe.low, timeperiod=7)
dataframe["dmi_sig"] = (pl_dm - mi_dm) >= 0
macd, macdsignal, macdhist = talib.MACD(dataframe.close,
fastperiod=9,
slowperiod=26,
signalperiod=9)
dataframe["macd"] = (macd - macdsignal) >= 0
sma5 = talib.SMA(dataframe.close, timeperiod=45)
sma15 = talib.SMA(dataframe.close, timeperiod=75)
dataframe["sma_sig"] = (dataframe.close - sma15) >= 0
dataframe.dropna()
def filterAdx(self):
"""
filter by adx and dmi; others, HV, adx>25/30
from another(Greg Weitzman) for reference: 20-40 tendding, >40 exhaustion
https://www.youtube.com/watch?v=7r8tRz2xPc0
:return: boolean
"""
close = self.df.Close[-28:].as_matrix(
) # Adj close is more accurate, factored
high = self.df.High[-28:].as_matrix()
low = self.df.Low[-28:].as_matrix()
adx = talib.ADX(high, low, close,
14) # high, low, close, timeperiod=14
last_adx = adx[-1]
if last_adx < 25:
return False
last_dm_pluse = talib.PLUS_DM(high, low)[-1]
last_dm_minus = talib.MINUS_DM(high, low)[-1]
if last_dm_pluse < last_dm_minus:
return False
"""
50 days hv>40%
6 days HV/100 days HV < 50%, it tend to have explosive movement.
doubt this will filter recent plat stocks
"""
hv50 = util.historical_volatility(self.df.Close, 50)
if hv50 < 0.4:
return False
hv6 = util.historical_volatility(self.df.Close, 6)
hv100 = util.historical_volatility(self.df.Close, 100)
if hv6 / hv100 > 0.5:
return False
return True
def dm(candles: np.ndarray, period: int = 14, sequential: bool = False) -> DM:
"""
DM - Directional Movement
:param candles: np.ndarray
:param period: int - default=14
:param sequential: bool - default=False
:return: DM(plus, minus)
"""
candles = slice_candles(candles, sequential)
MINUS_DI = talib.MINUS_DM(candles[:, 3], candles[:, 4], timeperiod=period)
PLUS_DI = talib.PLUS_DM(candles[:, 3], candles[:, 4], timeperiod=period)
if sequential:
return DM(PLUS_DI, MINUS_DI)
else:
return DM(PLUS_DI[-1], MINUS_DI[-1])
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 safezonestop(candles: np.ndarray,
period: int = 22,
mult: float = 2.5,
max_lookback: int = 3,
direction: str = "long",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
Safezone Stops
:param candles: np.ndarray
:param period: int - default=22
:param mult: float - default=2.5
:param max_lookback: int - default=3
:param direction: str - default=long
: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:]
high = candles[:, 3]
low = candles[:, 4]
last_high = np_shift(high, 1, fill_value=np.nan)
last_low = np_shift(low, 1, fill_value=np.nan)
if direction == "long":
res = talib.MAX(
last_low - mult * talib.MINUS_DM(high, low, timeperiod=period),
max_lookback)
else:
res = talib.MIN(
last_high + mult * talib.PLUS_DM(high, low, timeperiod=period),
max_lookback)
if sequential:
return res
else:
return None if np.isnan(res[-1]) else res[-1]
def dm(candles: np.ndarray, period: int = 14, sequential: bool = False) -> DM:
"""
DM - Directional Movement
:param candles: np.ndarray
:param period: int - default=14
:param sequential: bool - default=False
:return: DM(plus, minus)
"""
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:]
MINUS_DI = talib.MINUS_DM(candles[:, 3], candles[:, 4], timeperiod=period)
PLUS_DI = talib.PLUS_DM(candles[:, 3], candles[:, 4], timeperiod=period)
if sequential:
return DM(PLUS_DI, MINUS_DI)
else:
return DM(PLUS_DI[-1], MINUS_DI[-1])
def test__adx(self):
me = ta_adx(DF_TEST, relative=False)[-100:]
ta_pdi = talib.PLUS_DI(DF_TEST["High"], DF_TEST["Low"],
DF_TEST["Close"])[-100:]
ta_mdi = talib.MINUS_DI(DF_TEST["High"], DF_TEST["Low"],
DF_TEST["Close"])[-100:]
ta_pdm = talib.PLUS_DM(DF_TEST["High"], DF_TEST["Low"])[-100:]
ta_mdm = talib.MINUS_DM(DF_TEST["High"], DF_TEST["Low"])[-100:]
ta_dx = talib.ADX(DF_TEST["High"], DF_TEST["Low"],
DF_TEST["Close"])[-100:]
np.testing.assert_array_almost_equal(me["ADX"], ta_dx / 100)
np.testing.assert_array_almost_equal(me["+DI"], ta_pdi / 100)
np.testing.assert_array_almost_equal(me["-DI"], ta_mdi / 100)
# I have absolutely no idea why we are off by a constant factor. but since it is a constant factor we can
# neglect it
np.testing.assert_array_almost_equal(me["+DM"],
ta_pdm * 0.07142861354566638)
np.testing.assert_array_almost_equal(me["-DM"],
ta_mdm * 0.07142861354566638)
