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, market_pair):
ic_analyzer = IchimokuCloud()
base_line_data = self.exchange_interface.get_historical_data(
market_pair=market_pair, period_count=26, time_unit='1d')
conversion_line_data = self.exchange_interface.get_historical_data(
market_pair=market_pair, period_count=9, time_unit='1d')
span_b_data = self.exchange_interface.get_historical_data(
market_pair=market_pair, period_count=52, time_unit='1d')
leading_span_a = ic_analyzer.calculate_leading_span_a(
base_line_data, conversion_line_data)
leading_span_b = ic_analyzer.calculate_leading_span_b(span_b_data)
return leading_span_a, leading_span_b
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
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,
market_pair,
exchange,
ichimoku_threshold=0):
ic_analyzer = IchimokuCloud()
span_b_periods = self.ichimoku_config['span_b']['period_count']
base_line_periods = self.ichimoku_config['base_line']['period_count']
conversion_line_periods = self.ichimoku_config['conversion_line'][
'period_count']
max_period_count = max(span_b_periods, base_line_periods,
conversion_line_periods)
history = self.__exchange_interface.get_historical_data(
market_pair=market_pair,
exchange=exchange,
period_count=max_period_count,
time_unit=self.ichimoku_config['time_unit'])
base_line_data = history[0:base_line_periods]
conversion_line_data = history[0:conversion_line_periods]
span_b_data = history[0:span_b_periods]
leading_span_a = ic_analyzer.get_leading_span_a(
base_line_data, conversion_line_data)
leading_span_b = ic_analyzer.get_leading_span_b(span_b_data)
is_ichimoku_trending = ic_analyzer.is_ichimoku_trending(
self.ichimoku_config["ichimoku_threshold"])
ichimoku_data = {
'span_a_value': leading_span_a,
'span_b_value': leading_span_b,
'is_ichimoku_trending': is_ichimoku_trending
}
return ichimoku_data