def execute(self, sliding_window):
points_to_process = list(
filter(lambda item: 2 * item + 1 <= len(sliding_window),
self.interval_points))
logging.info("interval points to process: {points_to_process}".format(
points_to_process=points_to_process))
if len(points_to_process) == 0:
return None
points = point_extractor.extract_points(sliding_window,
points_to_process)
logging.info("extracted points for each interval: {points}".format(
points=points))
for point in points:
threshold = self.threshold[point] if isinstance(
self.threshold, collections.Mapping) else self.threshold
if analyzer.is_local_max(points[point], threshold):
logging.info(
"point {point} of domain {domain} is local max".format(
point=points[point], domain=point))
return 'SELL'
elif analyzer.is_local_min(points[point], threshold):
logging.info(
"point {point} of domain {domain} is local min".format(
point=points[point], domain=point))
return 'BUY'
return None
def test_local_max_stable_no_thres(self):
point = {'left': 1.32, 'mid': 1.32, 'right': 1.32}
self.assertTrue(analyzer.is_local_max(point, 0) == False, "point {point} is no local max".format(point = point))
def test_local_max_thres(self):
point = {'left': 1.32, 'mid': 1.3333, 'right': 1.30}
threshold = 0.01
self.assertTrue(analyzer.is_local_max(point, threshold) == True, "point {point} is local max with threshold {threshold}".format(point = point, threshold = threshold))
def test_local_max_no_thres(self):
point = {'left': 1.32, 'mid': 1.33, 'right': 1.30}
self.assertTrue(analyzer.is_local_max(point, 0) == True, "point {point} is local max".format(point = point))