一、双均线概念
均线：对于每一个交易日，都可以计算出前N天的移动平均值，然后把这些平均值连起来，成为一条线，就叫做N日移动平均线。移动平均线常用线有5日、10日、30日、60日、120日的指标。
5日和10日的是短线操作参照指标，称作日均线指标；
30日和60日的是中期均线指标，称作季均线指标；
120日和240日的是长期均线指标，称作年均线指标。
金叉：短期均线上穿长期均线，买入信号。
死叉：短期均线下穿长期均线，卖出信号。
交易策略：金叉买入，死叉卖出。
二、金叉死叉交易练习
1、使用tushare包获取某股票的历史行情
import numpy as npimport pandas as pdimport matplotlib.pyplot as plt # Python绘图和数据可视化的工具包import tushare as tsts.get_k_data("601318", start="-01-30").to_csv('601318.csv')df = pd.read_csv('601318.csv', index_col="date", parse_dates=['date'])[['open', 'close', 'high', 'low']]print(df)"""open close highlowdate -03-01 21.254 19.890 21.666 19.469-03-02 19.979 19.728 20.166 19.503... ............-01-22 85.000 85.220 85.480 83.830-01-23 84.010 83.490 84.560 82.480[3076 rows x 4 columns]"""
2、使用pandas包计算该股票历史数据的5日和30均线
5日均线则前4天没有均线，30日均线则前29天没有均线。
(1)普通方法
df['ma5'] = np.nan# 创建5日均值列df['ma30'] = np.nan # 创建30日均值列print(df)"""open close highlow ma5 ma30date -03-01 21.254 19.890 21.666 19.469 NaN NaN-03-02 19.979 19.728 20.166 19.503 NaN NaN... ............ ... ...-01-22 85.000 85.220 85.480 83.830 NaN NaN-01-23 84.010 83.490 84.560 82.480 NaN NaN"""for i in range(4, len(df)): # 从第五个到最后一个# 当天和前四天的收盘价的平均值为5日均线值df.loc[df.index[i], 'ma5'] = df["close"][i-4:i+1].mean() # i=4是第五天，[0,5]print(df)"""open close highlowma5 ma30date -03-01 21.254 19.890 21.666 19.469NaN NaN-03-02 19.979 19.728 20.166 19.503NaN NaN-03-05 19.545 18.865 19.626 18.504NaN NaN-03-06 18.704 19.235 19.554 18.597NaN NaN-03-07 19.252 19.758 19.936 19.090 19.4952 NaN... ............... ...-01-23 84.010 83.490 84.560 82.480 85.6320 NaN"""for i in range(29, len(df)): # 从第30个到最后一个# 当天和前29天的收盘价的平均值为30日均线值df.loc[df.index[i], 'ma30'] = df["close"][i-29:i+1].mean()print(df)"""open close highlowma5 ma30date -03-01 21.254 19.890 21.666 19.469NaN NaN-03-02 19.979 19.728 20.166 19.503NaN NaN-03-05 19.545 18.865 19.626 18.504NaN NaN-03-06 18.704 19.235 19.554 18.597NaN NaN-03-07 19.252 19.758 19.936 19.090 19.4952 NaN... ............... ...-01-17 86.150 86.250 86.900 85.850 86.1780 85.292333-01-20 88.300 87.600 88.700 87.350 86.4080 85.387667-01-21 87.000 85.600 87.290 85.600 86.1620 85.452667-01-22 85.000 85.220 85.480 83.830 86.0440 85.507000-01-23 84.010 83.490 84.560 82.480 85.6320 85.488667"""
(2)简单方法
df['ma5'] = df['close'].rolling(5).mean()df['ma30'] = df['close'].rolling(30).mean()print(df)"""open close highlowma5 ma30date -03-01 21.254 19.890 21.666 19.469NaN NaN-03-02 19.979 19.728 20.166 19.503NaN NaN-03-05 19.545 18.865 19.626 18.504NaN NaN-03-06 18.704 19.235 19.554 18.597NaN NaN-03-07 19.252 19.758 19.936 19.090 19.4952 NaN... ............... ...-01-17 86.150 86.250 86.900 85.850 86.1780 85.292333-01-20 88.300 87.600 88.700 87.350 86.4080 85.387667-01-21 87.000 85.600 87.290 85.600 86.1620 85.452667-01-22 85.000 85.220 85.480 83.830 86.0440 85.507000-01-23 84.010 83.490 84.560 82.480 85.6320 85.488667"""
3、使用matplotlib包可视化历史数据的收盘价和两条均线
df[['close', 'ma5', 'ma30']].plot()plt.show()
运行后显示效果：
4、分析输出所有金叉日期和死叉日期
如果前一交易日五日均线小于30日均线，后一交易日五日均线大于30日均线，则说明是金叉；
如果前一交易日五日均线大于30日均线，后一交易日五日均线小于30日均线，则说明是死叉。
(1)循环的解法
# dropna()删除含有空数据的全部行,axis参数可删除含义空数据的全部列df = df.dropna()gloden_cross = [] # 金叉death_cross = []# 死叉for i in range(0, len(df)-1):if df['ma5'][i] >= df['ma30'][i] and df['ma5'][i-1] <= df['ma30'][i-1]:gloden_cross.append(df.index[i])if df['ma5'][i] <= df['ma30'][i] and df['ma5'][i-1] >= df['ma30'][i-1]:death_cross.append(df.index[i])print('金叉日期', gloden_cross)"""金叉日期 [Timestamp('-06-14 00:00:00'), Timestamp('-12-10 00:00:00'),..., Timestamp('-01-02 00:00:00')]"""print('死叉日期', death_cross)"""死叉日期 [Timestamp('-06-04 00:00:00'), Timestamp('-11-06 00:00:00'),..., Timestamp('-12-23 00:00:00')]"""
(2)简便算法
# dropna()删除含有空数据的全部行,axis参数可删除含义空数据的全部列df = df.dropna()sr1 = df['ma5'] < df['ma30']sr2 = df['ma5'] >= df['ma30']death_cross = df[sr1 & sr2.shift(1)].indexgolden_cross = df[-(sr1 | sr2.shift(1))].indexprint('金叉日期', golden_cross)"""金叉日期 DatetimeIndex(['-04-12', '-06-14', '-12-10', '-04-23',..., '-01-02']"""print('死叉日期', death_cross)"""死叉日期 DatetimeIndex(['-06-04', '-11-06', '-12-13', '-05-20',..., '-11-12', '-12-23']"""
5、使用该策略的炒股收益率
如果我从1月1日起，初始资金为100000元，金叉尽量买入，死叉全部卖出，则到今天为止，我的炒股收益率？
# 炒股收益率first_money = 100000money = first_money # 持有的资金hold = 0# 持有的股票sr1 = pd.Series(1, index=golden_cross)sr2 = pd.Series(0, index=death_cross)sr = sr1.append(sr2).sort_index() # 将两个表合并，并按时间排序sr = sr['-01-01':] # 从1月1日开始for i in range(0, len(sr)):p = df['open'][sr.index[i]]# 当天的开盘价if sr.iloc[i] == 1:# 金叉buy = money // (100 * p) # 买多少手hold += buy * 100money -= buy * 100 * pelse:# 死叉money += hold * phold = 0 # 持有股票重置为0# 计算最后一天股票市值加上持有的资金p = df['open'][-1]now_money = hold * p + moneyprint('当前持有资产总额:', now_money)print('盈亏情况:', now_money - first_money)"""当前持有资产总额: 551977.7999999997盈亏情况: 451977.7999999997"""
需要注意的是：这里金叉死叉都是按照当天的收盘价计算的。但是如果得到当天的收盘价就已经无法进行交易了。因此要让策略可行，需要按照当天的开盘价计算。
