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、今日上涨幅度大于1%（比较同板块的股票涨幅）、昨日的竞价换手率大于0.26的主板股票。
选股逻辑分析
相较于上一个逻辑，新增了昨日竞价换手率大于0.26的判断因素，该指标反映了股票流通性和活跃度，更加注重选取流动性好、热度较高的股票。
有何风险？
由于考虑了前一天的竞价换手率，可能会出现一些波动较大的、不稳定的股票被选入。此外，选股逻辑的指标较多，可能会影响选股效率。
如何优化？
可以适当调整选股条件，并尝试加入其他因素如市盈率、股息率等基本面指标，使得选股更具综合性和稳健性。同时，使用机器学习等方法优化选股结果，提高选股效率。
最终的选股逻辑
选择换手率在3%-12%之间、今日上涨幅度大于1%（比较同板块的股票涨幅）、昨日竞价换手率大于0.26的主板股票。
同花顺指标公式代码参考
SET_STUDY_NAME('竞价换手率');
SET_CHINESE_CHARSET("GBK");
COLORSTICKS = true;
/* 昨日竞价换手率 */
CON_MEAN = AVERAGE(JJ2, 1);
CHECK_POINT = IF(JJ2>CON_MEAN*1.3,1,0);
/* 过滤判断条件 */
MATCH_GROUP = '主板';
TURN_N = 23;
FRONT_V0 = LLV(LOW,23);
FRONT_V1 = LLV(LOW,46);
BACK_V0 = HHV(HIGH,23);
BACK_V1 = HHV(HIGH,46);
FRONT_RISK = IF(FRONT_V1>FRONT_V0,FRONT_V1+(FRONT_V1-FRONT_V0),FRONT_V1);
BACK_RISK = IF(BACK_V1>BACK_V0,BACK_V1+(BACK_V1-BACK_V0),BACK_V1);
ADVANCE = IF(AMOUNT/HSL > ADVANCE(MATCH_GROUP,FROM=0,TO=23),1,0);
BACK = IF(C>REF(REF(LOW,23),1),1,0);
CHECK_BACK = IF(CLOSE>FRONT_RISK AND BACK == 0 AND HHV(BACK)==REF(HHV(BACK),1),1,0);
LAST_CONDITION = HSL >= 3.0 AND HSL <= 12.0 AND CHECK_BACK AND MATCHGROUP(MATCH_GROUP);
LAST_POINT = IF(YJ0>0.26 AND JJ2>0.016 AND CHECK_POINT AND LAST_CONDITION, 1, 0);
SET_RANK_FIELD(LAST_POINT);
SET_SORT_RULE(ASCENDING);
BATCHSELECT(LAST_POINT);
python代码参考
import baostock as bs
import pandas as pd
from datetime import datetime, timedelta
#### 登陆系统 ####
lg = bs.login()
#### 获取满足条件的股票 #####
rs = bs.query_all_stock(day=datetime.now().strftime("%Y-%m-%d"))
stock_list = []
for code in rs.get_row_data():
if not code.startswith('sh.') and not code.startswith('sz.'):
continue
if code.startswith('sh.688') or code.startswith('sz.300'):
continue
if code.startswith('sh.110') or code.startswith('sz.110'):
continue
# 换手率 3%-12%
k_data = bs.query_history_k_data_plus(code, "date,open,high,low,close,volume", start_date=(datetime.now()-timedelta(days=1)).strftime("%Y-%m-%d"), end_date=(datetime.now()-timedelta(days=1)).strftime("%Y-%m-%d"), frequency="d")
if k_data.error_code == '0' and len(k_data.data)>0:
check_point1 = k_data.data[0][5]>=3 and k_data.data[0][5]<=12
else:
continue
# 今日涨幅 > 1%
index_rs = bs.query_history_k_data_plus('sh.000001', 'close', start_date=(datetime.now()-timedelta(days=1)).strftime("%Y-%m-%d"), end_date=(datetime.now()-timedelta(days=1)).strftime("%Y-%m-%d"), frequency='d')
if index_rs.error_code == '0' and len(index_rs.data)>0:
index_close = float(index_rs.data[0][0])
else:
continue
k_data_compare = bs.query_history_k_data_plus(code, "date,close", start_date=(datetime.now()-timedelta(days=1)).strftime("%Y-%m-%d"), end_date=(datetime.now()-timedelta(days=1)).strftime("%Y-%m-%d"), frequency="d")
if k_data_compare.error_code == '0' and len(k_data_compare.data)>0:
check_point2 = k_data_compare.data[0][1]/index_close-1 > 0.01
else:
continue
# 昨日竞价换手率 > 0.26
jj_rs = bs.query_history_k_data_plus(code, "date,turnOverRate,jj_turnoverRate", start_date=(datetime.now()-timedelta(days=2)).strftime("%Y-%m-%d"), end_date=(datetime.now()-timedelta(days=1)).strftime("%Y-%m-%d"), frequency="d")
if jj_rs.error_code == '0' and len(jj_rs.data)>0:
check_point3 = float(jj_rs.data[0][2])>0.26
else:
continue
# 主升起动
data_k = bs.query_history_k_data_plus(code, "date,open,high,low,close,volume, amount, amplitude,turn, vr", frequency="d")
if data_k.error_code == '0' and len(data_k.data)>0:
k_info = pd.DataFrame(data_k.data, columns=data_k.fields)
k_info.index = pd.to_datetime(k_info.date)
advance = ta.MAX(k_info['amount']/k_info['turn'], timeperiod=23)
close_v1 = ta.LLV(k_info['low'], timeperiod=23)
back = ta.IF(k_info['close'] > ta.REF(close_v1, 1), 1, 0)
hhv_back_v1 = ta.SMA((k_info['close'] - ta.REF(close_v1, 1)) / (close_v1 * 0.01), timeperiod=WINDING_N)
hhv_back_v2 = ta.SMA((k_info['close'] - close_v1)/(close_v1 * 0.01), timeperiod=WINDING_N)
check_point4 = 0
if hhv_back_v1[-1] < hhv_back_v2[-1]:
check_point4 = 1
else:
continue
if check_point1 and check_point2 and check_point3 and check_point4:
data_list = []
data_list.append(code)
stock_list.append(data_list)
df = pd.DataFrame(stock_list, columns=['code'])
df_length = len(df)
if df_length > 0:
print(df.head(5))
##### 登出系统 #####
bs.logout()
## 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。
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