볼린저밴드
def bollinger_band(data, w = 20, k = 2):
data = data.copy()
mbb = data['close'].rolling(w).mean()
ubb = mbb + k * data['close'].rolling(w).std()
lbb = mbb - k * data['close'].rolling(w).std()
data['width'] = ubb - lbb
# 볼린저 밴드에서 종가 위치
data['bollinger_band'] = (data['close'] - lbb) / data['width']
# 볼린저 밴드 넓이 증가량
data['bollinger_band_ratio'] = np.zeros(len(data))
data.loc[1:, 'bollinger_band_ratio']= (data['width'][1:].values - data['width'][:-1].values) / data['width'][:-1].values
data.drop(['width'], axis = 1, inplace = True)
return dataMACD
def cal_MACD(data, num_long=12, num_short=26, num_signal=9):
data = data.copy()
ema_long = data['close'].ewm(span=num_long, min_periods=num_long - 1).mean()
ema_short = data['close'].ewm(span=num_short, min_periods=num_short - 1).mean()
MACD = ema_long - ema_short
MACD_signal = MACD.ewm(span=num_signal, min_periods=num_signal - 1).mean()
data['MACD_diff'] = MACD - MACD_signal
# MACD cross
data['MACD_cross'] = pd.Series(np.where(data['MACD_diff'] >= 0, 1, -1), index=data.index)
# 지난 MACD 대비 MACD 비율
data['MACD_lastMACD_ratio'] = np.zeros(len(data))
data.loc[1:, 'MACD_lastMACD_ratio'] = (data['MACD_diff'][1:].values - data['MACD_diff'][:-1].values) / data[
'MACD_diff'][
:-1].values
data.drop('MACD_diff', axis=1, inplace=True)
return dataRSI
def cal_RSI(data, period = 9):
data = data.copy()
U = np.where(data['close'].diff(1) > 0, data['close'].diff(1), 0)
D = np.where(data['close'].diff(1) < 0, data['close'].diff(1) * (-1), 0)
AU = pd.Series(U, index = data.index).rolling(window=period, min_periods=period).mean()
AD = pd.Series(D, index = data.index).rolling(window=period, min_periods=period).mean()
RS = AU / AD
data['RSI'] = 1 - 1 / (1 + RS)
return dataStochastic Oscilator
def cal_stochastic_oscillator(data, n = 5):
data = data.copy()
size = len(data)
temp=[]
for i in range(size):
if i >= n-1:
tempUp = data['close'][i] - min(data['low'][i-n+1:i+1])
tempDown = max(data['high'][i-n+1:i+1]) - min(data['low'][i-n+1:i+1])
if tempDown == 0:
tempDown = 0.001
temp.append(tempUp / tempDown)
else:
temp.append(0) #n보다 작은 초기값은 0 설정
data['sto_K'] = pd.Series(temp, index=data.index)
data['sto_D'] = data['sto_K'].rolling(3).mean()
return data
OBV
def cal_OBV(data, n=9):
data = data.copy()
OBV = []
OBV.append(data['volume'][0])
for i in range(1, len(data)):
if data['close'][i] > data['close'][i - 1]:
OBV.append(OBV[-1] + data['volume'][i])
elif data['close'][i] < data['close'][i - 1]:
OBV.append(OBV[-1] - data['volume'][i])
else:
OBV.append(OBV[-1])
OBV = pd.Series(OBV, index=data.index)
data['OBV_ewm'] = OBV.ewm(n).mean()
# OBV signal
data['OBV_cross'] = pd.Series(np.where(OBV >= data['OBV_ewm'], 1,-1), index = data.index)
# 지난 OBV_ewm 대비 OBV_ewm 비율
data['OBV_lastOBV_ratio'] = np.zeros(len(data))
data.loc[1:, 'OBV_lastOBV_ratio'] = (data['OBV_ewm'][1:].values - data['OBV_ewm'][:-1].values) / data['OBV_ewm'][
:-1].values
data.drop('OBV_ewm', axis=1, inplace=True)
return data
Log Return
def cal_log_return(data):
data = data.copy()
data['log_return'] = np.zeros(len(data))
data['log_return'] = np.log(data['close'] / data['close'].shift(1))
return data
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