def BBANDS(cls, data, ndays):
MA = pd.Series(pd.rolling_mean(data['Close'], ndays))
SD = pd.Series(pd.rolling_std(data['Close'], ndays))
b1 = MA + (2 * SD)
B1 = pd.Series(b1, name='Upper BollingerBand')
data = data.join(B1)
b2 = MA - (2 * SD)
B2 = pd.Series(b2, name='Lower BollingerBand')
data = data.join(B2)
return data
def BBANDS(data, ndays):
MA = pd.Series(pd.rolling_mean(data['Close'], ndays))
SD = pd.Series(pd.rolling_std(data['Close'], ndays))
b1 = MA + (2 * SD)
B1 = pd.Series(b1, name='Upper BollingerBand')
data = data.join(B1)
b2 = MA - (2 * SD)
B2 = pd.Series(b2, name='Lower BollingerBand')
data = data.join(B2)
return data
def CCI(data, ndays):
TP = (data['High'] + data['Low'] + data['Close']) / 3
CCI = pd.Series((TP - pd.rolling_mean(TP, ndays)) /
(0.015 * pd.rolling_std(TP, ndays)),
name='CCI')
data = data.join(CCI)
return data
def EVM(data, ndays):
dm = ((data['High'] + data['Low']) / 2) - (
(data['High'].shift(1) + data['Low'].shift(1)) / 2)
br = (data['Volume'] / 100000000) / ((data['High'] - data['Low']))
EVM = dm / br
EVM_MA = pd.Series(pd.rolling_mean(EVM, ndays), name='EVM')
data = data.join(EVM_MA)
return data
def SMA(data, ndays):
SMA = pd.Series(pd.rolling_mean(data['Close'], ndays), name='SMA')
data = data.join(SMA)
return data
def EWMA(data, ndays):
EMA = pd.Series(pd.ewma(data['Close'], span=ndays, min_periods=ndays - 1),
name='EWMA_' + str(ndays))
data = data.join(EMA)
return data
def ForceIndex(data, ndays):
FI = pd.Series(data['Close'].diff(ndays) * data['Volume'],
name='ForceIndex')
data = data.join(FI)
return data
def ROC(data,n):
N = data['Close'].diff(n)
D = data['Close'].shift(n)
ROC = pd.Series(N/D,name='Rate of Change')
data = data.join(ROC)
return data