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,底部抬高。
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，并且底部呈现出上升趋势的股票。其通过K线形态来判断底部抬高的情况，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。同时，该策略对K线形态的判断有一定的主观性，可能存在判断失误的情况。
如何优化？
可以进一步优化K线形态的判断方式，引入更多的股票形态指标，如MACD，RSI，布林带等。同时加入其他的技术面和基本面指标，如财务指标、行业趋势等。对选股策略进行量化优化，比如引入机器学习算法对股票形态进行判断和分析。
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；同时要求底部抬高。
同花顺指标公式代码参考
通达信指标代码：
MA5:=MA(CLOSE,5);
MA10:=MA(CLOSE,10);
MA20:=MA(CLOSE,20);
MA30:=MA(CLOSE,30);
MA60:=MA(CLOSE,60);
MA120:=MA(CLOSE,120);
MA250:=MA(CLOSE,250);
H:=HHV(HIGH,60);
L:=LLV(LOW,60);
var2:=L+(H-L)*0.5;
var1:=H-(H-L)*0.25;
var3:=MA20*1.05+MA30*0.95;
Uptrend:=MA5>MA10 AND MA10>MA20 AND MA20>MA30 AND MA30>MA60;
MA_UP:=MA20>MA60 AND MA60>MA120 AND MA120>MA250;
v1:=-1;
v2:=-1;
if(LASTDAY(L<var2 AND CLOSE>VAR1), 0, IF(REF(LASTDAY(MA(CLOSE,5)>LASTDAY(MA(CLOSE,60)) AND CLOSE>LASTDAY(HHV(HIGH,60)))), 1, -1));
if(MAUP AND Uptrend, 1, -1);
if(REF(CROSS(0,EMA(CLOSE,8))-1=0, 1, 0);
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:
ma5 = k_data['close'].rolling(5).mean()
ma10 = k_data['close'].rolling(10).mean()
ma20 = k_data['close'].rolling(20).mean()
ma30 = k_data['close'].rolling(30).mean()
ma60 = k_data['close'].rolling(60).mean()
ma120 = k_data['close'].rolling(120).mean()
ma250 = k_data['close'].rolling(250).mean()
hhv60 = k_data['high'].rolling(60).max()
llv60 = k_data['low'].rolling(60).min()
var2 = llv60 + (hhv60 - llv60) * 0.5
var1 = hhv60 - (hhv60 - llv60) * 0.25
var3 = ma20 * 1.05 + ma30 * 0.95
uptrend = (ma5 > ma10) & (ma10 > ma20) & (ma20 > ma30) & (ma30 > ma60)
ma_up = (ma20 > ma60) & (ma60 > ma120) & (ma120 > ma250)
v1 = -1
if k_data['low'].iloc[-1] < var2.iloc[-1] and k_data['close'].iloc[-1] > var1.iloc[-1]:
if ma5.iloc[-1] > ma60.iloc[-1] and k_data['close'].iloc[-1] > hhv60.iloc[-1]:
v1 = 1
if ma_up and uptrend and v1 == 1:
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。
