def get_input_name(inputs):
parts = []
if isinstance(inputs, list):
parts.extend([Indicator.get_input_name(i) for i in inputs])
else:
# parts.extend(Indicator.get_input_name(inputs))
parts.append(Indicator.get_input_name(inputs))
return ",".join(str(part) for part in parts)
def __init__(self, name, inputs, input_keys, length=None, desc=None, **kwargs):
super(PipeLine, self).__init__(name=name, keys=None, desc=desc, **kwargs)
# f = lambda i: i \
# if isinstance(i, DataSeries) or isinstance(i, Indicator) \
# else inst_data_mgr.get_series(i)
# if isinstance(inputs, list):
# self.inputs = [f(i) for i in inputs]
# else:
# self.inputs = f(inputs)
input_names = []
self.input_names_and_series = OrderedDict()
if isinstance(inputs, list):
for i in inputs:
if isinstance(i, DataSeries):
input_name = DataSeries.get_name(i)
input_names.append(input_name)
self.input_names_and_series[input_name] = i
elif isinstance(i, Indicator):
input_name = Indicator.get_name(i)
input_names.append(input_name)
self.input_names_and_series[input_name] = i
elif isinstance(i, PipeLine):
input_name = PipeLine.get_name(i)
input_names.append(input_name)
self.input_names_and_series[input_name] = i
else:
input_names.append(i)
else:
if isinstance(inputs, DataSeries):
input_name = DataSeries.get_name(inputs)
input_names.append(input_name)
self.input_names_and_series[input_name] = inputs
elif isinstance(inputs, Indicator):
input_name = Indicator.get_name(inputs)
input_names.append(input_name)
self.input_names_and_series[input_name] = inputs
elif isinstance(inputs, PipeLine):
input_name = PipeLine.get_name(inputs)
input_names.append(input_name)
self.input_names_and_series[input_name] = inputs
else:
input_names.append(inputs)
self.numPipes = len(input_names)
self.length = length if length is not None else 1
self.input_names = input_names
self.input_names_pos = dict(zip(input_names,
range(len(input_names))))
self.input_keys = self._get_key(input_keys, None)
# self.calculate = True
self.__curr_timestamp = None
self._flush_and_create()
self.inputs = []
def __init__(self, input=None, input_key=None, length=14, num_std=3, desc="Bollinger Bands"):
self.length = int(length)
self.num_std = int(num_std)
self.__sma = SMA(input, self.length)
self.__std_dev = STD(input, self.length)
super(BB, self).__init__(Indicator.get_name(BB.__name__, input, input_key, length, num_std), input, input_key,
desc)
def __init__(self, input=None, length=14, desc="Average True Range"):
self.length = int(length)
self.__prev_close = None
self.__value = None
self.__average = SMA(input, self.length)
super(ATR, self).__init__(Indicator.get_name(ATR.__name__, input, length), input, ['high', 'low', 'close'],
desc)
def __init__(self, input=None, length=14, desc="Average True Range"):
self.length = int(length)
self.__prev_close = None
self.__value = None
self.__average = SMA(input, self.length)
super(ATR,
self).__init__(Indicator.get_name(ATR.__name__, input, length),
input, ['high', 'low', 'close'], desc)
def __init__(self,
input,
input_key=None,
length=0,
desc="TALib Simple Moving Average"):
self.length = int(length)
super(SMA, self).__init__(
Indicator.get_name(SMA.__name__, input, input_key, length), input,
input_key, desc)
def __init__(self,
input=None,
input_key=None,
length=1,
desc="Rate Of Change"):
self.length = int(length)
super(ROC, self).__init__(
Indicator.get_name(ROC.__name__, input, input_key, length), input,
input_key, desc)
def __init__(self,
input,
input_key=None,
length=0,
desc="Standard Deviation"):
super(STD, self).__init__(
Indicator.get_name(STD.__class__, input, input_key, length), input,
input_key, desc)
self.length = int(length)
super(STD, self).update_all()
def __init__(self,
input,
input_key=None,
length=0,
desc="TALib Exponential Moving Average"):
super(EMA, self).__init__(
Indicator.get_name(EMA.__name__, input, input_key, length), input,
input_key, desc)
self.length = int(length)
super(EMA, self).update_all()
def __init__(self,
input=None,
input_key=None,
length=0,
ann_factor=252,
desc="Historical Volatility"):
self.length = int(length)
self.ann_factor = ann_factor
super(HistoricalVolatility, self).__init__(
Indicator.get_name(HistoricalVolatility.__name__, input, input_key,
length), input, input_key, desc)
def __init__(self,
input=None,
input_key=None,
length=14,
desc="Relative Strength Indicator"):
super(RSI, self).__init__(
Indicator.get_name(RSI.__name__, input, input_key, length), input,
input_key, desc)
self.length = int(length)
self.__prev_gain = None
self.__prev_loss = None
super(RSI, self).update_all()
def __init__(self,
input,
input_key='close',
length=30,
desc="Rolling Standard Deviation"):
super(StdDev,
self).__init__(input,
func=lambda x: np.std(x, axis=0),
name=Indicator.get_name(StdDev.__name__, input,
input_key, length),
length=length,
input_key=input_key,
desc=desc)
def __init__(self,
input=None,
input_key=None,
length=14,
num_std=3,
desc="Bollinger Bands"):
self.length = int(length)
self.num_std = int(num_std)
self.__sma = SMA(input, self.length)
self.__std_dev = STD(input, self.length)
super(BB, self).__init__(
Indicator.get_name(BB.__name__, input, input_key, length, num_std),
input, input_key, desc)
def __init__(self,
input,
input_key='close',
length=30,
desc="Rolling Kurtosis"):
super(Kurtosis,
self).__init__(input,
func=lambda x: pd_kurtosis_wrapper(x),
name=Indicator.get_name(Kurtosis.__name__, input,
input_key, length),
length=length,
input_key=input_key,
desc=desc)
def __init__(self,
input,
input_key='close',
length=30,
desc="Rolling Skew"):
super(Skew,
self).__init__(input,
func=lambda x: pd_skew_wrapper(x),
name=Indicator.get_name(Skew.__name__, input,
input_key, length),
length=length,
input_key=input_key,
desc=desc)
def get_name(input, length):
return "KalmanFilteringPairRegression(%s,%s)" % (
Indicator.get_input_name(input), length)
def __init__(self, input=None, input_key=None, length=0, desc="Simple Moving Average", **kwargs):
self.length = int(length)
super(SMA, self).__init__(Indicator.get_name(SMA.__name__, input, input_key, length), input, input_key, desc,
**kwargs)
def __init__(self, input, input_key=None, length=0, desc="Variance"):
super(VAR, self).__init__(Indicator.get_name(VAR.__class__, input, input_key, length), input, input_key,
desc)
self.length = int(length)
def __init__(self, input, input_key='close', length=30, desc="Rolling Kurt"):
super(Kurt, self).__init__(input,
func=lambda x: pd_kurt_wrapper(x),
name=Indicator.get_name(Kurt.__name__, input, input_key, length),
length=length,
input_key=input_key, desc=desc)
def __init__(self, input, input_key=None, length=0, desc="Minimum"):
super(MIN, self).__init__(Indicator.get_name(MIN.__class__, input, input_key, length), input, input_key,
desc)
self.length = int(length)
super(MIN, self).update_all()
def __init__(self, input=None, input_key=None, length=14, desc="Relative Strength Indicator"):
super(RSI, self).__init__(Indicator.get_name(RSI.__name__, input, input_key, length), input, input_key, desc)
self.length = int(length)
self.__prev_gain = None
self.__prev_loss = None
super(RSI, self).update_all()
def __init__(self, input, input_key=None, length=0, desc="TALib Exponential Moving Average"):
super(EMA, self).__init__(Indicator.get_name(EMA.__name__, input, input_key, length), input, input_key, desc)
self.length = int(length)
super(EMA, self).update_all()
def __init__(self, input=None, input_key=None, length=1, desc="Rate Of Change"):
self.length = int(length)
super(ROC, self).__init__(Indicator.get_name(ROC.__name__, input, input_key, length), input, input_key, desc)
def get_or_create_indicator(self, cls, *args, **kwargs):
name = Indicator.get_name(cls, *args, **kwargs)
if not self.app_context.inst_data_mgr.has_series(name):
return globals()[cls](*args, **kwargs)
return self.app_context.inst_data_mgr.get_series(name, create_if_missing=False)
def __init__(self, input=None, input_key=None, length=0, desc="Maximum"):
self.length = int(length)
super(MAX, self).__init__(Indicator.get_name(MAX.__class__, input, input_key, length), input, input_key,
desc)
def __init__(self, input, input_key='close', length=30, desc="Rolling Standard Deviation"):
super(StdDev, self).__init__(input,
func=lambda x: np.std(x, axis=0),
name=Indicator.get_name(StdDev.__name__, input, input_key, length),
length=length,
input_key=input_key, desc=desc)
def __init__(self, input, input_key=None, length=0, desc="Standard Deviation"):
super(STD, self).__init__(Indicator.get_name(STD.__class__, input, input_key, length), input, input_key,
desc)
self.length = int(length)
super(STD, self).update_all()
def __init__(self, input=None, input_key=None, length=0, ann_factor=252, desc="Historical Volatility"):
self.length = int(length)
self.ann_factor = ann_factor
super(HistoricalVolatility, self).__init__(
Indicator.get_name(HistoricalVolatility.__name__, input, input_key, length), input, input_key, desc)
def get_name(input, length):
return "KalmanFilteringPairRegression(%s,%s)" % (Indicator.get_input_name(input), length)
def __init__(self, input, input_key='close', length=30, desc="Rolling Skew"):
super(Skew, self).__init__(input,
func=lambda x: pd_skew_wrapper(x),
name=Indicator.get_name(Skew.__name__, input, input_key, length),
length=length,
input_key=input_key, desc=desc)