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）、主升起动的主板股票。
选股逻辑分析
该选股逻辑除了注重技术面外，加入了对于主升起动的判断，更加注重选股的稳定性和可靠性。同时，将股票选择范围限定在主板上，减少了选择垃圾股的风险。
有何风险？
该策略可能会略过一些潜力较大的股票。同时，股票的短线波动也可能会影响选股结果。主升起动的判断也可能存在一定的误差。
如何优化？
可以适当调整选股条件，并尝试加入基本面因素如公司财务情况、行业地位等等，使选择更具综合性。同时，可以增加其他技术指标的判断来进一步筛选。
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）、主升起动的主板股票。
同花顺指标公式代码参考
SET_STUDY_NAME('升起动');
SET_CHINESE_CHARSET("GBK");
COLORSTICKS = true;
WINDING_N = 4;
MATCH_GROUP = '主板'; // 选股条件
CONDITION1 = HSL >= 3.0 AND HSL <= 12.0; // 换手率在3%-12%之间
CONDITION2 = MAX(CLOSE/REF(CLOSE,1),INDEXPRV(MATCH_GROUP))>1.01; // 今日上涨幅度大于1%
/* 主升起动 */
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 = CONDITION1 AND CONDITION2 AND CHECK_BACK; // 最终选股逻辑
SET_RANK_FIELD(CHECK_BACK);
SET_SORT_RULE(ASCENDING);
SET_RANK_FIELD(HSL);
SET_SORT_RULE(ASCENDING);
BATCHSELECT(last_condition);
python代码参考
import baostock as bs
import pandas as pd
import talib as ta
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
# 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_point3 = 0
if hhv_back_v1[-1] < hhv_back_v2[-1]:
check_point3 = 1
else:
continue
if check_point1 and check_point2 and check_point3:
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。
## 如果有任何问题请添加 下方的二维码进群提问。
