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、现量大于1万手并且当日高开的股票中选取。
选股逻辑分析
该选股策略主要从交易活跃度、市场参与度、流通性等角度综合考虑，选取具有一定上涨潜力的股票，并注重短期涨势。买一量大于卖一量是选取市场需求强劲的标准，现量大于1万手是筛选流通性好的标准，当天高开则是反映当天交易情况的重要指标。
有何风险？
该选股策略依然过于依赖技术指标的解读，可能存在误判的情况。此外，选股时也过于关注短期涨势而缺乏基本面等长期因素的考虑，可能导致筛选出具有较高波动性的股票，风险较高。
如何优化？
可以将技术指标与基本面因素相结合，增强选股的可靠性。同时，可以加入财务指标等长期因素的考虑，如盈利能力、成长能力等。此外，可以针对短期涨幅明显的股票，采取定期调仓或止盈策略，减小投资风险。
最终的选股逻辑
在换手率在3%~12%之间、买一量大于卖一量、现量大于1万手并且当日高开的股票中选取。
同花顺指标公式代码参考
SELECT SYMBOL FROM (
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 MARKET = '主板' AND LIST_STATUS = '上市')
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 SYMBOL FROM BJDC WHERE NAME = '高开' AND TO_DATE(TIME, 'yyyy-mm-dd') = TO_DATE('${date}', 'yyyy-mm-dd'))
AND SYMBOL IN (SELECT SYMBOL FROM GDH WHERE NAME = '现量' AND CAST(DATA AS NUMBER) > 10000)
) A
LEFT JOIN (
SELECT SYMBOL FROM (
SELECT SYMBOL, SUM(HOLD_RATIO) RATIO FROM STOCK_HOLDERS WHERE TRADE_DATE = '2022-04-22'
GROUP BY SYMBOL HAVING SUM(HOLD_RATIO) > 0.2
)
) B ON A.SYMBOL = B.SYMBOL
WHERE B.SYMBOL IS NULL;
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、现量大于1万手的股票
market_df = pro.market_detail(symbol='', trade_date=date)
df1 = market_df[(market_df['bid_vol'] > market_df['ask_vol']) & (market_df['volume'] > 10000)]
df1 = df1[df1['ts_code'].str.startswith('0')]
# 按换手率筛选股票
daily_basic_df = pro.daily_basic(ts_code='', trade_date=date, 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')]
# 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'])]
# 查询日线数据，并选取前一天的数据
ts_codes = df1['ts_code'].tolist()
daily_df = pro.daily(ts_code=','.join(ts_codes), start_date=date, end_date=date, fields='ts_code,trade_date,open,close,pre_close')
df1 = pd.merge(df1, daily_df, on='ts_code', how='inner')
# 检查是否当日高开
df1 = df1[df1['open'] > df1['pre_close']]
# 合并所有指标，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。
