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,反包。
选股逻辑分析
该选股策略主要通过筛选市场活跃度较高的股票，选择前25天有涨停的股票，同时对反弹进行把握，可能能从市场情绪和技术面上选择赚取一定收益。
有何风险？
相对而言，该选股策略风险较大。对于一些后续未能反弹或出现股价逆势下跌的股票，可能会造成较大的亏损。
如何优化？
可以对反弹要求更加严格，比如设置阻力位和支撑位等指标，判断股票价格走势的可能性和可持续性，降低风险；对于涨幅过大的股票，可以适当进行剔除，避免投资时间不当造成的损失。
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；同时加入反包，判断股票价格反弹的可能性和可持续性；同时进行涨幅过大的股票进行剔除。
同花顺指标公式代码参考
通达信指标代码：
SELECT VOL/(AMOUNT/OPEN/10000) > 0.03 AND VOL/(AMOUNT/OPEN/10000) <= 0.12 AND
AVG((CLOSE-REF(CLOSE,1))/REF(CLOSE,1),25)>=0 AND
LASTDAY(CLOSE>5 AND OPEN>LOW AND CLOSE-OPEN<(REF(OPEN,1)-REF(CLOSE,1))*(REF(OPEN,1)-REF(CLOSE,1))*10000/REF(CLOSE,1))
,
COUNT(VOL/(AMOUNT/OPEN/10000) > 0.03 AND VOL/(AMOUNT/OPEN/10000) <= 0.12 AND
AVG((CLOSE-REF(CLOSE,1))/REF(CLOSE,1),25)>=0 AND
LASTDAY(CLOSE>5 AND OPEN>LOW AND CLOSE-OPEN<(REF(OPEN,1)-REF(CLOSE,1))*(REF(OPEN,1)-REF(CLOSE,1))*10000/REF(CLOSE,1)))
/ 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 \
df_stocks[df_stocks['code'] == code]['delist_date'].values[0] == '':
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':
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 and \
k_data['close'].iloc[-1] / k_data['open'].iloc[-1] < 1 and \
k_data['open'].iloc[-1] <= k_data['low'].iloc[-1] and \
k_data['close'].iloc[-1] - k_data['open'].iloc[-1] < (k_data['open'].iloc[-2] - k_data['close'].iloc[-2]) * 10000 / k_data['close'].iloc[-2]:
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]})
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。
