问财量化选股策略逻辑
选取振幅大于1，KDJ刚形成金叉，三个技术指标同时金叉的股票为投资组合。
选股逻辑分析
- 振幅大于1表明该股票波动性大，有望获得高回报；
- KDJ刚形成金叉，预示着市场情绪化好转，有较大概率涨势明显；
- 三个技术指标同时金叉，说明多重信号确认该股票具有较大上涨潜力。
有何风险？
- 忽略了公司基本面因素，如行业竞争，产品质量等因素，导致投资决策失误；
- 市场产生大幅波动时，选股逻辑容易出现失效。
如何优化？
- 增加基本面指标的考虑，如PEG等指标，提高投资决策的科学性；
- 结合股票板块的行情，优选热门板块股票，提高投资的胜算。
最终的选股逻辑
选取振幅大于1，KDJ刚形成金叉，MACD、DMI、RSI三个技术指标同时金叉的股票为投资组合。
同花顺指标公式代码参考
// 振幅大于1
COND1:=(HIGH-LOW)/LOW>0.01;
// KDJ金叉
RSV:=(CLOSE-LOWEST(CLOSE,9))/(HIGHEST(CLOSE,9)-LOWEST(CLOSE,9))*100;
K:SMA(RSV,3,1);
D:SMA(K,3,1);
J:3*K-2*D;
GCROSS1: CROSS(J,D);
COND2:=REF(GCROSS1,1)=0 AND GCROSS1=1;
// MACD金叉
DIF:=EMA(CLOSE,12)-EMA(CLOSE,26);
DEA:=EMA(DIF,9);
MACD:=(DIF-DEA)*2;
GCROSS2: CROSS(MACD,DEA);
COND3:=REF(GCROSS2,1)=0 AND GCROSS2=1;
// DMI金叉
MTR:=MAX(MAX(HIGH-LOW,ABS(HIGH-REF(CLOSE,1))),ABS(LOW-REF(CLOSE,1)));
HD:=HIGH-REF(HIGH,1);
LD:=REF(LOW,1)-LOW;
DMP:=MA(MAX(MAX(HD,0),MTR),14);
DMM:=MA(MAX(MAX(LD,0),MTR),14);
# PDI: DMP/(DMP+DMM)*100;
# MDI: DMM/(DMP+DMM)*100;
PDI:=DMP/(DMP+DMM);
MDI:=DMM/(DMP+DMM);
ADXR:=EMA(EXPMA(PDI-MDI,14),14);
GCROSS3: CROSS(PDI,MDI);
COND4:=REF(GCROSS3,1)=0 AND GCROSS3=1;
// RSI金叉
RSI1: RSI(CLOSE, 6);
RSI2: RSI(CLOSE, 12);
RSI3: RSI(CLOSE, 24);
GCROSS4: CROSS(RSI1,RSI2) AND CROSS(RSI2,RSI3);
COND5:=REF(GCROSS4,1)=0 AND GCROSS4=1;
// 综合条件
CONDITION:=COND1 AND COND2 AND COND3 AND COND4 AND COND5;
SIGNAL: CHECKCOND(CONDITION,1);
Python代码参考
import pandas as pd
import akshare as ak
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","流通市值":"circ_cap","主力净流入":"net_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
return stock_history_df
def select(df):
# 振幅
cond1 = (df['high'] - df['low']) / df['low'] > 0.01
# KDJ
high, low, close = df['high'].values, df['low'].values, df['close'].values
rsv = (close - pd.Series(low).rolling(window=9).min().values) / (pd.Series(high).rolling(window=9).max().values - pd.Series(low).rolling(window=9).min().values) * 100
k = pd.Series(rsv).rolling(window=3).mean().values
d = pd.Series(k).rolling(window=3).mean().values
j = 3 * k - 2 * d
kdj_cond = (j[-1] > d[-1]) & (j[-2] < d[-2])
# MACD
ema12 = pd.Series(close).ewm(span=12).mean()
ema26 = pd.Series(close).ewm(span=26).mean()
dif = ema12 - ema26
dea = pd.Series(dif).ewm(span=9).mean()
macd = (dif - dea) * 2
macd_cond = (macd.iloc[-1] > 0) & (macd.iloc[-2] < 0)
# DMI
tr = pd.Series(np.maximum(np.maximum(high - low, abs(high - pd.Series(close).shift())), abs(low - pd.Series(close).shift())))
hd = high - pd.Series(high).shift()
ld = pd.Series(low).shift() - low
dmp = pd.Series(np.maximum(np.maximum(hd, 0), tr)).rolling(window=14).mean().values
dmm = pd.Series(np.maximum(np.maximum(ld, 0), tr)).rolling(window=14).mean().values
pdi = dmp / (dmp + dmm)
mdi = dmm / (dmp + dmm)
adxr = pd.Series((pdi - mdi).ewm(span=14).mean()).ewm(span=14).mean().values
dmi_cond = (pdi[-1] > mdi[-1]) & (pdi[-2] < mdi[-2])
# RSI
rsi6 = pd.Series(close).diff(1).rolling(window=6).apply(lambda x: (x[x > 0].sum())/(x[x < 0].abs().sum() + x[x > 0].sum()) *100).values
rsi12 = pd.Series(close).diff(1).rolling(window=12).apply(lambda x: (x[x > 0].sum())/(x[x < 0].abs().sum() + x[x > 0].sum()) *100).values
rsi24 = pd.Series(close).diff(1).rolling(window=24).apply(lambda x: (x[x > 0].sum())/(x[x < 0].abs().sum() + x[x > 0].sum()) *100).values
rsi_cond = (rsi6[-1] > rsi12[-1]) & (rsi12[-1] > rsi24[-1])
# 综合条件
basic_cond = cond1 & kdj_cond & macd_cond & dmi_cond & rsi_cond
df = df.loc[basic_cond].reset_index()
return df
## 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。
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