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，且昨天在龙虎榜上的股票为选股范围。
选股逻辑分析
该选股逻辑考虑了股票的活跃程度、市场情绪和热点、流通性等因素，同时限制了流通盘的大小，关注股票的交易情况和龙虎榜等信息，与前一个选股逻辑相比增加了龙虎榜作为交易情况的评价指标。
有何风险？
可能会漏掉一些潜在有价值但交易状况不太活跃的股票，而且龙虎榜本身也不一定代表股票的投资价值，有可能过分注重热点而忽略了基本面的变化等情况。另外，在选股逻辑中的流通盘阈值也可能过于严格。
如何优化？
可以考虑增加更多的技术指标、基本面数据、市场预测数据等来评估股票的价值潜力和未来发展趋势，加强对筛选结果的控制和监督，对龙虎榜细化评估，降低流通盘阈值来增加选股池中股票的数量和多样性等。
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，且昨天在龙虎榜上的股票为选股范围。
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;
-- 筛选昨天在龙虎榜上的股票
SELECT STOCK_SYMBOL FROM (
SELECT STOCK_SYMBOL FROM MarketActivityLt where listdate<([DATE]) and bs_flag=1
) X
-- 计算选股
SELECT STOCK_SYMBOL FROM (
SELECT ST.code, (C2 / C1) * SuperVolume AS Score FROM
(
SELECT STOCK_SYMBOL AS code, CLOSE AS C1, NetChangeRatio AS Chg FROM CandlesMin WHERE Cdl[:1] = LAST AND TIME = [TIME-1]
) ST,
(
SELECT STOCK_SYMBOL AS code, CLOSE AS C2 FROM CandlesMin WHERE Cdl[:1] = LAST AND TIME = [TIME-0]
) MT,
(
SELECT STOCK_SYMBOL AS code, VOL AS Vol FROM CandlesMin WHERE Cdl[:1] = LAST AND TIME = [TIME-0]
) VT,
(
SELECT STOCK_SYMBOL AS code, BUY_VOL_L_VOL AS Big FROM CandlesDay WHERE Cdl[:1] = LAST AND TIME = [TIME-1]
) BT,
(
SELECT STOCK_SYMBOL FROM (
SELECT STOCK_SYMBOL,TRADE_DATE FROM DragonTigerList WHERE TRADEDATE=PREVDAY)
TDL WHERE TDL.stock_symbol = ST.code
) LHB
WHERE ST.code = MT.code AND MT.code = VT.code AND VT.code = BT.code AND BT.code = LHB.code
AND ST.code in X
AND Chg > 2
AND MT.C2 > (ST.C1 * 1.05)
AND MT.C2 >= 5
AND VOL >= VT.VOL_AVG_21DAY AND VOL >= VT.VOL_AVG_5DAY AND VOL >= VT.VOL_AVG_10DAY AND VOL >= VT.VOL_AVG_30DAY AND VOL >= VT.VOL_AVG_60DAY AND VOL >= VT.VOL_AVG_240DAY
AND VOL >= VT.VOL_AVG_5DAY * 2 AND VOL >= VT.VOL_AVG_10DAY * 2 AND VOL >= VT.VOL_AVG_21DAY * 2 AND VOL >= VT.VOL_AVG_30DAY * 2 AND VOL >= VT.VOL_AVG_60DAY * 2
AND VOL >= VT.VOL_AVG_240DAY * 2 AND VOL >= 1000000
AND Stock_Minute_Numerical_Impact(VT.code, [TIME]) > 100
AND Chg < 20
ORDER BY Score DESC
LIMIT 10
Python代码参考
以下是Python代码实现该选股逻辑：
import pandas as pd
from typing import List
from datetime import datetime, timedelta
import talib
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)
net_amount_ratio = df['net_amount'].iloc[-1] / df['volume'].iloc[-1]
if df['dt'].iloc[-1] and \
(df['float_shares'].iloc[-1] / 1000000000 <= 5.5) and \
(df['turnover_rate'].iloc[-2] > 8) and (df['turnover_rate'].iloc[-2] < 20) and \
(df['turnover_rate'].iloc[-1] > 3) and (df['turnover_rate'].iloc[-1] < 12) and \
(df['pct_chg'].iloc[-1] * net_amount_ratio > 0):
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。
