def test_mail_notify_sender(self):
df_predict = k_data_predict_log_dao.get_predict_log_list(
get_current_date())
html = mail_content_render('mail_predict_daily_report_template.html',
{'df_predict': df_predict})
mail_notify_sender(default_config.MAIL_TO, 'Predict Daily Report',
html)
def get_multiple_k_data(self, code_list=None, start=None, end=None):
if start is None:
start = get_next_date(-720)
if end is None:
end = get_current_date()
sql = None
if code_list is None:
sql = ('''select *
from k_data
where time_key BETWEEN %(start)s and %(end)s order by time_key asc ''')
codes_list = None
else:
sql = ('''select *
from k_data
where code in %(code_list)s and time_key BETWEEN %(start)s and %(end)s order by time_key asc ''')
codes_list = [fill_market(code) for code in code_list]
data = pd.read_sql(sql=sql, params={"code_list": codes_list, "start": start, "end": end}
, con=dataSource.mysql_quant_conn)
return data
def test_mail_content_render(self):
df_predict = k_data_predict_log_dao.get_predict_log_list(
get_current_date())
html = mail_content_render('mail_predict_daily_report_template.html',
{'df_predict': df_predict})
print(html)
def test_get_last_macd_cross_point(self):
df = k_data_dao.get_k_data(code="601668",
start=get_next_date(-120),
end=get_current_date())
df = df.join(cal_macd(df))
item = k_data_dao.get_last_macd_cross_point(df, window_size=8)
print(item)
def test_get_data(self):
state, data = self.futu_quote_ctx.get_history_kline(
fill_market('600196'),
ktype='K_WEEK',
autype='qfq',
start='2018-08-01',
end=get_current_date())
print(data)
def insert(self, code, name, best_estimator, train_score, test_score, desc=None):
sql = text('replace into k_data_model_log (date, code, name, best_estimator, train_score, test_score, `desc`) '
'VALUES(:date,:code,:name,:best_estimator,:train_score,:test_score,:desc)')
result = dataSource.mysql_quant_conn.execute(sql, date=datetime_utils.get_current_date(),
code=code, name=name, best_estimator=best_estimator
, train_score=train_score, test_score=test_score, desc=desc)
return result
def exists(self, code):
# 绑定引擎
# 连接数据表
table = Table('k_data_model_log', dataSource.mysql_quant_metadata, autoload=True)
s = select([table.c.code, table.c.date]) \
.where(and_(table.c.date == datetime_utils.get_current_date(), table.c.code == code))
result = dataSource.mysql_quant_conn.execute(s)
logger.debug("row count:%s" % result.rowcount)
return result.rowcount > 0
def test_training(self):
code = '600276'
data, features = k_data_dao.get_k_data_with_features(
code, '2015-01-01', datetime_utils.get_current_date())
pac = PCAModel('k_data')
pac.training_model(code=code, data=data, features=features)
model = SequantialNeuralClassifier()
model.training_model(code, data, features)
def cal_stock_pool():
df_poll = stock_pool_dao.get_list()
code_list = list(df_poll['code'].values)
w_data_list = k_data_weekly_dao.get_multiple_k_data(code_list,
start='2013-01-01',
end=get_current_date())
k_data_list = k_data_dao.get_multiple_k_data(code_list=code_list,
start=get_next_date(-720),
end=get_current_date())
# k_data_list = k_data_list.set_index('code', inplace=True)
# k_data_list = k_data_dao.get_market_snapshot(code_list=code_list, futu_quote_ctx=futu_quote_ctx)
matched = []
for code in code_list:
rs = cal_single_stock(code, k_data_list, w_data_list)
if rs is True:
matched.append(code)
print("matched:%s" % matched)
def collect_single_daily(code, futu_quote_ctx, start=None, end=None):
table_name = 'k_data'
if start is None:
start = get_current_date()
if end is None:
end = get_current_date()
try:
state, data = futu_quote_ctx.get_history_kline(fill_market(code),
ktype='K_DAY',
autype='qfq',
start=start,
end=end)
data = data.tail(1)
data.to_sql(table_name,
dataSource.mysql_quant_engine,
if_exists='append',
index=False)
except Exception as e:
logger.error(e)
def get_k_data(self, code, start, end):
if start is None:
start = '2013-01-01'
if end is None:
end = get_current_date()
sql = ('''select *
from k_data_weekly
where code=%(code)s and time_key BETWEEN %(start)s and %(end)s order by time_key asc ''')
data = pd.read_sql(sql=sql, params={"code": fill_market(code), "start": start, "end": end}
, con=dataSource.mysql_quant_conn)
return data
def get_k_data(self, code, start=None, end=None):
if start is None:
start = get_next_date(-720)
if end is None:
end = get_current_date()
sql = ('''select time_key, code, open, close as close, high, low, change_rate, last_close, turnover, turnover_rate, volume, pe_ratio
from k_data
where code=%(code)s and time_key BETWEEN %(start)s and %(end)s order by time_key asc ''')
data = pd.read_sql(sql=sql, params={"code": fill_market(code), "start": start, "end": end}
, con=dataSource.mysql_quant_conn)
return data
def init_pool(self):
self.truncate()
stocks = stock_dao.query_all()
k_data_list = k_data_dao.get_multiple_k_data(start=get_next_date(-720), end=get_current_date())
df = pd.DataFrame(columns=['code', 'name'])
for stock in stocks:
try:
k_data = k_data_list.loc[k_data_list['code'] == fill_market(stock.code)]
k_data = k_data.join(cal_macd(k_data))
k_data['turnover7'] = cal_mavol7(k_data, column='turnover')
k_turnover7 = k_data['turnover7'].values[-1]
if len(k_data['code'].values) == 0:
continue
stock_basic = stock_basic_dao.get_by_code(stock.code)
eps_value = stock_basic['eps'].values[0]
profits_yoy_value = stock_basic['profits_yoy'].values[0]
if eps_value < 0:
continue
if profits_yoy_value < 0:
continue
if k_turnover7 < 65000000:
continue
dict = {"code": stock.code, "name": stock.name}
df = df.append(dict, ignore_index=True)
logger.debug("append code:%s" % stock.code)
except Exception as e:
logger.debug("code:%s, error:%s" % (stock.code, traceback.format_exc()))
df.to_sql('stock_pool', dataSource.mysql_quant_engine, if_exists='append', index=False)
'''
def cal_bk_vol():
bk_vol_frame = pd.DataFrame(columns=['bkcode', 'bk_name', 'total_mavol_3'])
filter_list = [
'BK0743', 'BK0804', 'BK0568', 'BK0707', 'BK0701', 'BK0611', 'BK0705',
'BK0612', 'BK0500'
]
org_bk_code_list = list(stock_industry_dao.get_bkcode_list().values)
bk_code_list = [c for c in org_bk_code_list if c not in filter_list]
for bk in bk_code_list:
bk_stocks = stock_industry_dao.get_by_bkcode(bk[0])
bk_vol3 = 0
try:
bk_name = bk_stocks['bk_name'][0]
except:
bk_name = 'N/A'
for code in bk_stocks['code'].values:
stock_df = k_data_dao.get_k_data(code=code,
start=get_next_date(-30),
end=get_current_date())
if len(stock_df) == 0:
continue
stock_df['mavol3'] = cal_mavol3(stock_df)
try:
bk_vol3 += stock_df['mavol3'].values[-1:][0]
except Exception as e:
logger.debug("code:%s, error:%s" % (code, repr(e)))
bk_vol_frame.loc[bk_vol_frame.shape[0] + 1] = {
'bkcode': bk,
'bk_name': bk_name,
'total_mavol_3': bk_vol3
}
bk_vol_frame = bk_vol_frame.sort_values('total_mavol_3', ascending=False)
bk_vol_frame.to_csv('bk_csv.csv', encoding='utf_8_sig')
def insert(self, code, logistic_regression, random_forest_classifier,
support_vector_classifier, xgb_classifier, sequantial_neural):
sql = text(
'replace into k_data_predict_log (date, code, '
'logistic_regression, '
'random_forest_classifier, '
'support_vector_classifier, '
'xgb_classifier, '
'sequantial_neural) '
'values(:date,:code,:logistic_regression,:random_forest_classifier,:support_vector_classifier,:xgb_classifier,:sequantial_neural)'
)
result = dataSource.mysql_quant_conn.execute(
sql,
date=datetime_utils.get_current_date(),
code=code,
logistic_regression=logistic_regression,
random_forest_classifier=random_forest_classifier,
support_vector_classifier=support_vector_classifier,
xgb_classifier=xgb_classifier,
sequantial_neural=sequantial_neural)
return result
def test_collect_single(self):
collect_single(code='601899',
futu_quote_ctx=self.futu_quote_ctx,
start='2013-01-01',
end=get_current_date())
# 死叉
# if pre_k > pre_d and ((k_value <= d_value) or (abs(k_value - d_value) <= 10)):
#
# shares = self.context.portfolio.positions[code].shares
# # 清仓
# if shares > 0:
# self.sell_value(code, shares)
if __name__ == '__main__':
context = Context(start='2018-07-01', end='2018-07-14', base_capital=50000)
kdj = KDJStrategy()
kdj.init(context)
context.current_date = get_current_date()
kdj.handle_data()
logger.debug("base_capital:%s" % context.base_capital)
logger.debug("blance:%s" % context.blance)
# context.current_date = convert_to_datetime('2018-07-04')
# kdj.handle_data()
# logger.debug(context.order_book[1])
logger.debug("blance:%s" % context.blance)
logger.debug("base_capital:%s" % context.base_capital)
kdj.futu_quote_ctx.close()
df_stock_industry = stock_industry_dao.get_by_code(code[3:])
name = df_stock_industry['name'].values[0]
if name.find('ST') > -1:
continue
bk_code = df_stock_industry['bk_code'].values[0]
bk_name = df_stock_industry['bk_name'].values[0]
data = data.append(
{
'bk_code': bk_code,
'bk_name': bk_name,
'code': code[3:],
'name': name,
'date': get_current_date(),
'label': label,
'k': k,
'd': d
},
ignore_index=True)
except Exception as e:
logger.debug("code:%s, error:%s" % (code, repr(e)))
data.to_sql('k_data_strategy_kdj_log',
dataSource.mysql_quant_engine,
if_exists='append',
index=False)
'''
label = cal_single_stock('600006')
