python pyplot k线图_利用matplotlib画用于机器学习的K线图练手任务

#-*- utf-8 -*-

__author__='chen shaowu'

importmatplotlib as mplimportmatplotlib.pyplot as pltimportnumpy as npimportos#全局变量：常量

X=14Y=20Z1=6Z2=3

#常量修改函数

def set_value(x=14,y=20,z1=6,z2=3):globalX,Y,Z1,Z2

X,Y,Z1,Z2=x,y,z1,z2#计算本数据段sigma

defcount_sigma(data1,index):globalX

allf=0#所有F之和

i=0#包含当天

while i<=X:

maxp=data1[index-i][3]

minp=data1[index-i][4]

cp=data1[index-i-1][1]#前一天的收盘价

f=max(abs(maxp-minp),abs(maxp-cp),abs(cp-minp))/cp

f=min(f,0.191)#if f>0.191:f=0.191

allf+=f

i+=1sigma=allf/Xreturnsigma#判断是不是大涨、跌

defjudge1(data1,index,greaterate,lendata1):globalY

i=1

while i<=Y:#涨幅=今日收盘相对当前图最后一日收盘的涨幅

if(index+i)==lendata1:return -3#当图后面的数据不足20个，不画这个图，直接over

yc=data1[index][1]#close price of the last day in the plot

tc=data1[index+i][1]#today close price

growrate=(tc-yc)/ycif growrate>0:if growrate>=greaterate:return 2

elif growrate<0:if abs(growrate)>=greaterate:return -2i+=1

return0#无大涨、跌时判断有无小涨、跌

defjudge2(data1,index,smallrate,lendata1):globalY

i=1

while i<=Y:if(index+i)==lendata1:return -3#当图后面的数据不足20个，不画这个图，直接over

yc=data1[index][1]#close price of the last day of the plot

tc=data1[index+i][1]#today close price

growrate=(tc-yc)/ycif growrate>0:if growrate>=smallrate:return 1

elif growrate<0:if abs(growrate)>=smallrate:return -1i+=1

return0#分类函数

#mark=0#{0:平；-1；小跌；-2：大跌；1：小涨；2：大涨;-3:数据不足}

def judge(data1,index,lendata1):#'index' is the index of final day

globalZ1,Z2if index==lendata1-1:return -3#当天为最后一个数据，让程序直接over啦

sigma=count_sigma(data1,index)

greaterate=sigma*Z1

smallrate=sigma*Z2

mark=judge1(data1,index,greaterate,lendata1)#首先判断有无大涨跌

if mark==-3:return -3#当图后面的数据不足20个，不画这个图，直接over

elif abs(mark)==2:return mark #大，over

elif mark==0:

mark=judge2(data1,index,smallrate,lendata1) #无大，判断有无小

return mark ###返回：1/0/-1

defscaler(data1,x,y,N):

data=data1[x:y]

sigma= count_sigma(data1,y-1)

min0= min(x[4] for x indata)

max0= max(x[3] for x indata)

sum0=0for i indata:

sum0+=(i[3]+i[4])/2m=sum0/60

return [min(m-N*sigma,min0),max(m+N*sigma,max0)]#找到伸缩因子N

def find_N(data1,lim=0.85):

N= -1count=0

num_pic= int((len(data1)-60)/5)#图片数量

while count/num_pic

N+=1

#遍历所有数据

x =0

y= 60

while y

data= data1[x:y]#当前数据

sigma = count_sigma(data1,y-1)#中心价格：

sum0=0for i indata:

sum0+=(i[3]+i[4])/2m=sum0/60min0= min(x[4] for x indata)

max0= max(x[3] for x indata)

ymin= m-N*sigma

ymax= m+N*sigmaif ymin<=min0 and ymax>=max0:

count+=1x+=5y+=5

print(N)returnNdefscaler(data1,x,y,N):

data=data1[x:y]

sigma= count_sigma(data1,y-1)

min0= min(x[4] for x indata)

max0= max(x[3] for x indata)

sum0=0for i indata:

sum0+=(i[3]+i[4])/2m=sum0/60

return [min(m-N*sigma,min0),max(m+N*sigma,max0)]'''def scaler(data1,x,y,index):

global X

N=3

data=data1[x:y]

sigma=count_sigma(data1,index)

min0=min(x[4] for x in data)

max0=max(x[3] for x in data)

sum0=0

for i in data:

sum0+=(i[3]+i[4])/2

m=sum0/60

ymin=min(m-N*sigma,min0)

ymax=max(m+N*sigma,max0)

return [min0,max0,ymin,ymax]#只能出现两种结果：1、刚刚好铺满；2、上层或下层留白'''

#主函数

defmain():#转到数据所在目录

os.chdir('C:\\Users\Steve\Desktop\python程序\量化.K线')#打开数据

with open('股票数据.csv') as file:

dataC=[]

dataD=[]

dataS=[]

lines=(x for x in file.readlines())#generator;save the memory

for row inlines:

f=row.strip('\n').strip(',').split(',')if len(f)==6 or len(f)==11 or len(f)==16 and ('日期' not inf):

temp=[f[0]]

temp.extend([float(x)for x in f[1:6]])

dataC.append(temp)#data\cohl\changerate

if len(f)==11 or len(f)==16 and ('日期' not inf):

temp=[f[0]]

temp.extend([float(x)for x in f[6:11]])

dataD.append(temp)#data\cohl\changerate

if len(f)==16 and ('日期' not inf):

temp=[f[0]]

temp.extend([float(x)for x in f[11:16]])

dataS.append(temp)#data\cohl\changerate

dic={0:'ChinaBanktest',1:'DaHuatest',2:'ShangRongtest'}

j=0while j<3:if j==0:data1=dataCelif j==1:data1=dataDelif j==2:data1=dataS

N=find_N(data1) #y轴伸缩因子

x=0

y=60k=0while y<=len(data1):if data1[y-1][5]==0 or len([x[5] for x in data1[x:y] if x[5]==0])>len(data1[x:y])*2/3:pass

else:

fig,ax=plt.subplots(figsize=(64,64))

ax.fill_between(range(60),np.clip([a[5] for a in data1[x:y]],0,15),0,color='blue')

plt.ylim(0,15)

plt.xlim(0.5,59.5)#配合下面完成横向2 60 2 pixel！

plt.axis('off')

ax2=ax.twinx()

ymin,ymax=scaler(data1,x,y,N)

i=0for a indata1[x:y]:

c,o,h,l=a[1:5]if c>=o:#yang

low=o

high=c

color1='#800000'color2='#ff0000'

else:#ying

low=c

high=o

color1='#008000'color2='#00ff00'ax2.bar(i,h-l,bottom=l,width=1,color=color1)

ax2.bar(i,high-low,bottom=low,width=1,color=color2)if c==o==h==l:

ax2.bar(i,(ymax-ymin)/60,bottom=l,width=1,color=color1)

i+=1plt.axis('off')

plt.ylim((ymin,ymax))

plt.subplots_adjust(right=62/64, left=2/64,bottom=2/64,top=62/64)#纵向2 60 2pixel在这完成！

os.chdir('C:\\Users\\Steve\\Desktop\\python程序\\量化.K线\\figure')

dpi= 1mark= judge(data1,y-1,len(data1))#注意这里的减一，使得index为其在数据中的确切index;len(data1)用来处理后面长度不足Y的数据

if mark==-3:break#当图后面的数据不足，不画这个图，直接over

if mark==2:

folder='%s\\++'%dic[j]