第一章考虑mysql数据存储方式
提示：这里可以
量化开始前需要考虑好使用的数据存储方式，本人选择以mysql存储，保留了扩展可以
支持其他的扩展方式，
添加本文要记录的大概内容：
抽象存储的父类
抽象存储父类后，未来可以通过替换别的存储方式，实现存储的替换
import pandas as pd
class Repoistory():
def saveData(self,data:pd.DataFrame,repoistoryParam:dict):
pass
def readData(self, repoistoryParam: dict) -> pd.DataFrame:
pass实现mysql存储
import pymysql as mysql
import pandas as pd
from sqlalchemy import create_engine
from repoistory.base.repoistory import Repoistory
import configEnv.config as conf
class MysqlRepoistory(Repoistory):
def getMysqlConn(self):
return mysql.connect(
host=conf.config.host,
port=conf.config.port,
user=conf.config.user,
password=conf.config.password,
database=conf.config.database,
charset=conf.config.charset
)
def createMysqlEngine(self):
return create_engine(conf.config.mysql_engine)
def handle_cursor(self, func,*args,**kwargs):
connect = self.getMysqlConn()
try:
cursor_obj = connect.cursor()
func(connect,cursor_obj,*args,**kwargs)
cursor_obj.close()
finally:
connect.close()
def saveData(self,data:pd.DataFrame,repoistoryParam:dict):
eng=self.createMysqlEngine()
tableName=repoistoryParam.get("table_name")
data.to_sql( name=tableName,con=eng, if_exists= "append",index= False)
def readData(self,repoistoryParam:dict)->pd.DataFrame:
eng=self.createMysqlEngine()
query_sql=repoistoryParam.get("query_sql")
# index_col = repoistoryParam.get("index_col")
return pd.read_sql(sql=query_sql, con=eng)
if __name__ == "__main__":
mysql=MysqlRepoistory()
repoistoryParam={"query_sql":"select * from trade_daily where stock_code="000002""}
db=mysql.readData(repoistoryParam)
print(db)使用mysql存储实例，将来换别的存储方式只需新的存储对象，然后替换掉
代码如下：
if __name__ == "__main__":
mysql=MysqlRepoistory()
repoistoryParam={"query_sql":"select * from trade_daily where stock_code="000002""}
db=mysql.readData(repoistoryParam)
print(db)以上是对于存储的定义，后续会使用mysql开始爬虫开发、策略开发、策略选股
文章为作者独立观点，不代表股票交易接口观点
股民评论