利用python炒股talib_Python 通过 TALib 包构建股票自动技术分析

#coding = utf-8

importtushareasts

importtalib

importpandasaspd

importnumpyasnp

fromdatetimeimportdatetime

importsys

# code:代码 name:名字，industry:所属行业 area:地区 pe:市盈率 outstanding:流通股本 totals:总股本(万) totalAssets:总资产(万)

# liqidAssets:流动资产 fixedAssets:固定资产 reserved:公积金 bvps:每股收益 pd:每股净资 timeToMarket:上市日期

defget_stock_list():

df = ts.get_stock_basics()

returndf

defget_ta(df_code,Dist):

operate_array1 = []

operate_array2 = []

operate_array3 = []

count =0

forcodeindf_code.index:

# index,0 - 6 date：日期 open：开盘价 high：最高价 close：收盘价 low：最低价 volume：成交量 price_change：价格变动 p_change：涨跌幅

# 7-12 ma5：5日均价 ma10：10日均价 ma20:20日均价 v_ma5:5日均量v_ma10:10日均量 v_ma20:20日均量

df = ts.get_hist_data(code,start='-11-20')

dflen = df.shape[0]

count = count +1

ifdflen >35:

(df,operate1) = get_macd(df)

(df,operate2) = get_KDJ(df)

(df,operate3) = Get_RSI(df)

operate_array1.append(operate1)#round(df.iat[(dflen-1),16],2)

operate_array2.append(operate2)

operate_array3.append(operate3)

df_code['MACD']=pd.Series(operate_array1,index=df_code.index)

df_code['KDJ']=pd.Series(operate_array2,index=df_code.index)

df_code['RSI']=pd.Series(operate_array3,index=df_code.index)

returndf_code

#通过macd判断买进和买出

defget_macd(df):

#参数 12,26,9

macd,macdsignal,macdhist = talib.MACD(df['close'].values,fastperiod=12,slowperiod=26,signalperiod=9)

signal_ma5 = talib.MA(macdsignal,timeperiod=5,matype=0)

signal_ma10 = talib.MA(macdsignal,timeperiod=10,matype=0)

signal_ma20 = talib.MA(macdsignal,timeperiod=20,matype=0)

#13-15 DIF DEA DIF-DEA

df['macd'] = pd.Series(macd,index=df.index)#DIF

df['signal'] = pd.Series(macdsignal,index=df.index)#DEA

df['macdhist'] = pd.Series(macdhist,index=df.index)#DIF-DEA

dflen = df.shape[0]

MAlen =len(signal_ma5)

operator =0

#俩个数组 1.DIF、DEA均为正，DIF向上穿过DEA

# 2.DIF、DEA均为负，DIF向下穿过DEA

ifdf.iat[(dflen-1),13] >0:

ifdf.iat[(dflen-1),14] >0:

ifdf.iat[(dflen-1),13] > df.iat[(dflen-1),14]anddf.iat[(dflen-2),13] <= df.iat[(dflen-2),14]:

operator = operator+10#买进

else:

ifdf.iat[(dflen-1),14] <0:

ifdf.iat[(dflen-1),13] == df.iat[(dflen-2),14]:

operator = operator -10#卖出

#DEA与K线发生背离 K线趋势向上，MACD向下，顶背离，将要下降；K线趋势向下，MACD向上，底背离，将要上升

ifdf.iat[(dflen-1),7]>=df.iat[(dflen-1),8]anddf.iat[(dflen-1),8]>=df.iat[(dflen-1),9]:#K线上涨

ifsignal_ma5[MAlen-1]<=signal_ma10[MAlen-1]andsignal_ma10[MAlen-1]<=signal_ma20[MAlen-1]:#DEA下降

operator = operator-1

ifdf.iat[(dflen-1),7]<=df.iat[(dflen-1),8]anddf.iat[(dflen-1),8]<=df.iat[(dflen-1),9]:#K线下降

ifsignal_ma5[MAlen-1]>=signal_ma10[MAlen-1]andsignal_ma10[MAlen-1]>=signal_ma20[MAlen-1]:#DEA上升

operator = operator+1

#分析MACD柱状图 由负变正将上涨

ifdf.iat[(dflen-1),15] >0anddflen >30:

foriinrange(1,26):

ifdf.iat[(dflen-1-i),15] <=0:

operator = operator +5

break

#由正变负 将降低

ifdf.iat[(dflen-1),15] <0anddflen >30:

foriinrange(1,26):

ifdf.iat[(dflen-1-i),15] >=0:

operator = operator -5

break

returndf,operator

#通过KDJ判断买进和卖出

defget_KDJ(df):

#参数9,3,3

slowk,slowd = talib.STOCH(df['high'].values,df['low'].values,df['close'].values,fastk_period=9,slowk_period=3,slowk_matype=0,slowd_period=3,slowd_matype=0)

slowkMA5 = talib.MA(slowk,timeperiod=5,matype=0)

slowkMA10 = talib.MA(slowk,timeperiod=10,matype=0)

slowkMA20 = talib.MA(slowk,timeperiod=20,matype=0)

slowdMA5 = talib.MA(slowd,timeperiod=5,matype=0)

slowdMA10 = talib.MA(slowd,timeperiod=10,matype=0)

slowdMA20 = talib.MA(slowd,timeperiod=20,matype=0)

#16,17 K,D

df['slowk'] = pd.Series(slowk,index=df.index)#K

df['slowd'] = pd.Series(slowd,index=df.index)#D

dflen = df.shape[0]

MAlen =len(slowdMA5)

operator =0

#1.K线是快速确认线 -- 数值在90以上为超买信号，数值在10以下为超卖信号；2.D大于80为超卖状态，小于20为超卖状态

ifdf.iat[(dflen-1),16] >=90:

operator = operator -3

ifdf.iat[(dflen-1),16] <=10:

operator = operator +3

ifdf.iat[(dflen-1),17] >=80:

operator = operator -3

ifdf.iat[(dflen-1),17] <=20:

operator = operator +3

#上涨趋势中，K线向上穿过D线，黄金交叉，将进入多头市场，股价将上涨，应该买进

ifdf.iat[(dflen-1),16] > df.iat[(dflen-1),17]anddf.iat[(dflen-2),16] <= df.iat[(dflen-2),17]:

operator = operator +10

#下降趋势中，K线向下穿过D线，死亡交叉，将进入空头市场，股价将下降，应该卖出

ifdf.iat[(dflen-1),16] < df.iat[(dflen-1),17]anddf.iat[(dflen-2),16] >= df.iat[(dflen-2),17]:

operator = operator -10

#3.当随机指标与股价出现背离时，一般为转势的信号。

ifdf.iat[(dflen-1),7]>=df.iat[(dflen-1),8]anddf.iat[(dflen-1),8]>=df.iat[(dflen-1),9]:#K线上涨

if(slowkMA5[MAlen-1]<=slowkMA10[MAlen-1]andslowkMA10[MAlen-1]<=slowkMA20[MAlen-1])or\

(slowdMA5[MAlen-1]<=slowdMA10[MAlen-1]andslowdMA10[MAlen-1]<=slowdMA20[MAlen-1]):#K,D下降

operator = operator -1

elifdf.iat[(dflen-1),7]<=df.iat[(dflen-1),8]anddf.iat[(dflen-1),8]<=df.iat[(dflen-1),9]:#K线下降

if(slowkMA5[MAlen-1]>=slowkMA10[MAlen-1]andslowkMA10[MAlen-1]>=slowkMA20[MAlen-1])or\

(slowdMA5[MAlen-1]>=slowdMA10[MAlen-1]andslowdMA10[MAlen-1]>=slowdMA20[MAlen-1]):#K,D上涨

operator = operator +1

return(df,operator)

#通过RSI判断买入卖出

defGet_RSI(df):

#参数14,5

slowreal = talib.RSI(np.array(df['close']),timeperiod=14)

fastreal = talib.RSI(np.array(df['close']),timeperiod=5)

slowrealMA5 = talib.MA(slowreal,timeperiod=5,matype=0)

slowrealMA10 = talib.MA(slowreal,timeperiod=10,matype=0)

slowrealMA20 = talib.MA(slowreal,timeperiod=20,matype=0)

fastrealMA5 = talib.MA(fastreal,timeperiod=5,matype=0)

fastrealMA10 = talib.MA(fastreal,timeperiod=10,matype=0)

fastrealMA20 = talib.MA(fastreal,timeperiod=20,matype=0)

#18-19 慢速real，快速real

df['slowreal']=pd.Series(slowreal,index=df.index)#慢速real 18

df['fastreal']=pd.Series(fastreal,index=df.index)#快速real 19

dflen = df.shape[0]

MAlen =len(slowrealMA5)

operate =0

#RSI>80为超买区，RSI<20为超卖区

ifdf.iat[(dflen-1),18]>80ordf.iat[(dflen-1),19]>80:

operate = operate -2

elifdf.iat[(dflen-1),18]<20ordf.iat[(dflen-1),19]<20:

operate = operate +2

#RSI上穿50分界线为买入信号，下破50分界线为卖出信号

if(df.iat[(dflen-2),18]<=50anddf.iat[(dflen-1),18]>50)or(df.iat[(dflen-2),19]<=50anddf.iat[(dflen-1),19]>50):

operate = operate +4

elif(df.iat[(dflen-2),18]>=50anddf.iat[(dflen-1),18]<50)or(df.iat[(dflen-2),19]>=50anddf.iat[(dflen-1),19]<50):

operate = operate -4

#RSI掉头向下为卖出讯号，RSI掉头向上为买入信号

ifdf.iat[(dflen-1),7]>=df.iat[(dflen-1),8]anddf.iat[(dflen-1),8]>=df.iat[(dflen-1),9]:#K线上涨

if(slowrealMA5[MAlen-1]<=slowrealMA10[MAlen-1]andslowrealMA10[MAlen-1]<=slowrealMA20[MAlen-1])or\

(fastrealMA5[MAlen-1]<=fastrealMA10[MAlen-1]andfastrealMA10[MAlen-1]<=fastrealMA20[MAlen-1]):#RSI下降

operate = operate -1

elifdf.iat[(dflen-1),7]<=df.iat[(dflen-1),8]anddf.iat[(dflen-1),8]<=df.iat[(dflen-1),9]:#K线下降

if(slowrealMA5[MAlen-1]>=slowrealMA10[MAlen-1]andslowrealMA10[MAlen-1]>=slowrealMA20[MAlen-1])or\

(fastrealMA5[MAlen-1]>=fastrealMA10[MAlen-1]andfastrealMA10[MAlen-1]>=fastrealMA20[MAlen-1]):#RSI上涨

operate = operate +1

#慢速线与快速线比较观察，若两线同向上，升势较强；若两线同向下，跌势较强；若快速线上穿慢速线为买入信号；若快速线下穿慢速线为卖出信号

ifdf.iat[(dflen-1),19]> df.iat[(dflen-1),18]anddf.iat[(dflen-2),19]<=df.iat[(dflen-2),18]:

operate = operate +10

elifdf.iat[(dflen-1),19]< df.iat[(dflen-1),18]anddf.iat[(dflen-2),19]>=df.iat[(dflen-2),18]:

operate = operate -10

return(df,operate)

defOutput_Csv(df,Dist):

TODAY = datetime.date.today()

CURRENTDAY=TODAY.strftime('%Y-%m-%d')

# reload(sys)

sys.setdefaultencoding("gbk")

df.to_csv(Dist+CURRENTDAY+'stock.csv',encoding='gbk')#选择保存

df = get_stock_list()

Dist ='data/TEST'

df = get_ta(df,Dist)

Output_Csv(df,Dist)

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