问财量化选股策略逻辑
选取振幅大于1，前25天至少有一天涨停，DEA向上的股票进入待投资池。
选股逻辑分析
- 振幅大于1表明该股票波动性较大，有较大概率出现较高的涨幅；
- 前25天至少有一天涨停，代表该股票有望超额表现；
- DEA线是MACD技术指标中的一个中长期趋势线，DEA向上代表股票所处的中长期趋势有望持续向好。
有何风险？
- DEA线受市场行情影响较大，在市场震荡时，选出的股票数量可能会减少；
- 每个时期选择的DEA线指标周期可能会不同，因此需要注意指标选择的合理性。
如何优化？
- 可以考虑加入其他相关的指标，如K线形态、均线金叉等指标，提高选股的综合性分析；
- 对DEA选择周期要进行合理选择，避免因DEA选择过短而导致选出的股票波动性过大或是DEA选择过长而过度平滑化。
最终的选股逻辑
选取振幅大于1，前25天至少有一天涨停，DEA向上的股票进入待投资池。
同花顺指标公式代码参考
//振幅大于1
COND1:=(HIGH-LOW)/LOW>0.01;
//涨停，只选取前25天，这里举例取18
COND2:=REF(MAX(HIGH,1),1)/REF(CLOSE,1)>1.097 AND (HIGH=LOW) AND (CLOSE-REF(CLOSE,1))/REF(CLOSE,1)<0.009 AND (BARSSINCE(CLOSE=REF(MAX(HIGH,1),1))/COUNT>COUNT-18);
//MACD指标
DIF:=EMA(CLOSE,12)-EMA(CLOSE,26);
DEA:=EMA(DIF,9);
//DEA向上
COND3:=DEA>REF(DEA,1);
//综合条件
CONDITION:=COND1 AND COND2 AND COND3;
SIGNAL:=CHECKCOND(CONDITION,1);
Python代码参考
import pandas as pd
import akshare as ak
from talib import abstract
def get_trade_data(stock_code):
stock_history_df = ak.stock_zh_a_daily(symbol=stock_code, adjust="hfq")
stock_history_df.rename(columns={"交易日期":"date","开盘价":"open","最高价":"high","最低价":"low","收盘价":"close","成交量":"volume","成交额":"amount"}, inplace=True)
stock_history_df.sort_values("date", ascending=True, inplace=True)
stock_history_df['pct_chg'] = stock_history_df['close'].pct_change() * 100
stock_history_df['net_volume'] = stock_history_df['close'] * stock_history_df['volume'] * stock_history_df['pct_chg'] / 100 /10000
macd, signal, hist = abstract.MACD(stock_history_df['close'], fastperiod=12, slowperiod=26, signalperiod=9)
stock_history_df['dif'] = macd
stock_history_df['dea'] = signal
# 振幅
cond1 = (stock_history_df['high'] - stock_history_df['low']) / stock_history_df['low'] > 0.01
# 涨停
cond2 = (stock_history_df['high'] / stock_history_df['close'].shift(1) > 1.097) & (stock_history_df['high'] == stock_history_df['low']) & (stock_history_df['close'] / stock_history_df['close'].shift(1) - 1 < 0.009) & (stock_history_df['close'] == stock_history_df['high'].shift(1))
cond2 = cond2.rolling(window=18).sum() > 0
# DEA向上
cond3 = stock_history_df['dea'] > stock_history_df['dea'].shift(1)
# 综合条件
basic_cond = cond1 & cond2 & cond3
df = stock_history_df[basic_cond].reset_index(drop=True)
return df
def select(df):
return df.sort_values('net_volume', ascending=False)
## 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。
