def analyze_ichimoku_cloud(self, market_pair, exchange, hot_thresh=0, cold_thresh=0):
ic_analyzer = IchimokuCloud()
tenkansen_period = 9
kijunsen_period = 26
senkou_span_b_period = 52
chikou_span_period = 26
tankensen_historical_data = self.day_historical_data[0:tenkansen_period]
kijunsen_historical_data = self.day_historical_data[0:kijunsen_period]
senkou_span_b_historical_data = self.day_historical_data[0:senkou_span_b_period]
leading_span_a = ic_analyzer.get_senkou_span_a(
kijunsen_historical_data,
tankensen_historical_data
)
leading_span_b = ic_analyzer.get_senkou_span_b(senkou_span_b_historical_data)
ichimoku_data = {
'values': (leading_span_a, leading_span_b),
'is_hot': False,
'is_cold': False
}
return ichimoku_data
def analyze_ichimoku_cloud(self,
historial_data,
hot_thresh=None,
cold_thresh=None):
"""Performs an ichimoku cloud analysis on the historical data
Args:
historial_data (list): A matrix of historical OHCLV data.
hot_thresh (float, optional): Defaults to None. The threshold at which this might be
good to purchase.
cold_thresh (float, optional): Defaults to None. The threshold at which this might be
good to sell.
Returns:
dict: A dictionary containing a tuple of indicator values and booleans for buy / sell
indication.
"""
ic_analyzer = IchimokuCloud()
tenkansen_period = 9
kijunsen_period = 26
senkou_span_b_period = 52
tenkansen_historical_data = historial_data[-tenkansen_period:]
kijunsen_historical_data = historial_data[-kijunsen_period:]
senkou_span_b_historical_data = historial_data[-senkou_span_b_period:]
tenkan_sen = ic_analyzer.get_tenkansen(tenkansen_historical_data)
kijun_sen = ic_analyzer.get_kijunsen(kijunsen_historical_data)
leading_span_a = ic_analyzer.get_senkou_span_a(
kijunsen_historical_data, tenkansen_historical_data)
leading_span_b = ic_analyzer.get_senkou_span_b(
senkou_span_b_historical_data)
is_hot = False
if leading_span_a > leading_span_b and hot_thresh is not None:
if historial_data[-1][4] > leading_span_a:
is_hot = True
is_cold = False
if leading_span_a < leading_span_b and cold_thresh is not None:
if historial_data[-1][4] < leading_span_b:
is_cold = True
if math.isnan(tenkan_sen):
tenkan_sen = None
if math.isnan(kijun_sen):
kijun_sen = None
ichimoku_data = {
'values': (tenkan_sen, kijun_sen),
'is_hot': is_hot,
'is_cold': is_cold
}
return ichimoku_data
def analyze_ichimoku_cloud(self,
historial_data,
hot_thresh=None,
cold_thresh=None):
"""Performs an ichimoku cloud analysis on the historical data
Args:
historial_data (list): A matrix of historical OHCLV data.
hot_thresh (float, optional): Defaults to None. The threshold at which this might be good
to purchase.
cold_thresh (float, optional): Defaults to None. The threshold at which this might be good
to sell.
Returns:
dict: A dictionary containing a tuple of indicator values and booleans for buy / sell
indication.
"""
ic_analyzer = IchimokuCloud()
tenkansen_period = 9
kijunsen_period = 26
senkou_span_b_period = 52
chikou_span_period = 26
tankensen_historical_data = historial_data[0:tenkansen_period]
kijunsen_historical_data = historial_data[0:kijunsen_period]
senkou_span_b_historical_data = historial_data[0:senkou_span_b_period]
leading_span_a = ic_analyzer.get_senkou_span_a(
kijunsen_historical_data, tankensen_historical_data)
leading_span_b = ic_analyzer.get_senkou_span_b(
senkou_span_b_historical_data)
ichimoku_data = {
'values': (leading_span_a, leading_span_b),
'is_hot': False,
'is_cold': False
}
return ichimoku_data