问财量化选股策略逻辑
选股逻辑为：选择换手率在3%~12%之间、买一量大于卖一量、企业性质为民营企业或中字头股票的股票。
选股逻辑分析
该选股策略除了考虑市场交易活跃度和市场参与度，还新增了企业性质这一维度，通过选股筛选出活跃度和参与度较高的、自身实力较强的企业。
有何风险？
企业性质虽然能够反映出企业的实力和业绩，但并非所有民营企业和中字头股票都是优秀的股票，筛选时需要结合具体企业的基本面和财务情况进行评估，过于依赖企业性质选择股票可能会存在一定的盲目性。
如何优化？
可以引入其他影响股票表现的因素，如行业龙头、板块题材、股价波动性、资产负债率等指标，综合考量企业的运营和发展情况，从而更加全面地评估选股策略。
最终的选股逻辑
在换手率在3%~12%之间、买一量大于卖一量、企业性质为民营企业或中字头股票的股票中，按换手率从大到小排序，选出前50个股票。
同花顺指标公式代码参考
SELECT STOCKCODE FROM (
SELECT STOCKCODE FROM BLOCK_STOCK WHERE BLOCKID = 'HS300'
AND STOCKCODE IN (SELECT STOCK_CODE FROM STOCK_BASIC WHERE MARKET='主板' AND LIST_STATUS='上市' AND IS_ST=0 AND IS_OPEN='1')
AND STOCKCODE IN
(SELECT STOCK_CODE FROM SDB WHERE NAME = '买一' AND (CAST(DATA AS NUMBER) > CAST(FDATA AS NUMBER)))
AND STOCKCODE IN (SELECT STOCK_CODE FROM GDH WHERE NAME = '换手率' AND (CAST(DATA AS NUMBER) > 3) AND (CAST(DATA AS NUMBER) < 12))
AND STOCKCODE IN
(SELECT STOCK_CODE FROM STOCK_BASIC WHERE (INDUSTRY='中字头' OR ND='1'))
)
ORDER BY (SELECT AVG(DATA) AS AVG_RQSB FROM (SELECT DATA FROM SDB WHERE STOCK_CODE=A.STOCKCODE AND NAME='资金流向' AND NAME_DESC='日前收买' ORDER BY DATE. DESC) WHERE ROWNUM<=5) DESC
WHERE ROWNUM <= 50;
python代码参考
import pandas as pd
import tushare as ts
def select_stocks():
pro = ts.pro_api()
# 查询挂单大量大于卖单、企业性质为民营企业或中字头股票的股票
market_df = pro.market_detail(symbol='', trade_date='20220422')
df1 = market_df[(market_df['bid_vol'] > market_df['ask_vol'])]
df1 = df1[df1['ts_code'].str.startswith(('0', '3', '6'))]
stock_basic_df = pro.stock_basic(exchange='', fields='ts_code,industry')
df1 = pd.merge(df1, stock_basic_df, on='ts_code', how='inner')
df1 = df1[(df1['industry'] == '中字头') | (df1['list_status'] == '民营企业')]
# 按换手率筛选股票
daily_basic_df = pro.daily_basic(ts_code='', trade_date='20220421', fields='ts_code,turnover_rate')
df1 = pd.merge(df1, daily_basic_df, on='ts_code', how='inner')
df1 = df1[(df1['turnover_rate'] > 3) & (df1['turnover_rate'] < 12)]
# 按市场筛选股票
df1 = df1[df1['ts_code'].str.startswith(('0', '3', '6'))]
# 按买卖盘挂单量筛选股票
sdb_df = pro.stk_holdernumber(ts_code='', start_date='20220420', end_date='20220420', fields='ts_code,mkv')
sdb_df.rename(columns={'ts_code': 'symbol'}, inplace=True)
df1 = pd.merge(df1, sdb_df, on='symbol', how='inner')
df1 = df1[(df1['buy_sm_vol'] > df1['sell_sm_vol']) & (df1['buy_sm_vol'] > df1['mkv'])]
# 按换手率排序
df1 = df1.sort_values('turnover_rate', ascending=False)
# 合并所有指标，按换手率从大到小排序，返回选股结果
return df1[:50]['ts_code']
## 如何进行量化策略实盘？
请把您优化好的选股语句放入文章最下面模板的选股语句中即可。
select_sentence = '市值小于100亿' #选股语句。
模板如何使用？
点击图标右上方的复制按钮，复制到自己的账户即可使用模板进行回测。
## 如果有任何问题请添加 下方的二维码进群提问。
