def wma_obv(self):
df2 = self.df
df2.rename(columns={'obv': 'close'})
try:
self.wma_obv = abstract.WMA(df2, 233)
except:
pprint(sys.exc_info())
def WMA(ohlcv, kw):
""" :return Weighted Moving Average (wma) """
params = {'timeperiod': 30}
timeperiod = _get_params(kw, params, ['timeperiod'])[0]
result = talib.WMA(ohlcv, timeperiod)
return {
'wma': result
}
def technical_index(self):
df = self.max_min_price()
df2 = self.institutional_investors()
df['RSI'] = abstract.RSI(df) / 100
df['CMO'] =(abstract.CMO(df)+100) / (2 *100)
df['MACD'] =(abstract.MACD(df)['macd']+abstract.MACD(df)['macd'].max()) / (2 *abstract.MACD(df)['macd'].max())
df['WILLR'] =(abstract.WILLR(df)+100) / (2 *100)
df['WMA'] =abstract.WMA(df) / abstract.WMA(df).max()
df['PPO'] =(abstract.PPO(df)+abstract.PPO(df).max()) / (2 *abstract.PPO(df).max())
df['EMA'] =abstract.EMA(df) / abstract.EMA(df).max()
df['ROC'] =(abstract.ROC(df)+abstract.ROC(df).max()) / (2 *abstract.ROC(df).max())
df['SMA'] =abstract.SMA(df) / abstract.SMA(df).max()
df['TEMA'] =abstract.TEMA(df) / abstract.TEMA(df).max()
df['CCI'] =(abstract.CCI(df)+abstract.CCI(df).max()) / (2 *abstract.CCI(df).max())
df['investment_trust'] = (df2['investment_trust'] + df2['investment_trust'].max()) / (2*df2['investment_trust'].max())
df['foreign_investor'] = (df2['foreign_investor'] + df2['foreign_investor'].max()) / (2*df2['foreign_investor'].max())
df = df.drop(columns=['volume', 'open', 'high', 'low', 'close', 'close_max', 'close_min'])
df = df.dropna()
return df
def scan_wma_obv_sloping_up(self):
self.df["both_sloping_up"] = 0
wma = abstract.WMA(self.df, 233)
df2 = self.df
df2.rename(columns={'obv': 'close'})
wma_obv = abstract.WMA(df2, 233)
for i in range(0, len(wma_obv)):
try:
a = wma_obv[i - 1]
b = wma_obv[i]
c = wma[i - 1]
d = wma[i]
if a < b and c < d:
self.df['both_sloping_up'][i] = 1
except:
pass
return self.df
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
def wma(self):
real = tb.WMA(self.close, timeperiod=30)
return real
def cal_wma(self):
self.analysis_data['wma5'] = ta.WMA(self.close, timeperiod=5)
self.analysis_data['wma20'] = ta.WMA(self.close, timeperiod=20)
self.analysis_data['wma60'] = ta.WMA(self.close, timeperiod=60)
self.analysis_data['wma120'] = ta.WMA(self.close, timeperiod=120)
def VMA(self):
real = tb.WMA(self.close, timeperiod=30)