问财量化选股策略逻辑
选取MACD值在零轴以上、企业性质良好，且周线MACD在零轴之上的股票作为投资标的。该策略从技术面和基本面两个角度出发，选取MACD值在零轴以上的股票代表其经济基本面整体上处于扩张状态，企业性质良好代表着公司未来具有较好的盈利前景，周线MACD在零轴之上则说明该股票处于整体上涨趋势中。
选股逻辑分析
该选股策略注重技术面和基本面的因素， MACD值在零轴以上的股票代表公司经济基本面的扩张状态，企业性质良好代表着公司未来具有良好的盈利前景，周线MACD在零轴之上则代表着该股票处于整体上涨趋势中。
有何风险？
该选股策略可能会忽略基本面数据的更新，如公司信息披露，财务数据等情况。同时，该选股策略过于注重技术面指标，可能会忽略一些企业的基本面是否为周期性的，从而做出不合理的投资决策。
如何优化？
可以加入更多基本面因素，如市盈率、市净率等因素来筛选出性价比更高的标的。另外，加入相应的技术面因素，如KDJ等以便更全面地反映股票的市场情况。
最终的选股逻辑
选取MACD值在零轴以上、企业性质良好，且周线MACD在零轴之上的股票作为投资标的。
同花顺指标公式代码参考
WCCI_5>0 AND WCCI_10>0 AND MACD>0 AND WEEKS>=52
Python代码参考
import talib
import jqdatasdk as jq
from datetime import datetime, timedelta
import numpy as np
jq.auth("账户名", "密码")
today = datetime.now().strftime("%Y-%m-%d")
months_ago = (datetime.now() - timedelta(days=365*2)).strftime('%Y-%m-%d')
q = jq.query(jq.valuation).filter(jq.valuation.code.in_(jq.get_all_securities().index.tolist()))
df_valuation = jq.get_fundamentals(q)
df_valuation = df_valuation[(df_valuation['pe_ratio']>0) & (df_valuation['pe_ratio']<100)]
df_stock_basics = jq.get_all_securities('stock')
df_stock_basics = df_stock_basics[df_stock_basics['start_date'] < months_ago]
price_df = jq.get_price(df_stock_basics.index.tolist(), end_date=today, frequency="daily", fields=["low", "high", "close", "volume"])
df = df_valuation.merge(price_df.reset_index(), left_index=True, right_on='code')[['name', 'pe_ratio', 'pb_ratio', 'low', 'high', 'close', 'volume']]
df = df[(df['pe_ratio']>0) & (df['pb_ratio']>0)]
df['market_cap'] = df['close'] * df['volume']
df = df[df['market_cap'] > 2e+8].sort_values('market_cap', ascending=False)
df['macd'], df['macdsignal'], df['macdhist'] = talib.MACD(df['close'], fastperiod=12, slowperiod=26, signalperiod=9)
df['MA'] = talib.MA(df['close'], timeperiod=20)
df['VMA'] = talib.MA(df['volume'], timeperiod=20)
df = df[df['macd']>0]
df_vol = jq.get_money_flow(df.index.tolist(), end_date=today)[['date', 'sec_code', 'net_amount_main']]
df_vol['is_big_trade'] = np.where(df_vol['net_amount_main'] > df_vol['net_amount_main'].rolling(10).mean() + 3*df_vol['net_amount_main'].rolling(30).std(), 1, 0)
df_vol.set_index(['date', 'sec_code'], inplace=True)
df_vol['MA'] = df_vol.groupby('sec_code')['net_amount_main'].transform(lambda x: talib.MA(x, timeperiod=20))
df_vol = df_vol[df_vol['is_big_trade'] == 1]
df = df[df.index.isin(df_vol.index.get_level_values('sec_code').unique())]
df['week_macd'], df['week_signal'], df['week_hist'] = talib.MACD(df['close'], fastperiod=12, slowperiod=26, signalperiod=9)
df_week = jq.get_price(df.index.tolist(), start_date=months_ago, end_date=today, frequency='1w', fields=['low', 'high', 'close', 'volume'])
df_week['macd'], df_week['macd_signal'], df_week['macd_hist'] = talib.MACD(df_week['close'], fastperiod=12, slowperiod=26, signalperiod=9)
df_week = df_week[df_week.index >= months_ago]
df_week['WCCI_5'] = talib.WILLR(df_week['high'], df_week['low'], df_week['close'], timeperiod=5)/(-100)
df_week['WCCI_10'] = talib.WILLR(df_week['high'], df_week['low'], df_week['close'], timeperiod=10)/(-100)
df_week = df_week[df_week['macd'] > 0]
df_week_vol = jq.get_money_flow(df_week.index.tolist(), end_date=today)[['date', 'sec_code', 'net_amount_main']]
df_week_vol['is_big_trade'] = np.where(df_week_vol['net_amount_main'] > df_week_vol['net_amount_main'].rolling(10).mean() + 3*df_week_vol['net_amount_main'].rolling(30).std(), 1, 0)
df_week_vol.set_index(['date', 'sec_code'], inplace=True)
df_week_vol['MA'] = df_week_vol.groupby('sec_code')['net_amount_main'].transform(lambda x: talib.MA(x, timeperiod=20))
df_week_vol = df_week_vol[df_week_vol['is_big_trade'] == 1]
df_week = df_week[df_week.index.isin(df_week_vol.index.get_level_values('sec_code').unique())]
df = df[df.index.isin(df_week.index)]
df = df[df.index.isin(df_vol.index.get_level_values('sec_code').unique())]
df = df.sort_values('market_cap', ascending=False)
stock_list = df.index.tolist()
for code in stock_list:
stock_name = jq.get_security_info(code).display_name
q = jq.query(jq.indicator.MACD).filter(jq.indicator.MACD.code==code).filter(jq.indicator.MACD.date==today)
df_macd = jq.indicator.run_query(q)
if len(df_macd) > 0 and df_macd.iloc[0]['macd'] > 0:
df_week_macd = df_week.loc[code, 'macd_hist']
if df_week_macd > 0:
print("(" + code + ")" + stock_name + " 符合条件")
## 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。
