问财量化选股策略逻辑
选股逻辑：选择换手率在3%到12%之间，涨跌幅乘以超大单净量大于0，同时排除北京A股。
选股逻辑分析
该选股策略在前一个策略基础上增加了排除北京A股的筛选条件，以排除这一地区潜在的投资风险。同时继续考虑换手率和上涨空间等选股因素，以期望在潜在股票中寻找具有投资价值的标的。
有何风险？
排除北京A股的筛选条件可能会漏掉一些具有潜在价值的标的，同时该选股策略中的指标筛选条件可能会对某些行业不适用，也可能存在指标过于简单的问题。
如何优化？
可以加入更多与基本面相关的指标，如市盈率、市净率、股息率等，以更全面地考虑选股因素。同时，应该对股票的所处行业进行适当的分析，选择更具有竞争力的标的。此外，也可以采用技术分析等方法以提高选股精准度。
最终的选股逻辑
选择换手率在3%到12%之间，涨跌幅乘以超大单净量大于0，同时排除北京A股为选股范围。
同花顺指标公式代码参考
以下是同花顺指标所需公式：
选股公式：
-- 计算涨跌幅乘以超大单净量
SuperVolume: (C*Big)/10000;
-- 计算选股
SELECT STOCK_SYMBOL FROM (
SELECT STOCK_SYMBOL AS code, (C1 / C0) * SuperVolume AS Score FROM
(
SELECT STOCK_SYMBOL AS code, CLOSE AS C0 FROM CandlesMin WHERE Cdl[:1] = LAST AND TIME = [TIME-1] AND Year(DATE) = YEAR(TODAY)
) ST,
(
SELECT STOCK_SYMBOL AS code, CLOSE AS C1 FROM CandlesMin WHERE Cdl[:1] = LAST AND TIME = [TIME-0] AND Year(DATE) = YEAR(TODAY)
) MT,
(
SELECT STOCK_SYMBOL AS code, BUY_VOL_L_VOL AS Big FROM CandlesDay WHERE Cdl[:1] = LAST AND TIME = [TIME-1] AND Year(DATE) = YEAR(TODAY) AND VOL >= 1000000
) BT,
(
SELECT STOCK_SYMBOL AS code FROM StkBasInfo WHERE MarketValue < 10000000000 AND Industry NOT LIKE '%亏损%' AND FINFOURL NOT NULL AND Area != '北京'
) SI
WHERE ST.code = MT.code AND MT.code = BT.code AND BT.code = SI.code AND ST.O / MA10 >= 0.98 AND ST.O / MA10 <= 1.02 AND Score > 0
ORDER BY Score DESC
LIMIT 10
Python代码参考
以下是Python代码实现该选股策略：
import pandas as pd
from typing import List
from datetime import datetime, timedelta
def select_stock(data: pd.DataFrame, n=10) -> List[str]:
selected_stocks = []
for code, df in data.groupby(level=0):
df = df.sort_values('trade_time', ascending=True)
if (df['float_shares'].iloc[-1] / 1000000000 <= 100) and (df['close'].iloc[-1] > 5) and \
(df['volume'].iloc[-1] / df['volume'].iloc[-6:-1].mean() > 3) and \
(df['turnover_rate'].iloc[-1] > 3) and (df['turnover_rate'].iloc[-1] < 12) and \
(df['pct_chg'].iloc[-1] * abs(df['buy_volume'].iloc[-1] - df['sell_volume'].iloc[-1]) / 10000 > 0) and \
((df['close'].iloc[-1] / df['close'].iloc[-11]) > 0 and (df['close'].iloc[-1] / df['close'].iloc[-11]) < 0.35) and \
(df['area'].iloc[-1] != '北京') and \
(df['industry'].iloc[-1].find('亏损') == -1):
s_weight = df['turnover_rate'].mean() * df['volume'].mean() / (df['close'].iloc[-1] * 10000)
selected_stocks.append((code, s_weight))
selected_stocks.sort(key=lambda x: x[1], reverse=True)
selected_stocks = selected_stocks[:n]
return [x[0] for x in selected_stocks]
## 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。
