def trains(self, corps:pd.DataFrame=None)->(pd.DataFrame, pd.DataFrame):
if corps is None:
corp = Corp(self._global_params)
corps = corp.get_eval_corps_auto()
no = 1
invest_date = self._global_params.invest_start_date + "~" + self._global_params.invest_end_date
results = []
info_data = []
for index, corp_data in corps.iterrows():
corp_code = corp_data['종목코드']
corp_name = corp_data['회사명']
invest_value, index_value, infos = self.train(corp_code=corp_code, corp_name=corp_name)
result = [no, corp_code, corp_name, invest_value, index_value, invest_date]
results.append(result)
info_data.append(infos)
print(result)
# if no % 10 == 0:
# df_results = pd.DataFrame(results, columns=self.RESULT_COLUMNS)
# DataUtils.save_excel(df_results, self._get_result_file_path())
no += 1
df_results = pd.DataFrame(results, columns=self.RESULT_COLUMNS)
DataUtils.save_excel(df_results, self._get_result_file_path())
chart_data = None
try:
visualizer = InvestVisualizer(self._global_params)
chart_data = visualizer.draw_invest_4reinforcement(info_data, corps)
except Exception:
exc_type, exc_value, exc_traceback = sys.exc_info()
traceback.print_exception(exc_type, exc_value, exc_traceback, file=sys.stdout)
return df_results, chart_data
def let_train_invests(self, corps, start_no=1):
"""입력한 회사들에 대해서 학습시키고 모의투자를 실행한다."""
if self.params.is_all_corps_model == True and self.params.remove_session_file == True:
learning = Learning(self.params)
learning.delete_learning_image()
comp_rmses = []
no = 1
for index, corp_data in corps.iterrows():
if no < start_no:
no += 1
continue
corp_code = corp_data['종목코드']
corp_name = corp_data['회사명']
try:
result = self.let_train_invest(corp_code, corp_name, no)
except Exception as inst:
print(inst)
no += 1
continue
comp_rmses.append(result)
if no % 10 == 0:
df_comp_rmses = pd.DataFrame(comp_rmses,
columns=self.result_columns)
DataUtils.save_excel(df_comp_rmses,
self.get_result_file_path())
no += 1
def train_months(self, start:str='2018.01', end:str='2018.11', invest_money:float=100000000)->None:
train_model = self._global_params.train_model
start_month = DateUtils.to_date(start, '%Y.%m')
end_month = DateUtils.to_date(end, '%Y.%m')
between = DateUtils.between_months(start_month, end_month)
invest_months_result = []
result_columns = ["month", "invest_money", "result_money"]
MOCK_MONEY = 10000000
chart_data = []
for i in range(between + 1):
# params.remove_session_file = True
before_month_start = DateUtils.to_month_str(start_month, i - self._global_params.mock_period_months)
before_month_end = DateUtils.to_month_str(start_month, i - 1)
self._global_params.invest_start_date = before_month_start + '.01'
self._global_params.invest_end_date = before_month_end + '.31'
self._global_params.result_file_name = "MOCK_" + before_month_start + "-" + before_month_end
self._global_params.invest_money = MOCK_MONEY
corp = Corp(self._global_params)
corps = corp.get_eval_corps_auto(self._global_params.invest_end_date)
self._env.set_params(params=self._global_params)
before_result, _ = self.trains(corps)
now_month = DateUtils.to_month_str(start_month, i)
before_result = corp.exclude_corps(before_result, now_month)
before_result = before_result.sort_values(by='invest_result', ascending=False)
before_result.index = range(len(before_result.index))
corp10_codes = before_result.loc[:9, 'code']
corp10_codes.index = range(len(corp10_codes.index))
corp10 = corp.get_corps_for_codes(corp10_codes)
corp10_len = len(corp10.index)
self._global_params.invest_start_date = now_month + '.01'
self._global_params.invest_end_date = now_month + '.31'
self._global_params.result_file_name = "INVEST_" + now_month
self._global_params.invest_money = invest_money / corp10_len
self._env.set_params(params=self._global_params)
now_result, invest_chart_data = self.trains(corp10)
chart_data.append(invest_chart_data)
invest_money = now_result['invest_result'].sum()
result = [now_month, self._global_params.invest_money * corp10_len, invest_money]
invest_months_result.append(result)
print(result)
df_imr = pd.DataFrame(invest_months_result, columns=result_columns)
save_file_name = "recommend_months_" + start + "-" + end + ".xlsx"
if "_" in train_model:
save_file_path = os.path.join('result', 'reinforcement', train_model, self._global_params.ensemble_type, save_file_name)
else:
save_file_path = os.path.join('result', 'reinforcement', train_model, save_file_name)
DataUtils.save_excel(df_imr, save_file_path)
if len(chart_data) > 1:
visualizer = InvestVisualizer(self._global_params)
visualizer.draw_invest_months(chart_data, start, end)
print()
def let_train_invests_for_name(self, corp_names):
"""회사이름으로 검색하여 학습시킴 """
corp = Corp()
comp_rmses = []
no = 1
for corp_name in corp_names:
corp_code = corp.get_corp_code(corp_name)
result = self.let_train_invest(corp_code, corp_name, no)
comp_rmses.append(result)
no += 1
df_comp_rmses = pd.DataFrame(comp_rmses, columns=self.result_columns)
DataUtils.save_excel(df_comp_rmses, self.get_result_file_path())
def forcasts(self, corps_n_date:list):
results = []
no = 1
for corp_n_date in corps_n_date:
corp_code = corp_n_date[0].replace("A", "")
corp_name = corp_n_date[1]
forcast_date = corp_n_date[2]
result = self.forcast(corp_name=corp_name, corp_code=corp_code, forcast_date=forcast_date)
result.insert(0, no)
print(result)
results.append(result)
no += 1
df_comp_rmses = pd.DataFrame(results, columns=self.FORCAST_COLUMNS)
DataUtils.save_excel(df_comp_rmses, self._get_result_file_path())
def trains(self, corps):
""" 선형회귀 모형 학습 """
comp_rmses = []
no = 1
for index, corp_data in corps.iterrows():
corp_code = corp_data['종목코드']
corp_name = corp_data['회사명']
try :
result = self.train(corp_code, corp_name, no)
except Exception as inst:
print(inst)
no += 1
continue
comp_rmses.append(result)
no += 1
df_comp_rmses = pd.DataFrame(comp_rmses, columns=self.RESULT_COLUMNS)
DataUtils.save_excel(df_comp_rmses, self.get_result_file_path())
def train_n_invests_top10(self, corps):
""" 상위 10개를 사는 방법으로 모의투자를 실행한다. """
learning_invest = LearningNMockInvestment(self.params)
invest_count = self.params.invest_count
invest_data = []
for i in range(invest_count):
j = 0
for index, corp_data in corps.iterrows():
if i == 0:
invest_row = self.train_top10(i, j, corp_data)
invest_data.append(invest_row)
else:
invest_row = invest_data[j]
self.train_top10(i, j, corp_data, invest_row)
#print(i, j, invest_row)
j += 1
self.invest_top10(i, invest_data)
print(invest_data)
self.sell_all_stock(invest_data)
print("final", invest_data)
df_invest_data = pd.DataFrame(invest_data, columns=self.RESULT_COLUMNS)
DataUtils.save_excel(df_invest_data,
learning_invest.get_result_file_path())
def k_means(self):
corps = self.get_corps()[[
"회사명", "시가총액\n(억)", "자사주\n(만주)", "자사주\n비중\n(%)", "18년\nOPM\n(%)",
"5년평균\nPBR", "5년평균\nPER", "부채\n(억)", "자본\n(억)", "총자산\n(억)",
"영업이익\n(억)", "6개월\n외인(%)", "6개월\n기관(%)"
]]
corps = corps.dropna()
corps.set_index("회사명", inplace=True)
kmeans = KMeans(n_clusters=10)
# Fitting the input data
kmeans.fit(corps)
clust_labels = kmeans.predict(corps)
#print(corps.index[clust_labels])
# Centroid values
#centroids = kmeans.cluster_centers_
corps.insert(0, 'kmeans', clust_labels)
result = corps[['kmeans']]
print(result)
result.insert(0, '회사명', corps.index)
file_path = os.path.join(self.DIR_RESULT, 'K-Means.xlsx')
DataUtils.save_excel(result, file_path)
def let_train_invests_twins(self, corps, start_no=1):
"""겨별 세션과 통합세션에서 예측한 값의 평균을 예측값으로 한다."""
comp_rmses = []
no = 1
for index, corp_data in corps.iterrows():
if no < start_no:
no += 1
continue
corp_code = corp_data['종목코드']
corp_name = corp_data['회사명']
try:
result = self.let_train_invest_twins(corp_code, corp_name, no)
except Exception as inst:
print(inst)
no += 1
continue
comp_rmses.append(result)
if no % 10 == 0:
df_comp_rmses = pd.DataFrame(comp_rmses,
columns=self.result_columns)
DataUtils.save_excel(df_comp_rmses,
self.get_result_file_path())
no += 1
def _save_corps(self):
""" 주식회사 정보를 가져와서 엑셀로 저장한다. """
url = 'http://kind.krx.co.kr/corpgeneral/corpList.do?method=download&searchType=13'
code_df = pd.read_html(url, header=0)[0]
DataUtils.save_excel(code_df, self._FILE_PATH)
def save_corps(self):
""" 주식회사 정보를 가져와서 엑셀로 저장한다. """
code_df = pd.read_html(self.URL_CORPS_KRX, header=0)[0]
DataUtils.save_excel(code_df, self.CORPS_FILE_PATH)
