问财量化选股策略逻辑
该选股策略选股逻辑为：换手率3%-12%,前25天有涨停,机器人概念且流通市值小于100亿。
选股逻辑分析
该选股策略主要通过筛选市场活跃度和热度较高的股票，选择前25天有涨停的股票，可能能从市场情绪较好的股票中选择赚取一定收益。同时，选取机器人概念且流通市值小于100亿的股票，选择小市值的股票，有机会在未来有更大的上涨空间。
有何风险？
该选股策略的风险在于忽略了企业的基本面和估值等因素，可能会选择到基本面不够强大或估值过高的个股。同时，排除大市值的股票，可能会错过那些大公司和龙头股，需要谨慎权衡。
如何优化？
可以在选股逻辑中加入企业基本面和估值等因素的考虑，例如市盈率、市净率、净利润增长率等指标，综合考虑股票的估值和盈利能力。另外，可以考虑公司的技术实力和前景，选择有潜力的行业和公司，从而做到更全面的选股。
最终的选股逻辑
该选股策略选股逻辑为：筛选换手率3%-12%、前25天有涨停；同时加入机器人概念且流通市值小于100亿的限制；同时加入市盈率、市净率、净利润增长率等基本面和财务因素的考虑，综合考虑股票的估值和盈利能力；同时考虑公司的技术实力和前景，从而做到更全面的选股。
同花顺指标公式代码参考
通达信指标代码：
SELECT((VOL/(AMOUNT/OPEN/10000))>-0.12 AND (VOL/(AMOUNT/OPEN/10000))<=-0.03 AND
AVG((CLOSE-REF(CLOSE,1))/REF(CLOSE,1),25)>=0 AND
LEFT(CODE,3)!='131' AND LEFT(CODE,3)!='204' AND
CODE IN (SELECT CODE FROM F10GFIN WHERE EXISTS
(SELECT 1 FROM F9CCE WHERE F10GFIN.CODE=F9CCE.SECURITY_CODE AND
F9CCE.CYC=4 AND F9CCE.CONCEPT='机器人产业链'
AND F9CCE.TOTAL_SHARES < 10000000000))
,
COUNT((VOL/(AMOUNT/OPEN/10000))>-0.12 AND (VOL/(AMOUNT/OPEN/10000))<=-0.03 AND
AVG((CLOSE-REF(CLOSE,1))/REF(CLOSE,1),25)>=0 AND
LEFT(CODE,3)!='131' AND LEFT(CODE,3)!='204' AND
CODE IN (SELECT CODE FROM F10GFIN WHERE EXISTS
(SELECT 1 FROM F9CCE WHERE F10GFIN.CODE=F9CCE.SECURITY_CODE AND
F9CCE.CYC=4 AND F9CCE.CONCEPT='机器人产业链'
AND F9CCE.TOTAL_SHARES < 10000000000)))
/ COUNT(CODE) > 0.5 AND LEFT(CODE,2)='60' AS RESULT
python代码参考
import pandas as pd
import numpy as np
import time
from datetime import datetime, timedelta
from pytdx.hq import TdxHq_API
from pytdx.util.best_ip import select_best_ip
api = TdxHq_API()
ip = select_best_ip('tdx')
api.connect(ip['ip'], ip['port'])
end_date = datetime.today().strftime('%Y-%m-%d')
start_date = (datetime.today() - timedelta(days=25)).strftime('%Y-%m-%d')
all_stocks = api.get_security_list(0, 0)
df_stocks = pd.DataFrame(all_stocks, columns=['code', 'name', 'market_type', 'exchange_type',
'industry', 'list_date', 'delist_date', 'infolevel'])
selected_stocks = []
for code in df_stocks['code']:
if code.startswith('60') and df_stocks[df_stocks['code'] == code]['market_type'].values[0] != 5 and \
code[:3] != '131' and code[:3] != '204':
if not api.is_ST(code) and api.get_stock_list_by_market(1)['name'].tolist().count(df_stocks[df_stocks['code'] == code]['name'].values[0]) == 0 and \
api.get_stock_list_by_market(2)['name'].tolist().count(df_stocks[df_stocks['code'] == code]['name'].values[0]) == 0 and \
df_stocks[df_stocks['code'] == code]['list_date'].values[0] <= '2000-01-01' and df_stocks[df_stocks['code'] == code]['delist_date'].values[0] == '':
try:
stock_info = api.get_security_quotes([code])[0]
k_data = api.get_security_bars(9, 0, code, 4, datetime.today().strftime('%Y-%m-%d'))
k_data['date'] = k_data['datetime'].apply(lambda x: datetime.fromtimestamp(time.mktime(time.strptime(str(x), '%Y%m%d%H%M%S'))).strftime('%Y-%m-%d'))
k_data.set_index('date', inplace=True)
k_data = k_data.loc[(k_data.index >= start_date) & (k_data.index <= end_date)]
if len(k_data) > 0 and \
k_data['turnover'].quantile(0.7) >= 0.03 and \
k_data['turnover'].quantile(0.7) <= 0.12 and \
len(k_data[k_data['pct_chg'] >= 9.90]) >= 1:
finance_data = api.get_finance_info(0, code)['data']
if finance_data[1][7] != '-' and \
finance_data[1][9] != '0.00' and \
finance_data[1][9] != '-' and \
finance_data[1][18] != '0.00' and \
finance_data[1][18] != '-' and \
finance_data[1][27] != '-' and \
finance_data[0][15] != '-':
concept_data = api.get_finance_info(0x0D, code)['data']
for concept in concept_data:
if concept[2] == '机器人产业链' and float(concept[5]) < 10000000000:
selected_stocks.append({'code': code,
'name': df_stocks[df_stocks['code'] == code]['name'].values[0],
'market_type': df_stocks[df_stocks['code'] == code]['market_type'].values[0],
'industry': df_stocks[df_stocks['code'] == code]['industry'].values[0],
'infolevel': df_stocks[df_stocks['code'] == code]['infolevel'].values[0],
'pe_ratio': float(finance_data[1][7])/float(finance_data[1][9]),
'pb_ratio': float(finance_data[1][9])/float(finance_data[1][18]),
'net_profit_growth_ratio': float(finance_data[1][27])/100,
'asset_liability_ratio': float(finance_data[0][15])/100})
break
except Exception as e:
print(e)
continue
df_selected_stocks = pd.DataFrame(selected_stocks)
api.disconnect()
## 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。
