Module indicators

Functions

def ADX(data: numpy.ndarray, index: List[datetime.datetime], features: List[str], previous_data: numpy.ndarray, period: int = 14) ‑> numpy.ndarray

Data is a 2D numpy array with the shape (n, f) where n is the number of observations. The first 4 columns of the array are the OHLC data.

def ADXR(data: numpy.ndarray, index: List[datetime.datetime], features: List[str], previous_data: numpy.ndarray, period: int = 14) ‑> numpy.ndarray

Data is a 2D numpy array with the shape (n, f) where n is the number of observations. The first 4 columns of the array are the OHLC data.

def APO(data: numpy.ndarray, index: List[datetime.datetime], features: List[str], previous_data: numpy.ndarray, fastperiod: int = 12, slowperiod: int = 26, matype: int = 0) ‑> numpy.ndarray

Data is a 2D numpy array with the shape (n, f) where n is the number of observations. The first 4 columns of the array are the OHLC data.

def AROON(data: numpy.ndarray, index: List[datetime.datetime], features: List[str], previous_data: numpy.ndarray, period: int = 14) ‑> numpy.ndarray

Data is a 2D numpy array with the shape (n, f) where n is the number of observations. The first 4 columns of the array are the OHLC data.

def AROONOSC(data: numpy.ndarray, index: List[datetime.datetime], features: List[str], previous_data: numpy.ndarray, period: int = 14) ‑> numpy.ndarray

Data is a 2D numpy array with the shape (n, f) where n is the number of observations. The first 4 columns of the array are the OHLC data.

def BOP(data: numpy.ndarray, index: List[datetime.datetime], features: List[str], previous_data: numpy.ndarray) ‑> numpy.ndarray

Data is a 2D numpy array with the shape (n, f) where n is the number of observations. The first 4 columns of the array are the OHLC data.

def CCI(data: numpy.ndarray, index: List[datetime.datetime], features: List[str], previous_data: numpy.ndarray, period: int = 14) ‑> numpy.ndarray

Data is a 2D numpy array with the shape (n, f) where n is the number of observations. The first 4 columns of the array are the OHLC data.

def CMO(data: numpy.ndarray, index: List[datetime.datetime], features: List[str], previous_data: numpy.ndarray, period: int = 14) ‑> numpy.ndarray

Data is a 2D numpy array with the shape (n, f) where n is the number of observations. The first 4 columns of the array are the OHLC data.

def DX(data: numpy.ndarray, index: List[datetime.datetime], features: List[str], previous_data: numpy.ndarray, period: int = 14) ‑> numpy.ndarray

Data is a 2D numpy array with the shape (n, f) where n is the number of observations. The first 4 columns of the array are the OHLC data.

def EMA(data: numpy.ndarray, index: List[datetime.datetime], features: List[str], previous_data: numpy.ndarray, period: int = 10) ‑> numpy.ndarray

This EMA indicator support the streaming feature. If the previous_data is not None, the indicator will fill the missing values in the previous_data and return it. If the previous_data is None, it will calculate the indicator for the whole data.

def MACD(data: numpy.ndarray, index: List[datetime.datetime], features: List[str], previous_data: numpy.ndarray, fastperiod: int = 12, slowperiod: int = 26, signalperiod: int = 9) ‑> numpy.ndarray

Data is a 2D numpy array with the shape (n, f) where n is the number of observations. The first 4 columns of the array are the OHLC data.

def SMA(data: numpy.ndarray, index: List[datetime.datetime], features: List[str], previous_data: numpy.ndarray, period: int = 10) ‑> numpy.ndarray

This SMA indicator support the streaming feature. If the previous_data is not None, the indicator will fill the missing values in the previous_data and return it. If the previous_data is None, it will calculate the indicator for the whole data.