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、20日均线大于120日均线的股票。
选股逻辑分析
该选股策略主要从交易活跃度、市场参与度、技术面等角度综合考虑，选取具有一定上涨潜力的股票。
有何风险？
该选股策略也相对简单，没有考虑公司基本面、宏观经济等因素对股票表现的影响，所以选出的股票也具有一定的盲目风险。此外，该选股策略选股的样本量较小，可能存在选股不稳定的风险。
如何优化？
除了选股时考虑交易活跃度和技术面外，额外加入公司基本面、宏观经济、行业环境等因素进行选股筛选，可以提高筛选出有较好表现的股票的概率。
最终的选股逻辑
在换手率在3%~12%之间，买一量大于卖一量，20日均线大于120日均线的股票中，按照涨幅从高到低排序，选取前50只股票。
同花顺指标公式代码参考
SELECT A.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 MA WHERE NAME = 'MA20' AND ID = 'DAY' AND (CAST(DATA AS NUMBER) > CAST(FDATA AS NUMBER)))
AND SYMBOL IN (SELECT SYMBOL FROM MA WHERE NAME = 'MA120' AND ID = 'DAY' AND (CAST(DATA AS NUMBER) > CAST(FDATA AS NUMBER)))
) 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
ORDER BY A.CHG 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')]
# 查询20日均线和120日均线
ma_df = pro.moving_average(ts_code='', start_date='20220411', end_date='20220422', fields='ts_code,ma20,ma120')
ma_df1 = ma_df[ma_df['ma20'] > ma_df['ma120']]
df1 = pd.merge(df1, ma_df1, on='ts_code', how='inner')
# 查询股票持股分布情况
stock_holder_df = pro.stk_holdernumber(ts_code='', start_date='20220422', end_date='20220422', fields='ts_code,sum_ratio')
stock_holder_df = stock_holder_df.groupby(['ts_code']).sum()
stock_holder_df.reset_index(inplace=True)
# 根据股票持股分布情况筛选股票
df1 = pd.merge(df1, stock_holder_df, on='ts_code', how='left')
df1 = df1[(df1['sum_ratio'] < 0.2) | (df1['sum_ratio'].isnull())]
# 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')]
# 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'])]
# 按涨幅排序
df1 = df1.sort_values('pct_chg', ascending=False)
# 合并所有指标，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。
