def DEMA(ohlcv, kw):
""" :return Double Exponential Moving Average (dema) """
params = {'timeperiod': 30}
timeperiod = _get_params(kw, params, ['timeperiod'])[0]
result = talib.DEMA(ohlcv, timeperiod)
return {
'dema': result
}
def get_overlap_studies(self):
# https://mrjbq7.github.io/ta-lib/func_groups/overlap_studies.html
if self.verbose:
print self.ticker, 'get_overlap_studies'
_a = ['open', 'high', 'low', 'close', 'volume']
inputs = {_a[i]: self.data[_a[i]].values for i in range(len(_a))}
# simple moving average
self.data['os_sma_20'] = abstract.SMA(inputs, timeperiod=20)
self.data['os_sma_50'] = abstract.SMA(inputs, timeperiod=50)
self.data['os_sma_200'] = abstract.SMA(inputs, timeperiod=200)
# bollinger bands
self.data['os_bbu_20'], self.data['os_bbm_20'], self.data[
'os_bbl_20'] = abstract.BBANDS(inputs,
timeperiod=20,
nbdevup=2,
nbdevdn=2,
matype=0)
# double exponential moving average
self.data['os_dema_20'] = abstract.DEMA(inputs, timeperiod=20)
# exponential moving average
self.data['os_ema_20'] = abstract.EMA(inputs, timeperiod=20)
# midpoint over period
self.data['os_mp_14'] = abstract.MIDPOINT(inputs, timeperiod=14)
# parabolic SAR
self.data['os_sar'] = abstract.SAR(inputs, acceleration=0, maximum=0)
# triple exponential moving average
self.data['os_tema_5'] = abstract.TEMA(inputs, timeperiod=5)
# triangular moving average
self.data['os_trima_30'] = abstract.TRIMA(inputs, timeperiod=30)
# weighted moving average
self.data['os_wma_30'] = abstract.WMA(inputs, timeperiod=30)
def PMAX(dataframe, period=4, multiplier=0.1, length=4, MAtype=7, src=1):
import talib.abstract as ta
df = dataframe.copy()
mavalue = 'MA_' + str(MAtype) + '_' + str(length)
atr = 'ATR_' + str(period)
df[atr] = ta.ATR(df, timeperiod=period)
pm = 'pm_' + str(period) + '_' + str(multiplier) + '_' + str(length) + '_' + str(MAtype)
pmx = 'pmX_' + str(period) + '_' + str(multiplier) + '_' + str(length) + '_' + str(MAtype)
if src == 1:
masrc = df["close"]
elif src == 2:
masrc = (df["high"] + df["low"]) / 2
elif src == 3:
masrc = (df["high"] + df["low"] + df["close"] + df["open"]) / 4
if MAtype == 1:
df[mavalue] = ta.EMA(masrc, timeperiod=length)
elif MAtype == 2:
df[mavalue] = ta.DEMA(masrc, timeperiod=length)
elif MAtype == 3:
df[mavalue] = ta.T3(masrc, timeperiod=length)
elif MAtype == 4:
df[mavalue] = ta.SMA(masrc, timeperiod=length)
elif MAtype == 5:
df[mavalue] = VIDYA(df, length=length)
elif MAtype == 6:
df[mavalue] = ta.TEMA(masrc, timeperiod=length)
elif MAtype == 7:
df[mavalue] = ta.WMA(df, timeperiod=length)
elif MAtype == 8:
df[mavalue] = vwma(df, length)
elif MAtype == 9:
df[mavalue] = zema(df, period=length)
# Compute basic upper and lower bands
df['basic_ub'] = df[mavalue] + (multiplier * df[atr])
df['basic_lb'] = df[mavalue] - (multiplier * df[atr])
# Compute final upper and lower bands
df['final_ub'] = 0.00
df['final_lb'] = 0.00
for i in range(period, len(df)):
df['final_ub'].iat[i] = df['basic_ub'].iat[i] if (
df['basic_ub'].iat[i] < df['final_ub'].iat[i - 1]
or df[mavalue].iat[i - 1] > df['final_ub'].iat[i - 1]) else df['final_ub'].iat[i - 1]
df['final_lb'].iat[i] = df['basic_lb'].iat[i] if (
df['basic_lb'].iat[i] > df['final_lb'].iat[i - 1]
or df[mavalue].iat[i - 1] < df['final_lb'].iat[i - 1]) else df['final_lb'].iat[i - 1]
df[pm] = 0.00
for i in range(period, len(df)):
df[pm].iat[i] = (
df['final_ub'].iat[i] if (df[pm].iat[i - 1] == df['final_ub'].iat[i - 1]
and df[mavalue].iat[i] <= df['final_ub'].iat[i])
else df['final_lb'].iat[i] if (
df[pm].iat[i - 1] == df['final_ub'].iat[i - 1]
and df[mavalue].iat[i] > df['final_ub'].iat[i]) else df['final_lb'].iat[i]
if (df[pm].iat[i - 1] == df['final_lb'].iat[i - 1]
and df[mavalue].iat[i] >= df['final_lb'].iat[i]) else df['final_ub'].iat[i]
if (df[pm].iat[i - 1] == df['final_lb'].iat[i - 1]
and df[mavalue].iat[i] < df['final_lb'].iat[i]) else 0.00)
# up/down belirteçi / main logic
df[pmx] = np.where((df[pm] > 0.00), np.where((df[mavalue] < df[pm]), 'down', 'up'), np.NaN)
df.drop(['basic_ub', 'basic_lb', 'final_ub', 'final_lb'], inplace=True, axis=1)
df.fillna(0, inplace=True)
return df