def generate_recommendation(self):
"""
Applies the price dispersion algorithm and sets the following
instance variables:
self.recommendation_set
A SecurityRecommendationSet object with the current
recommendation
self.raw_dataframe
Dataframe with all stocks sorted into deciles. Useful for
displaying intermediate results.
self.recommendation_dataframe
A Dataframe containing just the recommended stocks
Returns
------------
None
"""
financial_data = self.__load_financial_data__()
self.raw_dataframe = pd.DataFrame(financial_data)
pd.options.display.float_format = '{:.3f}'.format
# sort the dataframe into deciles
self.raw_dataframe['decile'] = pd.qcut(
financial_data['dispersion_stdev_pct'],
10,
labels=False,
duplicates='drop')
self.raw_dataframe = self.raw_dataframe.sort_values(
['decile', 'analyst_expected_return'], ascending=(False, False))
self.recommendation_dataframe = self.raw_dataframe.head(
self.output_size).drop([
'decile', 'target_price_avg', 'dispersion_stdev_pct',
'analyst_expected_return'
],
axis=1)
# price the recommended securitues
priced_securities = {}
for row in self.recommendation_dataframe.itertuples(index=False):
priced_securities[row.ticker] = row.analysis_price
# determine the recommendation valid date range
valid = self.analysis_end_date + timedelta(days=1)
(valid_from, valid_to) = intrinio_util.get_month_date_range(
valid.year, valid.month)
self.recommendation_set = SecurityRecommendationSet.from_parameters(
datetime.now(), valid_from, valid_to, self.analysis_end_date,
self.STRATEGY_NAME, "US Equities", priced_securities)
def test_is_current_current_date_single(self):
# Create a recommendation set from the past (2019/8)
recommendation_set = SecurityRecommendationSet.from_parameters(
datetime(2020, 3, 1, 4, 56, 57, tzinfo=timezone.utc),
date(2019, 8, 1), date(2019, 8, 1), date(2019, 8, 1),
"PRICE_DISPERSION", "US Equities", {
"GE": 123.45,
"INTC": 123.45,
"AAPL": 123.45
})
self.assertTrue(recommendation_set.is_current(date(2019, 8, 1)))
def test_notify_new_recommendation_with_boto_error(self):
with patch.object(aws_service_wrapper, 'cf_read_export_value',
return_value="some_sns_arn"), \
patch.object(aws_service_wrapper, 'sns_publish_notification',
side_effect=AWSError("test exception", None)):
notification_list = []
notification_list.append(SecurityRecommendationSet.from_parameters(datetime.now(), datetime.now(
), datetime.now(), datetime.now(), 'STRATEGY_NAME', 'US Equities', {'AAPL': 100}))
with self.assertRaises(AWSError):
recommendation_svc.notify_new_recommendation(
notification_list, 'sa')
def generate_recommendation(self):
'''
Analyzes all securitues supplied in the ticker list and returns a SecurityRecommendationSet
object containing all stocks with a positive MACD crossover. These are stocks
that are bullish and have positive momentum behind them.
internally sets the self.recommendation_set object
'''
analysis_data = {
'ticker_symbol': [],
'price': [],
'macd': [],
'signal': [],
'divergence': [],
'momentum': []
}
recommended_securities = {}
for ticker_symbol in self.ticker_list.ticker_symbols:
(current_price, macd_line, signal_line) = self._read_price_metrics(
ticker_symbol)
buy_sell_indicator = self._analyze_security(
current_price, macd_line, signal_line)
analysis_data['ticker_symbol'].append(ticker_symbol)
analysis_data['price'].append(current_price)
analysis_data['macd'].append(macd_line)
analysis_data['signal'].append(signal_line)
analysis_data['divergence'].append(macd_line - signal_line)
if buy_sell_indicator == True:
analysis_data['momentum'].append("BULLISH")
recommended_securities[ticker_symbol] = current_price
else:
analysis_data['momentum'].append("BEARISH")
self.raw_dataframe = pd.DataFrame(analysis_data)
self.raw_dataframe = self.raw_dataframe.sort_values(
['momentum', 'divergence'], ascending=(False, False))
valid_from = valid_to = self.analysis_date
self.recommendation_set = SecurityRecommendationSet.from_parameters(
datetime.now(), valid_from, valid_to, self.analysis_date, self.STRATEGY_NAME,
"US_EQUITIES", recommended_securities
)
def test_valid_parameters(self):
SecurityRecommendationSet.from_parameters(
datetime.now(), datetime.now(), datetime.now(), datetime.now(),
'STRATEGY_NAME', 'US Equities', {'AAPL': 100})
def test_invalid_parameters(self):
'''
Combine these into a single test for brevity
'''
with self.assertRaises(ValidationError):
SecurityRecommendationSet.from_parameters(None, date.today(),
date.today(),
datetime.now(),
'STRATEGY_NAME',
'US Equities',
{'AAPL': 100})
with self.assertRaises(ValidationError):
SecurityRecommendationSet.from_parameters(datetime.now(), None,
date.today(),
date.today(),
'STRATEGY_NAME',
'US Equities',
{'AAPL': 100})
with self.assertRaises(ValidationError):
SecurityRecommendationSet.from_parameters(datetime.now(),
date.today(), None,
datetime.now(),
'STRATEGY_NAME',
'US Equities',
{'AAPL': 100})
with self.assertRaises(ValidationError):
SecurityRecommendationSet.from_parameters(datetime.now(),
date.today(),
date.today(), None,
'STRATEGY_NAME',
'US Equities',
{'AAPL': 100})
with self.assertRaises(ValidationError):
SecurityRecommendationSet.from_parameters(datetime.now(),
date.today(),
date.today(),
date.today(), None,
'US Equities',
{'AAPL': 100})
with self.assertRaises(ValidationError):
SecurityRecommendationSet.from_parameters(datetime.now(),
date.today(),
date.today(),
date.today(),
'STRATEGY_NAME', None,
{'AAPL': 100})
with self.assertRaises(ValidationError):
SecurityRecommendationSet.from_parameters(datetime.now(),
date.today(),
date.today(),
date.today(),
'STRATEGY_NAME',
'US Equities', None)
with self.assertRaises(ValidationError):
SecurityRecommendationSet.from_parameters(
datetime.now(), date.today(), date.today(), date.today(),
'STRATEGY_NAME', 'US Equities', "Not A Dictionary")