利用matplotlib画用于机器学习的K线图练手任务
前个阶段完成了利用matplotlib画用于机器学习的K线图练手任务,
代码如下：
#-*- utf-8 -*- __author__='chen shaowu' import matplotlib as mpl import matplotlib.pyplot as plt import numpy as np import os #全局变量：常量 X=14 Y=20 Z1=6 Z2=3 #常量修改函数 def set_value(x=14,y=20,z1=6,z2=3): global X,Y,Z1,Z2 X,Y,Z1,Z2=x,y,z1,z2 #计算本数据段sigma def count_sigma(data1,index): global X 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+=1 sigma=allf/X return sigma #判断是不是大涨、跌 def judge1(data1,index,greaterate,lendata1): global Y 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)/yc if growrate>0: if growrate>=greaterate: return 2 elif growrate<0: if abs(growrate)>=greaterate: return -2 i+=1 return 0 #无大涨、跌时判断有无小涨、跌 def judge2(data1,index,smallrate,lendata1): global Y 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)/yc if growrate>0: if growrate>=smallrate: return 1 elif growrate<0: if abs(growrate)>=smallrate: return -1 i+=1 return 0 #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 global Z1,Z2 if 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 def scaler(data1,x,y,N): data = data1[x:y] sigma = count_sigma(data1,y-1) 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 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 = -1 count = 0 num_pic = int((len(data1)-60)/5)#图片数量 while count/num_pic<lim: N+=1 #遍历所有数据 x = 0 y = 60 while y<len(data1): data = data1[x:y]#当前数据 sigma = count_sigma(data1,y-1) #中心价格： sum0=0 for i in data: sum0+=(i[3]+i[4])/2 m=sum0/60 min0 = min(x[4] for x in data) max0 = max(x[3] for x in data) ymin = m-N*sigma ymax = m+N*sigma if ymin<=min0 and ymax>=max0: count+=1 x+=5 y+=5 print(N) return N def scaler(data1,x,y,N): data = data1[x:y] sigma = count_sigma(data1,y-1) 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 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 def main(): #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 in lines: f=row.strip('\n').strip(',').split(',') if len(f)==6 or len(f)==11 or len(f)==16 and ('日期' not in f): 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 in f): 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 in f): 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=0 while j<3: if j==0:data1=dataC elif j==1:data1=dataD elif j==2:data1=dataS N=find_N(data1) #y轴伸缩因子 x=0 y=60 k=0 while 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=0 for a in data1[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+=1 plt.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 = 1 mark = 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] os.makedirs(folder,exist_ok=True) os.chdir(folder) plt.savefig('%d%s.png'%(k,'++'),format='png',dpi=dpi) if mark==1: folder='%s\\+'%dic[j] os.makedirs(folder,exist_ok=True) os.chdir(folder) plt.savefig('%d%s.png'%(k,'+'),format='png',dpi=dpi) if mark==0: folder='%s\\flat'%dic[j] os.makedirs(folder,exist_ok=True) os.chdir(folder) plt.savefig('%d%s.png'%(k,'flat'),format='png',dpi=dpi) if mark==-1: folder='%s\\-'%dic[j] os.makedirs(folder,exist_ok=True) os.chdir(folder) plt.savefig('%d%s.png'%(k,'-'),format='png',dpi=dpi) if mark==-2: folder='%s\\--'%dic[j] os.makedirs(folder,exist_ok=True) os.chdir(folder) plt.savefig('%d%s.png'%(k,'--'),format='png',dpi=dpi) plt.close() x+=5 y+=5 k+=1 j+=1 if __name__=='__main__': main() '''疑问： sclaer:min\max做y的底或顶？那么要N何用？ '''
