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,60开头的股票。
选股逻辑分析
该选股策略主要通过筛选换手率和前25天有涨停来选择市场活跃度和热度较高的股票，同时通过限制选择60开头的股票来过滤一部分股票。这种选股逻辑不涉及基本面等因素，主要侧重于市场情绪和投资者热捧程度。
有何风险？
这种选股策略忽略了公司的基本面、盈利能力、资产质量等因素，选择的股票可能存在投资风险。同时，仅选择60开头的股票可能会忽略其他良好表现的股票，造成股票池的偏差。
如何优化？
可以在筛选标准中加入一些基本面和财务指标，例如市盈率、净利润增长率、资产质量等，综合考虑股票的盈利能力和风险程度。同时可以选择更多类型的股票，例如沪深A股和中小板股票，以使选股更加全面。
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，选择60开头的股票；同时加入市盈率、净利润增长率、资产质量等基本面和财务因素的考虑，综合考虑股票的盈利能力和风险程度。
同花顺指标公式代码参考
通达信指标代码：
SELECT(CLOSE>REF(CLOSE,1) AND (CLOSE-REF(CLOSE,1))/REF(CLOSE,1)>0.098
AND (VOL/(AMOUNT/OPEN/10000))>-0.12 AND (VOL/(AMOUNT/OPEN/10000))<=-0.03 AND
LEFT(CODE,2)='60' AND AVG((VOL/(AMOUNT/OPEN/10000)),25)>=3 AND
AVG((VOL/(AMOUNT/OPEN/10000)),25)<=12, COUNT(CLOSE>REF(CLOSE,1)
AND (CLOSE-REF(CLOSE,1))/REF(CLOSE,1)>0.098 AND (VOL/(AMOUNT/OPEN/10000))>-0.12 AND
(VOL/(AMOUNT/OPEN/10000))<=-0.03 AND LEFT(CODE,2)='60')/COUNT(CODE)>0.5) 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:
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:
year_data = api.get_k_data_year(code, 2021)
if len(year_data) > 0:
if year_data.iloc[-1]['close'] > year_data.iloc[-2]['close'] and \
(year_data.iloc[-1]['close'] - year_data.iloc[-2]['close'])/year_data.iloc[-2]['close'] > 0.098 and \
(year_data.iloc[-1]['vol']/(year_data.iloc[-1]['amount']/year_data.iloc[-1]['open']/10000)) > -0.12 and \
(year_data.iloc[-1]['vol']/(year_data.iloc[-1]['amount']/year_data.iloc[-1]['open']/10000)) <= -0.03 and \
sum(year_data.iloc[-29:-1]['close'] > year_data.iloc[-30:-2]['close'])/len(year_data.iloc[-29:-1]) > 0.5:
pe_ratio = api.get_finance_info(0, code)['data'][1][44]
net_profit_growth_ratio = api.get_finance_info(0, code)['data'][1][37]
asset_quality = api.get_finance_info(0, code)['data'][0][8]
if pe_ratio != '-' and \
net_profit_growth_ratio != '-' and \
asset_quality != '-' and \
float(pe_ratio) < 50 and \
float(net_profit_growth_ratio) > 0 and \
float(asset_quality) > 0.9:
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(pe_ratio),
'net_profit_growth_ratio': float(net_profit_growth_ratio),
'asset_quality': float(asset_quality)})
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。
