def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
"""
Adds several different TA indicators to the given DataFrame
Performance Note: For the best performance be frugal on the number of indicators
you are using. Let uncomment only the indicator you are using in your strategies
or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
"""
# Commodity Channel Index: values Oversold:<-100, Overbought:>100
dataframe['cci'] = ta.CCI(dataframe)
# MFI
dataframe['mfi'] = ta.MFI(dataframe)
# CMO
dataframe['cmo'] = ta.CMO(dataframe)
return dataframe
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 evaluate_cmo(self,
period=20,
prefix="cmo",
impact_buy=1,
impact_sell=1):
"""
evaluates the cmo
:param dataframe:
:param period:
:param prefix:
:return:
"""
self._weights(impact_buy, impact_sell)
dataframe = self.dataframe
name = f"{prefix}_{period}"
dataframe[name] = ta.CMO(dataframe, timeperiod=period)
dataframe.loc[((dataframe[name] < -50)),
f"buy_{name}"] = 1 * impact_buy
dataframe.loc[((dataframe[name] > 50)),
f"sell_{name}"] = 1 * impact_sell
def evaluate_cmo(self,
period=20,
prefix="cmo",
impact_buy=1,
impact_sell=1):
"""
evaluates the osc
:param dataframe:
:param period:
:param prefix:
:return:
"""
self._weights(impact_buy, impact_sell)
dataframe = self.dataframe
name = '{}_{}'.format(prefix, period)
dataframe[name] = ta.CMO(dataframe, timeperiod=period)
dataframe.loc[((dataframe[name] < -50)),
'buy_{}'.format(name)] = (1 * impact_buy)
dataframe.loc[((dataframe[name] > 50)),
'sell_{}'.format(name)] = (1 * impact_sell)
def __countCMO(self): self.cmo = ta.CMO(self.stock.inputs)
def CMO(self):
cmo = abstract.CMO(self.company_stock, timeperiod=14)
self.company_stock['CMO'] = cmo