def analyze_moving_averages(self, coin_pair, period_count=20, time_unit='5m'):
ma_analyzer = MovingAverages()
historical_data = self.exchange_aggregator.get_historical_data(
coin_pair=coin_pair,
period_count=period_count,
time_unit=time_unit
)
sma_value = ma_analyzer.calculate_sma(period_count, historical_data)
ema_value = ma_analyzer.calculate_ema(period_count, historical_data)
return sma_value, ema_value
def analyze_moving_averages(self, market_pair, period_count=20, time_unit='5m'):
ma_analyzer = MovingAverages()
historical_data = self.exchange_interface.get_historical_data(
market_pair=market_pair,
period_count=period_count,
time_unit=time_unit
)
sma_value = ma_analyzer.calculate_sma(period_count, historical_data)
ema_value = ma_analyzer.calculate_ema(period_count, historical_data)
return sma_value, ema_value
class BollingerBands(object):
"""
Bollinger Bands
"""
def __init__(self):
self.logger = structlog.get_logger()
self.utils = Utils()
self.sma = MovingAverages()
'''
Retrieves the upper and lower bollinger bands for the given parameters:
:param historical_data: A list of historical data points for a coin pair
:param period: The period of the moving average to be used in bollinger calculation (defaults to 21)
:param k: The number of standard deviations away from the 'period'-length moving average
to place the upper and lower band (defaults to 2)
:returns A 2-element tuple containing the upper and lower band, respectively.
'''
def get_bollinger_bands(self, historical_data, period=21, k=2):
sma = self.sma.calculate_sma(period, historical_data)
closing_prices = self.utils.get_closing_prices(historical_data)
std_dev = np.std(closing_prices[-period:])
return sma + k * std_dev, sma - k * std_dev
def analyze_ema(self, market_pair, exchange, hot_thresh=0, cold_thresh=0):
ma_analyzer = MovingAverages()
period_count = 15
historical_data = self.day_historical_data[0:period_count]
ema_value = ma_analyzer.get_ema_value(period_count, historical_data)
is_ema_trending = ma_analyzer.is_ema_trending(ema_value, hot_thresh)
ema_data = {
'values': (ema_value,),
'is_hot': is_ema_trending,
'is_cold': False
}
return ema_data
def analyze_sma(self, historial_data, hot_thresh=0, cold_thresh=0):
ma_analyzer = MovingAverages()
period_count = 15
sma_historical_data = historial_data[0:period_count]
sma_value = ma_analyzer.get_sma_value(period_count,
sma_historical_data)
is_sma_trending = ma_analyzer.is_sma_trending(sma_value, hot_thresh)
sma_data = {
'values': (sma_value, ),
'is_hot': is_sma_trending,
'is_cold': False
}
return sma_data
def analyze_moving_averages(self, market_pair, exchange):
ma_analyzer = MovingAverages()
period_count = self.ma_config["period_count"]
historical_data = self.__exchange_interface.get_historical_data(
market_pair=market_pair,
exchange=exchange,
period_count=period_count,
time_unit=self.ma_config["time_unit"])
sma_value = ma_analyzer.get_sma_value(period_count, historical_data)
ema_value = ma_analyzer.get_ema_value(period_count, historical_data)
is_sma_trending = ma_analyzer.is_sma_trending(
sma_value, self.ma_config["sma_threshold"])
is_ema_trending = ma_analyzer.is_ema_trending(
ema_value, self.ma_config["ema_threshold"])
ma_data = {
'sma_value': sma_value,
'ema_value': ema_value,
'is_sma_trending': is_sma_trending,
'is_ema_trending': is_ema_trending
}
return ma_data
def __init__(self):
self.logger = structlog.get_logger()
self.utils = Utils()
self.sma = MovingAverages()