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、开盘前15分钟内涨幅小于6%。
选股逻辑分析
该选股策略主要考虑了股票的流动性、市值以及短期市场的热度。换手率与买卖盘挂单量的考虑，提高了选股的可靠性。同时，开盘前15分钟的涨幅对应当前市场的热度，过高的升幅表明该股票存在过热的风险，因此在涨幅小于6%的情况下选取股票，有更大概率能够在较短时间内获得收益。
有何风险？
该策略强调短期热点因素，对个股的长期走势和内在价值因素考虑不足。同时，在开盘前选择股票，可能会导致由于收盘价等因素与选股时期存在一定偏差，影响收益。
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，可以逐渐延长选股的时间窗口，在市场状况合适的情况下进行持仓。
最终的选股逻辑
在换手率在3%~12%之间、买一量大于卖一量、开盘前15分钟内涨幅小于6%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 STOCK_CODE FROM LCAP WHERE CAST(DATA AS NUMBER) > 100)
AND SYMBOL IN
(SELECT STOCK_CODE FROM TICK
WHERE TRADE_TIME = '0925' AND (CAST(CURRENT_PRICE AS NUMBER) - CAST(PRE_CLOSE_PRICE AS NUMBER))/CAST(PRE_CLOSE_PRICE AS NUMBER) < 0.06);
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,last_price,pre_close')
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'])]
# 按照开盘前15分钟的涨幅筛选股票
tick_df = pro.query('tick', trade_date=date, start_time='09:25:00', end_time='09:40:00', fields='ts_code,last_price,pre_close')
tick_df['pct_chg'] = (tick_df['last_price'] - tick_df['pre_close']) / tick_df['pre_close']
df1 = pd.merge(df1, tick_df, on='ts_code', how='inner')
df1 = df1[(df1['pct_chg'] < 0.06)]
# 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。
