问财量化选股策略逻辑
选股逻辑为：在换手率3%-12%、反包的股票中，选出昨日换手率乘以今日竞价成交量除以昨日成交量的结果在0.5-2之间的股票。
选股逻辑分析
该选股策略进一步考虑了昨日和今日的成交情况，选股结果更加准确，但仍然只针对股票活跃度因素进行筛选。
有何风险？
该选股策略仍有可能会出现噪声干扰的情况，还需要进一步加入其它因素进行筛选。
如何优化？
可以进一步加入技术指标和基本面指标等因素进行筛选，建立更为综合的选股模型。
最终的选股逻辑
选股条件为：在换手率3%-12%、反包的股票中，选出昨日换手率乘以今日竞价成交量除以昨日成交量的结果在0.5-2之间的股票。
同花顺指标公式代码参考
选股条件：turnover_rate>=3 AND turnover_rate<=12
AND (STICKORYC("fgs", ["1t", "2t", "3t", "4t"], "t_1", "lg",
"sm", "b") + STICKORYC("fjj", ["1t", "2t", "3t", "4t"], "t_1",
"lg", "sm", "b")) > 0
AND (REF(turnover_rate,1)*STICKORCY("cjlx",["1t"],"t_1","t_0"))/
STICKORCY("cjlx",["1t"],"t_1","t_1") > 0.5
AND (REF(turnover_rate,1)*STICKORCY("cjlx",["1t"],"t_1","t_0"))/
STICKORCY("cjlx",["1t"],"t_1","t_1") < 2
选股结果：fml('turnover_rate>=3 AND turnover_rate<=12
AND (STICKORYC("fgs", ["1t", "2t", "3t", "4t"], "t_1", "lg",
"sm", "b") + STICKORYC("fjj", ["1t", "2t", "3t", "4t"], "t_1",
"lg", "sm", "b")) > 0
AND (REF(turnover_rate,1)*STICKORCY("cjlx",["1t"],"t_1","t_0"))/
STICKORCY("cjlx",["1t"],"t_1","t_1") > 0.5
AND (REF(turnover_rate,1)*STICKORCY("cjlx",["1t"],"t_1","t_0"))/
STICKORCY("cjlx",["1t"],"t_1","t_1") < 2', 80)
Python代码参考
import tushare as ts
import pandas as pd
ts.set_token('your_token')
pro = ts.pro_api()
# 筛选好股票函数
def select_good_stocks():
# 换手率3%-12%
df1 = pro.daily_basic(ts_code='', trade_date='20220110',
fields='ts_code,trade_date,turnover_rate')
df1 = df1[(df1['turnover_rate'] >= 3) & (df1['turnover_rate'] <= 12)]
# 反包策略
df2 = pro.daily(ts_code='', trade_date='20220110',
fields='ts_code,trade_date,low,high,pre_close')
df2['AT'] = df2['high'] - df2['low']
df2['ST'] = abs(df2['pre_close'] - df2['low'])
df2['BT'] = abs(df2['pre_close'] - df2['high'])
df2['RCT1'] = df2['ST'] / df2['AT']
df2['RCT2'] = df2['BT'] / df2['AT']
df2['RC'] = df2[['RCT1', 'RCT2']].min(axis=1)
df3 = df2[df2['trade_date'] == '20220110']
good_stocks = df3[df3['RC'] <= 0.2]['ts_code']
# 加入其它指标
good_stocks = pd.merge(good_stocks.to_frame(),
pro.daily_basic(ts_code=good_stocks.to_string(index=False),
trade_date='20220110',
fields='ts_code,close,pe,pb,total_mv,
float_mv,turnover_rate,roe_diluted'),
on='ts_code', how='inner')
# 昨日换手率乘以今日竞价成交量/昨日成交量
df4 = pro.market_info(trade_date='20220110',
fields='ts_code,trade_date,turnover_rate')
df5 = pro.top_list(trade_date='20220110')
df5['STICK'] = df5['price'] * df5['vol']
df6 = pd.merge(df4, df5, on='ts_code', how='left')
good_stocks = pd.merge(good_stocks, df6, on='ts_code', how='inner')
good_stocks = good_stocks[(good_stocks['turnover_rate_x'] *
good_stocks['STICK']) / good_stocks['vol'] >
0.5]
good_stocks = good_stocks[(good_stocks['turnover_rate_x'] *
good_stocks['STICK']) / good_stocks['vol'] <
2]
return good_stocks.reset_index(drop=True)
good_stocks = select_good_stocks()
print(good_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。
