问财量化选股策略逻辑
选股逻辑为：在换手率在3%~12%之间、买一量大于卖一量、未清偿可转债简称不可为空的股票中选取。
选股逻辑分析
该选股策略主要从交易活跃度、市场参与度、转债情况等角度综合考虑，选取具有一定上涨潜力的股票。买一量大于卖一量是选取市场需求强劲的标准，同时转债未清偿则可能存在转债股的买方意愿。因此优选转债未清偿，现金流充足、债务水平低的企业是更具上涨潜力的目标。
有何风险？
此选股策略过于依赖市场情绪和投机性需求，可能存在市场短期行情波动和资金大幅流入流出的风险。与此同时，转债情况的变化也可能导致选股误判。
如何优化？
可以将技术指标与基本面因素相结合，增强选股的可靠性。同时，可以加入财务指标等长期因素的考虑，如盈利能力、成长能力等。此外，可以采用定期估值分析，以确定投资策略的优劣性。
最终的选股逻辑
在换手率在3%~12%之间、买一量大于卖一量、未清偿可转债简称不可为空的股票中选取。
同花顺指标公式代码参考
SELECT SYMBOL FROM (
SELECT SYMBOL FROM GDH WHERE NAME = '换手率'
AND (CAST(DATA AS NUMBER) > 3) AND (CAST(DATA AS NUMBER) < 12)
AND SYMBOL IN (SELECT STOCK_CODE FROM STOCK_BASIC WHERE MARKET = '主板' AND LIST_STATUS = '上市')
AND SYMBOL IN (SELECT STOCK_CODE FROM SDB WHERE NAME = '买一' AND CAST(DATA AS NUMBER) > CAST(FDATA AS NUMBER))
AND SYMBOL IN (SELECT SYMBOL FROM CBR_CONV_BOND WHERE NAME = '未偿余额' AND DATA IS NOT NULL AND DATA != '' AND SYMBOL != '')
) A
LEFT JOIN (
SELECT SYMBOL FROM STOCK_FINANCE WHERE MARKET_TYPE = '主板'
AND CASH_FLOW >= 500000000
AND DEBT_TO_ASSETS_RATIO < 70
AND TOTAL_ASSETS >= 1000000000
AND NET_PROFIT >= 50000000
) B ON A.SYMBOL = B.SYMBOL
WHERE B.SYMBOL IS NOT NULL;
python代码参考
import pandas as pd
import tushare as ts
def select_stocks(date):
pro = ts.pro_api()
# 查询挂单大量大于卖单、未清偿可转债简称不为空的股票
market_df = pro.market_detail(symbol='', trade_date=date)
df1 = market_df[(market_df['bid_vol'] > market_df['ask_vol'])]
cbr_df = pro.conversion_rate(ts_code='', trade_date=date, fields='ts_code,conv_unl_vol')
cbr_symbols = cbr_df[cbr_df['conv_unl_vol'] > 0]['ts_code'].tolist()
df1 = df1[df1['ts_code'].isin(cbr_symbols)]
# 按换手率筛选股票
daily_basic_df = pro.daily_basic(ts_code='', trade_date=date, fields='ts_code,turnover_rate')
df1 = pd.merge(df1, daily_basic_df, on='ts_code', how='inner')
df1 = df1[(df1['turnover_rate'] > 3) & (df1['turnover_rate'] < 12)]
# 按市场筛选股票
df1 = df1[df1['ts_code'].str.startswith('0')]
# 按买卖盘挂单量筛选股票
sdb_df = pro.stk_holdernumber(ts_code='', start_date=date, end_date=date, fields='ts_code,mkv')
sdb_df.rename(columns={'ts_code': 'symbol'}, inplace=True)
df1 = pd.merge(df1, sdb_df, on='symbol', how='inner')
df1 = df1[(df1['buy_sm_vol'] > df1['sell_sm_vol']) & (df1['buy_sm_vol'] > df1['mkv'])]
# 查询转债未清偿余额
cbr_conv_df = pro.conversion_rate(ts_code='', trade_date=date, fields='ts_code,conv_unl_vol')
df1 = pd.merge(df1, cbr_conv_df, on='ts_code', how='inner')
df1 = df1[df1['conv_unl_vol'] > 0]
# 按财务指标筛选股票
finance_df = pro.fina_indicator(ts_code='', start_date=date, end_date=date, fields='ts_code,cash_flow,debt_to_assets_ratio,total_assets,net_profit')
finance_df = finance_df.fillna(0)
finance_df = finance_df[(finance_df['cash_flow'] >= 500000000) & (finance_df['debt_to_assets_ratio'] < 70) &
(finance_df['total_assets'] >= 1000000000) & (finance_df['net_profit'] >= 50000000)]
df1 = pd.merge(df1, finance_df, on='ts_code', how='inner')
# 合并所有指标，返回选股结果
return df1['ts_code']
## 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。
