def main():
'''
Main testing script
'''
try:
ticker_list = TickerList.from_local_file("%s/djia30.json" %
(constants.APP_DATA_DIR))
config = Configuration.try_from_s3(constants.STRATEGY_CONFIG_FILE_NAME,
'sa')
#macd_strategy = MACDCrossoverStrategy.from_configuration(config, 'sa')
macd_strategy = MACDCrossoverStrategy(ticker_list, date(2020, 6, 16),
0.0016, 12, 16, 9)
macd_strategy.generate_recommendation()
macd_strategy.display_results()
'''
#pd_strategy = PriceDispersionStrategy.from_configuration(config, 'sa')
pd_strategy = PriceDispersionStrategy(
ticker_list, '2020-06', date(2020, 5, 16), 3)
pd_strategy.generate_recommendation()
pd_strategy.display_results()
'''
except Exception as e:
log.error("Could run script, because, %s" % (str(e)))
def test_recommendation_set_dates(self):
price_date = date(2020, 6, 8)
with patch.object(intrinio_data, 'get_daily_stock_close_prices',
return_value=self.price_dict), \
patch.object(intrinio_data, 'get_sma_indicator',
return_value=self.sma_dict), \
patch.object(intrinio_data, 'get_macd_indicator',
return_value=self.macd_dict):
strategy = MACDCrossoverStrategy(self.ticker_list, price_date,
0.0016, 12, 26, 9)
strategy.generate_recommendation()
recommendation_set = strategy.recommendation_set
self.assertEqual(recommendation_set.model['valid_from'],
str(date(2020, 6, 8)))
self.assertEqual(recommendation_set.model['valid_to'],
str(date(2020, 6, 8)))
self.assertEqual(recommendation_set.model['price_date'],
str(date(2020, 6, 8)))
def main():
"""
Main Function for this script
"""
description = """
Executes a backtest for the MACD_CROSSOVER strategy given a ticker list,
start date, end date and threshold, e.g. -0.02, used to determine the maximum
allowed loss of a trade before a stop loss takes effect.
"""
parser = argparse.ArgumentParser(description=description)
parser.add_argument("-ticker_list",
help="Ticker List File",
type=str,
required=True)
date_parser = lambda s: datetime.strptime(s, '%Y/%m/%d')
parser.add_argument("-start_date",
help="Backtest start date (YYY/MM/DD)",
type=date_parser,
required=True)
parser.add_argument("-end_date",
help="Backtest end date (YYYY/MM/DD)",
type=date_parser,
required=True)
parser.add_argument("-stop_loss_theshold",
help="Stop Loss Threshold factor, e.g. -0.02 (-2%%)",
type=float,
required=True)
args = parser.parse_args()
ticker_file_name = args.ticker_list
start_date = args.start_date
end_date = args.end_date
stop_loss_theshold = args.stop_loss_theshold
log.info("Parameters:")
log.info("Ticker File: %s" % ticker_file_name)
log.info("Start Date: %s" % start_date)
log.info("End Date: %s" % end_date)
log.info("Stop Loss Threshold: %.2f" % stop_loss_theshold)
log.info("")
log.info("MACD Configuration:")
log.info("Divergence Tolerance Factor: %f" % DIVERGENCE_FACTOR_THRESHOLD)
log.info("Slow Period: %d" % SLOW_PERIOD)
log.info("Fast Period: %d" % FAST_PERIOD)
log.info("Signal Period: %d" % SIGNAL_PERIOD)
log.info("")
ticker_list = TickerList.from_local_file(
"%s/%s" % (constants.TICKER_DATA_DIR, ticker_file_name))
trade_dict = {
'ticker': [],
'buy_date': [],
'buy_price': [],
'sell_date': [],
'sell_price': [],
'trade_pnl_factor': [],
'false_signal': []
}
try:
date_list = get_business_date_list(start_date, end_date)
init_portfolio_dict(ticker_list)
for i in range(0, len(date_list) - 1):
recommendation_date = date_list[i]
trade_date = date_list[i + 1]
strategy = MACDCrossoverStrategy(ticker_list, recommendation_date,
DIVERGENCE_FACTOR_THRESHOLD,
FAST_PERIOD, SLOW_PERIOD,
SIGNAL_PERIOD)
strategy.generate_recommendation()
print("processing: %s" % recommendation_date, end="\r")
unwound_positions = trade(trade_date, strategy.recommendation_set)
for position in unwound_positions:
(ticker, buy_date, sell_date, buy_price) = position
sell_price = get_close_price(ticker, sell_date)
pnl = ((sell_price / buy_price) - 1)
false_signal = 0
if (pnl < stop_loss_theshold):
sell_price = 'STOP_LOSS'
false_signal = 1
pnl = stop_loss_theshold
if (pnl < 0):
false_signal = 1
trade_dict['ticker'].append(ticker)
trade_dict['buy_date'].append(buy_date)
trade_dict['buy_price'].append(buy_price)
trade_dict['sell_date'].append(str(sell_date))
trade_dict['sell_price'].append(sell_price)
trade_dict['trade_pnl_factor'].append(pnl)
trade_dict['false_signal'].append(false_signal)
trades_by_ticker = []
for ticker in ticker_list.model['ticker_symbols']:
trades_by_ticker.append(calculate_returns(ticker, trade_dict))
trade_dataframe = pd.concat(trades_by_ticker)
display_results(trade_dataframe)
except Exception as e:
log.error("Could run script, because, %s" % (str(e)))
raise e