def main(argv):
"""Основная функция выполнения обновления"""
print(argv)
log.init()
log.info('Начало проверки обновлений.')
# Инициализация настроек обновления из файла settings.json
settings_dict = worker.init_settings()
# Создание коннектора для работы с сервисом проверки обновлений 1С
connector = updateapi.ApiConnector(settings_dict["itsUsername"],
settings_dict["itsPassword"],
settings_dict["proxySettings"])
# Поиск и скачивание новых версий конфигураций 1С
worker.update_configurations(connector, settings_dict)
# Поиск и скачивание новой версии платформы 1С
worker.update_platform(connector, settings_dict)
# Поиск и скачивание новых версий конфигураций 1С
worker.update_configurations(connector, settings_dict)
log.info('Завершение проверки обновлений.')
log.close()
dest='global_shift',
default=True)
parser.add_argument('--invert',
help="invert color range",
dest='invert',
action='store_true',
default=False)
args = parser.parse_args()
# local mode
if args.rom == None:
print("Need --rom parameter")
sys.exit(-1)
log.init(args.debug)
logger = utils.log.get('Palette')
if args.seed == 0:
random.seed(random.randint(0, 9999999))
else:
random.seed(args.seed)
settings = {
# global same shift for everything flag
"global_shift": True,
#set to True if all suits should get a separate hue-shift degree
"individual_suit_shift": False,
#set to True if all tileset palettes should get a separate hue-shift degree
def main(run_directory: str,
dev_tsv_file: str = None,
tsv_file: str = None,
reload=False,
use_cuda=True):
init(run_directory + "log/out.txt")
from tensorboardX import SummaryWriter
writer = SummaryWriter(log_dir=os.path.join(os.getcwd(), "tensorboard"))
if reload:
searcher = CrossEntropyHyperparamSearch.read_runs(run_directory +
"searchdata.pkl.gz",
writer=writer)
else:
search_params = {
"batch_size": 8,
"n_samples": 21,
"elite_samples": 6,
"run_directory": run_directory,
"dev": dev_tsv_file,
"train": tsv_file,
"use_cuda": use_cuda
}
search_params["const_params"] = {
"use_copy": True,
"bidirectional": True
}
search_params["dim_params"] = [{
"name":
"num_unks",
"transform":
HyperparamTransform(min_val=2,
max_val=6.5,
make_int=True,
log_space=True,
log_base=2)
}, {
"name":
"lr",
"transform":
HyperparamTransform(min_val=-3.15, max_val=-2.3, log_space=True)
}, {
"name":
"hidden_size",
"transform":
HyperparamTransform(min_val=150, max_val=650, make_int=True)
}, {
"name":
"n_layers",
"transform":
HyperparamTransform(min_val=1, max_val=3, make_int=True)
}, {
"name":
"dropout",
"transform":
HyperparamTransform(min_val=0.1, max_val=0.8)
}, {
"name":
"bidirectional",
"transform":
HyperparamTransform(make_bool=True)
}, {
"name":
"attention_size",
"transform":
HyperparamTransform(min_val=100, max_val=650, make_int=True)
}, {
"name":
"copy_attn_size",
"transform":
HyperparamTransform(min_val=100, max_val=650, make_int=True)
}, {
"name":
"copy_extra_layer",
"transform":
HyperparamTransform(make_bool=True)
}, {
"name":
"attn_extra_layer",
"transform":
HyperparamTransform(make_bool=True)
}, {
"name":
"copy_extra_layer_size",
"transform":
HyperparamTransform(min_val=50, max_val=650, make_int=True)
}, {
"name":
"attn_extra_layer_size",
"transform":
HyperparamTransform(min_val=50, max_val=650, make_int=True)
}]
searcher = CrossEntropyHyperparamSearch(run_params=search_params,
writer=writer)
searcher.search_params(n_sets=50)
def main(train: bool,
model_path: str,
dev_tsv_file: str = None,
tsv_file: str = None,
vocab_list: str = None,
vocab_size=30000,
batch_size=64,
epochs=1,
use_cuda=True,
lr=0.002,
hidden_size=1024,
n_layers=1,
bidirectional=True,
dropout=0.5,
num_unks=10,
use_copy=True,
attention_size=512,
copy_attn_size=512,
copy_extra_layer=True,
attn_extra_layer=True,
copy_extra_layer_size=512,
attn_extra_layer_size=512,
continue_training=False,
do_sentiment=False,
use_coverage=False,
coverage_weight=1.0):
init("log/out.txt")
from tensorboardX import SummaryWriter
writer = SummaryWriter(log_dir=os.path.join(os.getcwd(), "tensorboard"))
dataset = None
devset = None
if train:
model_param = {
'lr': lr,
'hidden_size': hidden_size,
'n_layers': n_layers,
'bidirectional': bidirectional,
'dropout': dropout,
'num_unks': num_unks,
'use_copy': use_copy,
'attention_size': attention_size,
'copy_attn_size': copy_attn_size,
'copy_extra_layer': copy_extra_layer,
'attn_extra_layer': attn_extra_layer,
'copy_extra_layer_size': copy_extra_layer_size,
'attn_extra_layer_size': attn_extra_layer_size,
'use_coverage': use_coverage,
"do_sentiment": do_sentiment,
"coverage_weight": coverage_weight
}
misc_tokens = ["<SOS>", "<EOS>"]
model_param['misc_tokens'] = misc_tokens
model_param['vocab'] = IDKRephraseModel.get_vocab_from_list_and_files(
vocab_list, vocab_size, [tsv_file, dev_tsv_file], misc_tokens)
model = IDKRephraseModel(model_param, writer=writer)
dataset = IDKRephraseDataset.from_TSV(tsv_file, model.glove, "<SOS>",
"<EOS>", model.vocab,
do_sentiment)
else:
model = IDKRephraseModel.from_file(model_path, writer=writer)
model.set_cuda(use_cuda)
if dev_tsv_file is not None:
devset = IDKRephraseDataset.from_TSV(dev_tsv_file, model.glove,
"<SOS>", "<EOS>", model.vocab,
do_sentiment)
if train or continue_training:
if dataset is None:
dataset = IDKRephraseDataset.from_TSV(tsv_file, model.glove,
"<SOS>", "<EOS>",
model.vocab)
model.train_dataset(dataset, devset, batch_size, 0, epochs, model_path)
while True:
question = input(": ")
if question == "quit":
break
question = "<SOS> " + question + " <EOS>"
sentiment = input("Positive (y/n):")
sentiment_flag = 0
if sentiment.lower() == "y":
sentiment_flag = 1
condensed = input("Condensed (y/n):")
condensed_flag = 0
if condensed.lower() == "y":
condensed_flag = 1
info(" ".join(
model.predict(torch.LongTensor(
model.glove.words_to_indices(question.split())),
question.split(),
make_attn_graphics=False,
sentiment_tensor=torch.Tensor(
[sentiment_flag, condensed_flag]))))
if dev_tsv_file is not None:
#model.get_worst_bleu(devset)
model.evaluate_bleu(devset, print_predictions=True)
# noinspection SpellCheckingInspection
__author__ = 'wookjae.jo'
import os
from datetime import date
from datetime import datetime
from database.backtest.report import XlsxExporter
from database.backtest.strategies.bollinger import BackTest as BlgBackTest
from utils import log
log.init()
def run(earning_line_max, stop_line, comment):
begin = date(2018, 1, 1)
end = date(2021, 7, 27)
backtest = BlgBackTest(begin=begin,
end=end,
initial_deposit=1_0000_0000,
once_buy_amount=200_0000,
earning_line_min=5,
earning_line=10,
earning_line_max=earning_line_max,
stop_line=stop_line,
trailing_stop_rate=3,
blacklist=['007700', '086520'])
backtest.comment = comment
backtest.start()
import datetime
import logging
import os
import sys
import time
from utils import log
log.init(logging.DEBUG)
from creon import stocks, metrics
basedir = os.path.abspath(os.path.dirname(__file__))
sys.path.append(os.path.join(basedir, 'stocktock'))
from trading import Simulator_2
from utils.slack import Message
def main():
available_codes = stocks.get_availables()
# 정배열만 필터링
available_codes = [
code for code in available_codes
if metrics.get_calculator(code).is_straight()
]
print(f'정배열 개수: {len(available_codes)}')
# 시총 제한
# details: Dict[str, stocks.StockDetail2] = {detail.code: detail for detail in stocks.get_details(available_codes)}
import os import sys basedir = os.path.abspath(os.path.dirname(__file__)) sys.path.append(os.path.join(basedir, 'stocktock')) import logging from utils import log log.init(logging.INFO) from dateutil.parser import parse as parse_datetime import flask_cors import jsons from flask import Flask, request, send_file from werkzeug.serving import WSGIRequestHandler from creon import stocks from creon import charts from creon.exceptions import CreonError from creon.connection import connector as creon_connector from datetime import date, timedelta from dataclasses import dataclass from simstock import BreakAbove5MaEventSimulator # set protocol HTTP/1.1 WSGIRequestHandler.protocol_version = "HTTP/1.1" app = Flask(__name__) flask_cors.CORS(app)
help="Target script name",
action="store",
default=None)
parser.add_argument("-c",
"--target-count",
help="Target instance count",
action="store",
type=int,
default=1)
parser.add_argument("--shell",
help="Run command as shell mode",
action="store_true",
default=False)
parser.add_argument("--socket",
help="Unix domain socket path",
action="store",
default=None)
args = parser.parse_args()
log.init(args.name)
log.i(__name__, "Start QB Incubator")
if args.script is None and args.target is None:
log.e(__name__, "--script and --target are not set.")
exit(1)
if args.script is not None:
script_main(args.shell, args.script, args.socket)
else:
target_main(args.shell, args.target, args.target_count, args.socket)