问财量化选股策略逻辑
选股逻辑为：在换手率在3%~12%之间、买一量大于卖一量、昨天换手率大于8%选取股票。
选股逻辑分析
该选股策略主要考虑了股票的流动性、市值以及前一交易日的市场交易热度。换手率与买卖盘挂单量的考虑，提高了选股的可靠性。同时，昨天换手率大于8%的条件，对应短期市场的活跃程度，通过选取高交易活跃性的股票，有更大概率能够在较短时间内获得收益。
有何风险？
该策略过分追求短线交易机会，对于经营业绩较好的但短期交易不活跃的股票，可能会选择错过。同时，昨天换手率作为一天前的数据可能存在时间滞后的问题。
如何优化？
可以加入PE、PB等基本面指标考虑股票的长期确定性。同时，可以考虑选取长期稳定的标的，降低无谓的换手率导致的交易费用。可以引入其他短期交易活跃度指标进行筛选，提高选股的可靠性。
最终的选股逻辑
在换手率在3%~12%之间、买一量大于卖一量、昨天换手率大于8%选取股票。
同花顺指标公式代码参考
SELECT SYMBOL FROM GDH WHERE NAME = '换手率'
AND (CAST(DATA AS NUMBER) > 3) AND (CAST(DATA AS NUMBER) < 12)
AND SYMBOL IN (SELECT STOCK_CODE FROM STOCK_BASIC WHERE LIST_STATUS = '上市' AND MARKET_TYPE = '1' AND LIQUIDITY > 1)
AND SYMBOL IN (SELECT STOCK_CODE FROM SDB WHERE NAME = '买一' AND CAST(DATA AS NUMBER) > CAST(FDATA AS NUMBER))
AND SYMBOL IN
(SELECT DISTINCT SYMBOL FROM GDH WHERE NAME = '换手率'
AND (CAST(DATA AS NUMBER) > 8)
AND TRADE_DATE = (SELECT MAX(TRADE_DATE) FROM GDH WHERE TRADE_DATE < '${TRADE_DATE}' AND NAME = '换手率'));
python代码参考
import pandas as pd
import tushare as ts
def select_stocks(date):
pro = ts.pro_api()
# 查询挂单大量大于卖单的股票
market_df = pro.market_detail(symbol='', trade_date=date, fields='ts_code,bid_vol,ask_vol')
df1 = market_df[(market_df['bid_vol'] > market_df['ask_vol'])]
# 按流通市值和市场筛选股票
lc_df = pro.daily_basic(ts_code='', trade_date=date, fields='ts_code,circ_mv')
df1 = pd.merge(df1, lc_df, on='ts_code', how='inner')
df1 = df1[(df1['circ_mv'] > 10000000000)]
basic_df = pro.stock_basic(exchange='', list_status='L', fields='ts_code,market')
df1 = pd.merge(df1, basic_df, on='ts_code', how='inner')
df1 = df1[df1['market'] == '主板']
# 按买卖盘挂单量筛选股票
sdb_df = pro.stk_holdernumber(ts_code='', start_date=date, end_date=date, 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'])]
# 按昨日换手率筛选股票
gdh_df = pro.daily_basic(ts_code='', trade_date=date, fields='ts_code,trade_date,turnover_rate')
gdh_df2 = gdh_df[gdh_df['trade_date'] == (pd.to_datetime(date) - pd.Timedelta(days=1)).strftime('%Y%m%d')]
df1 = pd.merge(df1, gdh_df2, on='ts_code', how='inner')
df1 = df1[(df1['turnover_rate'] > 8)]
# 返回选股结果
return df1['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。
