def test_recommendation_set_price_date_during_analysisperiod(self):
with patch.object(PriceDispersionStrategy,
'_load_financial_data',
return_value=self.financial_data):
price_date = date(2020, 6, 10)
strategy = PriceDispersionStrategy(
TickerList.from_dict({
"list_name": "DOW30",
"list_type": "US_EQUITIES",
"comparison_symbol": "DIA",
"ticker_symbols": ['AAPL', 'V']
}), '2020-06', price_date, 3)
strategy.generate_recommendation()
recommendation_set = strategy.recommendation_set
self.assertEqual(recommendation_set.model['valid_from'],
str(date(2020, 7, 1)))
self.assertEqual(recommendation_set.model['valid_to'],
str(date(2020, 7, 31)))
self.assertEqual(recommendation_set.model['price_date'],
str(date(2020, 6, 10)))
def backtest(analysis_period: str):
log.info("Performing backtest for %s" % analysis_period)
period = pd.Period(analysis_period)
data_end_date = intrinio_util.get_month_period_range(period)[1]
strategy = PriceDispersionStrategy(
ticker_list, period, None, output_size)
strategy.generate_recommendation()
date_1m = get_nearest_business_date(data_end_date + timedelta(days=30))
date_2m = get_nearest_business_date(data_end_date + timedelta(days=60))
date_3m = get_nearest_business_date(data_end_date + timedelta(days=90))
portfolio_1m = calculator.mark_to_market(
strategy.recommendation_dataframe, 'ticker', 'analysis_price', date_1m)['actual_return'].mean() * 100
portfolio_2m = calculator.mark_to_market(
strategy.recommendation_dataframe, 'ticker', 'analysis_price', date_2m)['actual_return'].mean() * 100
portfolio_3m = calculator.mark_to_market(
strategy.recommendation_dataframe, 'ticker', 'analysis_price', date_3m)['actual_return'].mean() * 100
all_stocks_1m = calculator.mark_to_market(strategy.raw_dataframe, 'ticker', 'analysis_price', date_1m)[
'actual_return'].mean() * 100
all_stocks_2m = calculator.mark_to_market(strategy.raw_dataframe, 'ticker', 'analysis_price', date_2m)[
'actual_return'].mean() * 100
all_stocks_3m = calculator.mark_to_market(strategy.raw_dataframe, 'ticker', 'analysis_price', date_3m)[
'actual_return'].mean() * 100
backtest_report['investment_period'].append(
data_end_date.strftime('%Y/%m'))
backtest_report['ticker_sample_size'].append(
len(strategy.raw_dataframe))
backtest_report['avg_ret_1M'].append(all_stocks_1m)
backtest_report['sel_ret_1M'].append(portfolio_1m)
backtest_report['avg_ret_2M'].append(all_stocks_2m)
backtest_report['sel_ret_2M'].append(portfolio_2m)
backtest_report['avg_ret_3M'].append(all_stocks_3m)
backtest_report['sel_ret_3M'].append(portfolio_3m)
def backtest(year: int, month: int):
log.info("Peforming backtest for %d/%d" % (month, year))
data_end_date = intrinio_util.get_month_date_range(year, month)[1]
strategy = PriceDispersionStrategy(ticker_list, year, month,
output_size)
strategy.generate_recommendation()
date_1m = data_end_date + timedelta(days=30)
date_2m = data_end_date + timedelta(days=60)
date_3m = data_end_date + timedelta(days=90)
portfolio_1m = calculator.mark_to_market(
strategy.recommendation_dataframe,
date_1m)['actual_return'].mean() * 100
portfolio_2m = calculator.mark_to_market(
strategy.recommendation_dataframe,
date_2m)['actual_return'].mean() * 100
portfolio_3m = calculator.mark_to_market(
strategy.recommendation_dataframe,
date_3m)['actual_return'].mean() * 100
all_stocks_1m = calculator.mark_to_market(
strategy.raw_dataframe, date_1m)['actual_return'].mean() * 100
all_stocks_2m = calculator.mark_to_market(
strategy.raw_dataframe, date_2m)['actual_return'].mean() * 100
all_stocks_3m = calculator.mark_to_market(
strategy.raw_dataframe, date_3m)['actual_return'].mean() * 100
backtest_report['investment_period'].append(
data_end_date.strftime('%Y/%m'))
backtest_report['ticker_sample_size'].append(
len(strategy.raw_dataframe))
backtest_report['avg_ret_1M'].append(all_stocks_1m)
backtest_report['sel_ret_1M'].append(portfolio_1m)
backtest_report['avg_ret_2M'].append(all_stocks_2m)
backtest_report['sel_ret_2M'].append(portfolio_2m)
backtest_report['avg_ret_3M'].append(all_stocks_3m)
backtest_report['sel_ret_3M'].append(portfolio_3m)
def main():
"""
Main function for this script
"""
try:
(environment, ticker_file_name, output_size, month, year,
current_price_date, app_ns) = parse_params()
log.info("Parameters:")
log.info("Environment: %s" % environment)
log.info("Ticker File: %s" % ticker_file_name)
log.info("Output Size: %d" % output_size)
log.info("Analysis Month: %d" % month)
log.info("Analysis Year: %d" % year)
if environment == "TEST":
log.info("reading ticker file from local filesystem")
ticker_list = TickerFile.from_local_file(
constants.TICKER_DATA_DIR, ticker_file_name).ticker_list
log.info("Performing Recommendation Algorithm")
strategy = PriceDispersionStrategy(ticker_list, year, month,
output_size)
strategy.generate_recommendation()
display_calculation_dataframe(month, year, strategy,
current_price_date)
else: # environment == "PRODUCTION"
# test all connectivity upfront, so if there any issues
# the problem becomes more apparent
connector_test.test_aws_connectivity()
connector_test.test_intrinio_connectivity()
log.info("Reading ticker file from s3 bucket")
ticker_list = TickerFile.from_s3_bucket(ticker_file_name,
app_ns).ticker_list
log.info("Loading existing recommendation set from S3")
recommendation_set = None
try:
recommendation_set = SecurityRecommendationSet.from_s3(app_ns)
except AWSError as awe:
if not awe.resource_not_found():
raise awe
log.info("No recommendation set was found in S3.")
if recommendation_set == None \
or not recommendation_set.is_current(datetime.now()):
log.info("Performing Recommendation Algorithm")
strategy = PriceDispersionStrategy(ticker_list, year, month,
output_size)
strategy.generate_recommendation()
recommendation_set = strategy.recommendation_set
display_calculation_dataframe(month, year, strategy,
current_price_date)
recommendation_set.save_to_s3(app_ns)
recommendation_svc.notify_new_recommendation(
recommendation_set, app_ns)
else:
log.info(
"Recommendation set is still valid. There is nothing to do"
)
except Exception as e:
stack_trace = traceback.format_exc()
log.error("Could run script, because: %s" % (str(e)))
log.error(stack_trace)
if environment == "PRODUCTION":
aws_service_wrapper.notify_error(
e, "Securities Recommendation Service", stack_trace, app_ns)
