def test(stock_codes):
j = 0
for code, row in stock_codes.iterrows():
name = row["code_name"]
if check_name(name) and DbMgr.get_stock_count(code) > 300 and StockMgr.check_condition(code):
print(j, name, code)
j += 1
def keras_predict(stock_codes, col):
j = 0
for code, row in stock_codes.iterrows():
name = row["code_name"]
if check_name(name) and DbMgr.get_stock_count(code) > 300 and StockMgr.check_condition(code):
print(j, name, code)
StockMgr.keras_train(code, col)
j += 1
def all_train(stock_codes):
j = 0
for code, row in stock_codes.iterrows():
name = row["code_name"]
if check_name(name) and DbMgr.get_stock_count(code) > 300:
print(j, name, code)
result = StockMgr.keras_train(code, "stock_close")
if result:
with open("keras_stock.txt", "a") as file:
file.write("{}|".format(code))
j += 1
def tf_predict(stock_codes):
j = 0
for code, row in stock_codes.iterrows():
name = row["code_name"]
if check_name(name) and DbMgr.get_stock_count(code) > 300 and StockMgr.check_condition(code):
print(j, name, code)
result = StockMgr.tf_train(code, ["stock_open", "stock_high", "stock_low", "stock_volume", "stock_agency",
"stock_foreigner", "stock_close"])
if result:
with open("tf_stock.txt", "a") as file:
file.write("{}|".format(code))
j += 1
if __name__ == "__main__":
while 1:
menu = print_menu()
if menu == 1:
i = 0
CodeMgr.collect_stock_codes()
stock_codes = DbMgr.get_all_stock_code()
for code, row in stock_codes.iterrows():
print(i, row["code_name"], code)
i += 1
if DbMgr.exist_stock_info_db(code)[0][0] == 1:
if DbMgr.get_stock_count(code) < 10:
print("생성")
StockMgr.collect_total_stock_info(code)
else:
StockMgr.collect_stock_info(code)
else:
print("생성")
StockMgr.collect_total_stock_info(code)
elif menu == 2:
keras_predict(DbMgr.get_stock_code("P"), "stock_close")
elif menu == 3:
keras_predict(DbMgr.get_stock_code("Q"), "stock_close")
elif menu == 4:
tf_predict(DbMgr.get_stock_code("P"))
elif menu == 5:
tf_predict(DbMgr.get_stock_code("Q"))