股市有风险，投资需谨慎!!!股市有风险，投资需谨慎!!!股市有风险，投资需谨慎!!!
MACD的详细介绍请移步：macd百度百科
话不多说，上代码：
# -*- coding: utf-8 -*-'''实现功能：1.macd指标计算2.记录金叉死叉数据并输出CSV文件'''import pandas as pdfrom datetime import datetime,timedeltaimport tushare as tsimport syssys.setrecursionlimit(3000) #设置最大遍历次数pd.set_option('display.width', 280) # 打印最大列数设置def MACD(df, n_fast, n_slow, ksgn='close'):'''def MACD(df, n_fast, n_slow): #MACD指标信号和MACD的区别, MACD Signal and MACD difference MACD是查拉尔·阿佩尔(Geral Appel)于1979年提出的，由一快及一慢指数移动平均（EMA）之间的差计算出来。“快”指短时期的EMA，而“慢”则指长时期的EMA，最常用的是12及26日EMA：【输入】df, pd.dataframe格式数据源n，时间长度ksgn，列名，一般是：close收盘价【输出】 df, pd.dataframe格式数据源,增加了3栏：macd,sign,mdiff'''xnam = 'macd'.format(n=n_fast, n2=n_slow)xnam2 = 'msign'.format(n=n_fast, n2=n_slow)xnam3 = 'mdiff'.format(n=n_fast, n2=n_slow)EMAfast = pd.Series(pd.ewma(df[ksgn], span=n_fast, min_periods=n_slow - 1))EMAslow = pd.Series(pd.ewma(df[ksgn], span=n_slow, min_periods=n_slow - 1))MACD = pd.Series(EMAfast - EMAslow, name=xnam)MACDsign = pd.Series(pd.ewma(MACD, span=9, min_periods=8), name=xnam2)MACDdiff = pd.Series(MACD - MACDsign, name=xnam3)df = df.join(MACD)df = df.join(MACDsign)df = df.join(MACDdiff)return dfdef gold_kill(df):'''金叉死叉信息筛选:param df::return:'''df['msign_old'] = df['msign'].shift(1)df['macd_old'] = df['macd'].shift(1)killdate = df[(df['msign_old']<df['macd_old']) & (df['msign'] > df['macd'])]golddate = df[(df['msign_old']>=df['macd_old']) & (df['msign'] <= df['macd'])]return golddate,killdate#记录运行开始时间stardate = datetime.now()#获取最新A股数据stockdf = ts.get_stock_basics()#金叉死叉数据集stock_gold_kill_info = pd.DataFrame(columns=['code','name','date','close','lastgolddate','lastgoldclose','lastkilldate','lastkillclose'])# 计数变量n = 1#从180的数据开始计算，可更改startime = datetime.strftime(stardate-timedelta(days=180),'%Y-%m-%d')#遍历A股数据for code in stockdf.index:# 运行进度条i = int(n/len(stockdf)*100)s1 = "\r[%s%s]%d/%d"%("*"*i," "*(100-i),n,len(stockdf))sys.stdout.write(s1)sys.stdout.flush()n += 1# 获取截止当前日期的日K数据df = ts.get_hist_data(code,startime)#新股、停牌股等特殊情况没有金叉死叉数据，报错忽略try:df.sort_index(inplace=True)macddf = MACD(df,12,26)gold,kill=gold_kill(macddf)gold_kill_info = {'lastgolddate': gold.tail(1).index.values[0], #最近一次金叉时间'lastgoldclose': gold.tail(1).close.values[0], #最近一次金叉收盘价'lastkilldate': kill.tail(1).index.values[0], #最近一次死叉时间'lastkillclose': kill.tail(1).close.values[0], #最近一次死叉收盘价'date':df.tail(1).index.values[0], #最新收盘价日期'code':code, #股票代码'close':df.tail(1).close.values[0], #最新收盘价'name':stockdf[stockdf.index == code].name.values[0] #股票名字}except (IndexError,AttributeError) as e:continue# 追加到金叉死叉数据集stock_gold_kill_info = stock_gold_kill_info.append(pd.DataFrame.from_dict(gold_kill_info,orient='index').T,ignore_index=True)#保存金叉死叉数据集，目录与运行文件一样，可更改stock_gold_kill_info.to_csv('./stock_gold_kill_info.csv',index = False,encoding='gbk')#记录运行结束时间enddate = datetime.now()print('\n运行时间：',enddate-stardate)
运行结果展示（图片中出现的股票信息仅做展示使用，不构成投资建议）：
更多量化投资策略欢迎添加微信讨论：
