问财量化选股策略逻辑
选股逻辑为：选择换手率在3%-12%、饮料酒进出口、今日均线向上发散的股票。
选股逻辑分析
该策略综合考虑了行业、换手率及技术面因素，选出了换手率适中的饮料酒类股票，且今日均线向上发散，具有一定的技术上涨潜力。
有何风险？
该策略过于侧重于技术面和行业板块，忽略了公司基本面和财务数据等重要因素，存在投资风险。同时，技术指标容易被市场短期消息和情绪影响，难以保证选出的股票会持续上涨。
如何优化？
可以结合公司基本面、财务数据等因素，更全面地挖掘股票的潜力。同时，在技术指标的基础上，增加其他指标，如资金流入、相对强弱指标等，提高选股的准确性。
最终的选股逻辑
选择换手率在3%-12%、饮料酒进出口、今日均线向上发散的股票。
同花顺指标公式代码参考
换手率在3%-12%：SELECT(TURN<N>=AVG(TURN, N) AND TURN<N+1>AVG(TURN, N+1) AND TURN<N>3 AND TURN<N<12)
饮料酒进出口：SELECT(SECTORCODE('K40')=1)
今日均线向上发散：SELECT(C>=MA5 AND C>=MA10 AND C>=MA20 AND C>=MA30 AND C>=MA60 AND C>=MA120)
选股：SELECT(CODE, 换手率3%-12% AND SELECT_SECTORCOUNT('K43')>0 AND 今日均线向上发散 AND 饮料酒进出口)
python代码参考
import pandas as pd
import tushare as ts
def select_stocks():
pro = ts.pro_api()
df1 = pro.stock_basic(exchange='', list_status='L', fields='ts_code,industry,name')
df1 = df1[(df1['industry'].str.contains('饮料') & df1['industry'].str.contains('酒'))]
df2 = pro.daily(ts_code='', trade_date='20211013', fields='ts_code,trade_date,high,low,trade_status')
df = pd.merge(df2[['ts_code', 'trade_date']], df1[['ts_code']], on='ts_code')
df3 = pro.daily_basic(ts_code='', trade_date='20211013', fields='ts_code,turnover_rate')
df = pd.merge(df, df3, on=['ts_code'])
df = df[(df['turnover_rate']>=3) & (df['turnover_rate']<=12)]
df4 = pro.daily(ts_code='', trade_date='20211013', fields='ts_code,trade_date,open,high,low,close')
for i in [5, 10, 20, 30, 60, 120]:
col_name = 'ma' + str(i)
df[col_name] = df4.groupby('ts_code')['close'].apply(lambda x: x.rolling(i).mean())
df = df[(df['close']>=df['ma5']) & (df['close']>=df['ma10']) & (df['close']>=df['ma20']) & (df['close']>=df['ma30']) & (df['close']>=df['ma60']) & (df['close']>=df['ma120'])]
df = df.groupby('ts_code').tail(1)
df = pd.merge(df, df3[['ts_code', 'turnover_rate']], on=['ts_code'])
df1 = pro.daily(ts_code='', trade_date='20211014', fields='ts_code,trade_date,open,high,low,close')
for i in [5, 10, 20, 30, 60, 120]:
col_name = 'ma' + str(i)
df1[col_name] = df1.groupby('ts_code')['close'].apply(lambda x: x.rolling(i).mean())
df1 = df1[(df1['close']>=df1['ma5']) & (df1['close']>=df1['ma10']) & (df1['close']>=df1['ma20']) & (df1['close']>=df1['ma30']) & (df1['close']>=df1['ma60']) & (df1['close']>=df1['ma120'])]
df = pd.merge(df, df1[['ts_code', 'close']], on='ts_code', suffixes=('', '_today'))
df = df[(df['close_today']>df['ma5_today']) & (df['close_today']>df['ma10_today']) & (df['close_today']>df['ma20_today']) & (df['close_today']>df['ma30_today']) & (df['close_today']>df['ma60_today']) & (df['close_today']>df['ma120_today'])]
return df['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。
