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,竞价时涨跌幅买入大单.特大单共计买入量大于0.7千万。
选股逻辑分析
该选股策略主要是寻找具有活跃换手率且存在较大资金买入的股票，并且具有良好的股价上涨潜力。通过加入竞价时涨跌幅指标来筛选当前表现相对较好的股票，并结合买入大单.特大单的买入量大于0.7千万这一筛选条件，保证股票具有持续上涨的潜力。
有何风险？
该选股策略主要是基于技术面和资金面的选股方式，因此，可能忽略了股票基本面表现不够理想的情况。因此，该选股策略具有较大的市场风险，且需要及时跟踪市场的变化。
如何优化？
可以加入其他指标，如RSI、KDJ等技术面指标来协助选股，并结合股票基本面等因素，如收入、资产、估值等，来选取市场表现较好的股票。
最终的选股逻辑
该选股策略选股逻辑为：选择换手率3%-12%、前25天有涨停、竞价时涨跌幅买入大单.特大单共计买入量大于0.7千万的股票。
同花顺指标公式代码参考
通达信指标代码：
VOL := VOL/100;
COND1:=VOL/HGVBARS(VOL,30) >= 3 AND VOL/HGVBARS(VOL,30) <= 12 AND LEN(C > REF(C,25)) >=1;
P:=CLOSE-REF(CLOSE,1);
COND2:=P*100/REF(CLOSE,1) >=1.5 OR P*100/REF(CLOSE,1) <=-1.5;
M:=BERL(100*VOL/HGVBARS(VOL,30),400);
COND3:=M >= 1;
COND4:=VOL_PRICE_DX2>=0.7 AND ((VOL_PRICE_DX1 = 0 AND BUY_VOL_PRICE_DX1 = SELL_VOL_PRICE_DX1) OR
((VOL_PRICE_DX1 > 0 AND BUY_VOL_PRICE_DX1 > SELL_VOL_PRICE_DX1) OR (VOL_PRICE_DX1 < 0 AND BUY_VOL_PRICE_DX1 < SELL_VOL_PRICE_DX1)));
SELECTED:= COND1 AND COND2 AND COND3 AND COND4;
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'])
df_stocks = pd.read_csv('stock_list.csv')
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]
bid1 = stock_info['bid1_price']
ask1 = stock_info['ask1_price']
price_limit_down = stock_info['lower_limit']
price_limit_up = stock_info['upper_limit']
vol_price_dx1 = stock_info['vol_price_dx1']
buy_vol_price_dx1 = stock_info['buy_vol_price_dx1']
sell_vol_price_dx1 = stock_info['sell_vol_price_dx1']
vol_price_dx2 = stock_info['vol_price_dx2']
k_data = api.get_security_bars(9, 0, code, 4, (datetime.today() - timedelta(days=1)).strftime('%Y-%m-%d'))
k_data.set_index('date', inplace=True)
vol = k_data['volume']
cond1 = vol.rolling(30).sum() / vol.rolling(30).mean() >= 3 and vol.rolling(30).sum() / vol.rolling(30).mean() <= 12 and \
len(k_data[k_data['pct_chg'] >= 9.90]) >= 1
p = k_data['close'] - k_data['close'].shift(1)
cond2 = ((p / k_data['close'].shift(1)) * 100 >= 1.5) | ((p / k_data['close'].shift(1)) * 100 <= -1.5)
m = np.mean(k_data['volume'].rolling(window=30, center=False).mean())
mstd = np.std(k_data['volume'].rolling(window=30, center=False).mean(), ddof=1)
# upper_bound, lower_bound = m + 2*mstd, m - 2*mstd
upper_bound = np.percentile(k_data['volume'].rolling(window=30, center=False).mean().values, 90)
lower_bound = np.percentile(k_data['volume'].rolling(window=30, center=False).mean().values, 10)
cond3 = m > upper_bound
cond4 = vol_price_dx2 >= 0.7 and (((vol_price_dx1 == 0) and (buy_vol_price_dx1 == sell_vol_price_dx1)) or
((vol_price_dx1 > 0) and (buy_vol_price_dx1 > sell_vol_price_dx1)) or
((vol_price_dx1 < 0) and (buy_vol_price_dx1 < sell_vol_price_dx1)))
if len(k_data) > 0 and cond1 and cond2 and cond3 and cond4:
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]})
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。
