def __init__(self):
aws_host = Utils.read_properties().get('DATABASE','aws_host')
aws_database = Utils.read_properties().get('DATABASE','aws_database')
aws_user = Utils.read_properties().get('DATABASE','aws_user')
aws_password = Utils.read_properties().get('DATABASE', 'aws_password')
self.aws_db = mysql.connector.connect(host=aws_host, user=aws_user, passwd=aws_password, database=aws_database)
self.insert_data_statement = "INSERT INTO {} (symbol, company_name, trade_date, " \
"RSI_rsi, MACD_macd_signal, MACD_macd_histogram, MACD_macd," \
"BB_real_upper_band, BB_real_middle_band, BB_real_lower_band, OBV_obv, open_price, low_price, high_price, close_price, volume) " \
"VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)"
def main(self):
print("Portfolio Manager started")
parser = argparse.ArgumentParser()
parser.add_argument('-database',
action='store',
dest='database',
help='Either of Sqlite/AWS')
parser.add_argument('-mode',
action='store',
dest='mode',
help='Test Mode[test/real]')
parser.add_argument('-image',
action='store',
dest='image',
help='Generate Images[generate]')
parser.add_argument('-model',
action='store',
dest='model',
help='create machine learning model[create]')
parser.add_argument('-training_period',
action='store',
dest='training_period',
help='Training period in years')
self.args = parser.parse_args()
scraper = Scraper()
config = Utils.read_properties()
av = AlphaV.AlphaVantage()
ti = TechIndicators(key=os.environ["ALPHA_VANTAGE_KEY"],
output_format='pandas')
ts = TimeSeries(key=os.environ["ALPHA_VANTAGE_KEY"],
output_format='pandas')
if self.args.mode == 'test':
symbol_list = scraper.read_symbols()
else:
symbol_list = self.get_exchange_symbol_list(
Constants.SP500, scraper)
if self.args.database == 'sqllite':
self.dump_market_data(symbol_list, Constants.SP500, av, ti, ts,
config)
elif self.args.database == 'AWS':
for exchange in Constants.Exchanges:
symbol_list = self.get_exchange_symbol_list(exchange, scraper)
self.dump_market_data(symbol_list, exchange, av, ti, ts,
config)
if self.args.model == 'create':
recommender = Recommender(self.args, symbol_list, True)
else:
recommender = Recommender(self.args, symbol_list, False)
#recommender.generate_recommendation(Constants.LearningModel.IMAGE_BASED_CLASSIFICATION, symbol_list)
#recommender.generate_recommendation(Constants.LearningModel.DECISION_TREE_CLASSIFICATION, symbol_list)
recommender.generate_recommendation(
Constants.LearningModel.LSTM_CLASIFICATION, symbol_list)
