Jacky Liu's Blog
STS 图形界面
---- 动态图形操作界面,不是静态图片,仍然是基于 matplotlib 的。图形界面是对 Vim 文字界面的补充。一个程序两个界面,这样功能就更齐全了。
---- matplotlib 作为一个 Python 组件,跟 Vim 是一个整体,共享全部内存数据。如果用 PyQT,虽然绘图速度快,可是必须另起一个进程,靠 IPC 传数据。全市场的数据不仅量大而且结构复杂,必须放在一起,靠 IPC 不可行。
---- 如果既想要速度又想一体化,只能用 C++ 和 Qt 去直接扩展 Vim 了,这个我是不会去碰的。我是交易者,不是程序员。
---- 技术上已经没有什么太想写的东西了。前一阵子看了些量化和机器学习方面的材料,初步的印象是,它们对市场数据抽象得有点厉害。就像均线对价格求平均值掩盖掉了一些有用的细节一样,市场数据一旦被采样规整化,一些重要的 feature 也丢失了。不过这方面的思路和方法很重要,以后要继续深入。
---- 现在手上有一些看上去还不错的模式/策略,但还没有完全量化,需要人的介入。以后要摸索出最能够贴近原意而又简洁的量化 feature,目前已经部分成功,自己感觉有些 feature 真的定义得特别好。继续努力 ~
初次接触 PyQT, 鸣谢 散漫 童鞋
---- 这是 散漫 童鞋昨天发给我的 PyQT4 演示脚本画出来的图片。他只发了绘图脚本却没发数据文件给我,所以我生造了一个数据文件。因为错拿日线数据当成日内数据作为输入,所以图里的内容看起来很无稽,但只要能把图画出来就可以了:
---- 发给我的脚本是跑在 windows 上面的,我在 ubuntu 下面改了一个 py2 版和一个 py3 版:
py2 版:
# -*- coding: utf-8 -*- #!/usr/bin/python import sys import random from PyQt4 import QtGui, QtCore,Qt class report_painter: '''绘制行情类''' def __init__(self,parent): #初始化 self.parent = parent self.paint = QtGui.QPainter() self.paint.begin(self.parent) #设置抗锯齿 #self.paint.setRenderHint(QtGui.QPainter.Antialiasing) #度量尺对象 self.metrics = self.paint.fontMetrics() #设置字体库 self.fonts = dict() self.fonts['default'] = QtGui.QFont('Serif', 9, QtGui.QFont.Light) self.fonts['yahei_14_bold']= QtGui.QFont('Serif',12,QtGui.QFont.Bold) self.fonts['yahei_14']= QtGui.QFont('Serif',12,QtGui.QFont.Light) self.setFont('default') #设置笔刷样式库 self.pens = dict() #红色 1px粗 1px点 2px距 线条 self.pens['red_1px_dashline'] = QtGui.QPen( QtCore.Qt.red, 1, QtCore.Qt.DashLine) self.pens['red_1px_dashline'].setDashPattern([1,2]) #红色 1px粗 实线条 self.pens['red'] = QtGui.QPen( QtCore.Qt.red, 1, QtCore.Qt.SolidLine) #红色 3px粗 实线条 self.pens['red_2px'] = QtGui.QPen( QtCore.Qt.red, 2, QtCore.Qt.SolidLine) #红色 2px粗 实线条 self.pens['red_3px'] = QtGui.QPen( QtCore.Qt.red, 3, QtCore.Qt.SolidLine) #黄色 1px粗 实线条 self.pens['yellow'] = QtGui.QPen( QtCore.Qt.yellow, 1, QtCore.Qt.SolidLine) #白色 1px粗 实线条 self.pens['white'] = QtGui.QPen( QtCore.Qt.white , 1, QtCore.Qt.SolidLine) #灰色 1px粗 实线条 self.pens['gray'] = QtGui.QPen( QtCore.Qt.gray, 1, QtCore.Qt.SolidLine) #绿色 1px粗 实线条 self.pens['green'] = QtGui.QPen( QtCore.Qt.green, 1, QtCore.Qt.SolidLine) #绿色 3px粗 实线条 self.pens['green_2px'] = QtGui.QPen( QtCore.Qt.green, 2, QtCore.Qt.SolidLine) #亮蓝 1px粗 1px点 2px距 线条 self.pens['cyan_1px_dashline'] = QtGui.QPen( QtCore.Qt.cyan, 1, QtCore.Qt.DashLine) self.pens['cyan_1px_dashline'].setDashPattern([3,2]) #获得窗口的长和宽 size = self.parent.size() self.w = size.width() self.h = size.height() #设置grid的上下左右补丁边距 self.grid_padding_left = 45 #左侧补丁边距 self.grid_padding_right = 245 #右侧补丁边距 self.grid_padding_top = 25 #顶部补丁边距 self.grid_padding_bottom = 17 #底部补丁边距 #开始绘制 self.start_paint() self.paint.end() #结束 '''绘制流程步骤''' def start_paint(self): self.PriceGridPaint() self.rightGridPaint() self.timelinePaint() self.topInfoPaint() self.rulerPaint() self.VolumeGridPaint() self.volumePaint() self.pricePaint() self.xyPaint() '''设置使用的字体''' def setFont(self,code='default'): self.paint.setFont(self.fonts[code]) '''设置使用的笔刷''' def setPen(self,code='default'): self.paint.setPen(self.pens[code]) '''绘制股价走势表格''' def PriceGridPaint(self): self.setPen('red') self.paint.setBrush(QtCore.Qt.NoBrush) sum_width = self.grid_padding_left+self.grid_padding_right sum_height = self.grid_padding_top+self.grid_padding_bottom grid_height = self.h-sum_height #画边框 self.paint.drawRect(self.grid_padding_left,self.grid_padding_top, self.w-sum_width,self.h-sum_height) #成交量和走势的分界线 self.paint.drawLine(self.grid_padding_left,grid_height*0.7+self.grid_padding_top, self.w-self.grid_padding_right,grid_height*0.7+self.grid_padding_top) #股票昨收中间线 self.paint.drawLine(self.grid_padding_left+1, grid_height*0.35+self.grid_padding_top, self.w-self.grid_padding_right ,grid_height*0.35+self.grid_padding_top) #其他线条 self.paint.drawLine(0,self.h-self.grid_padding_bottom,self.w-self.grid_padding_right+44,self.h-self.grid_padding_bottom) self.paint.drawLine(0,self.h-self.grid_padding_bottom+16,self.w,self.h-self.grid_padding_bottom+16) self.paint.drawLine(self.w-self.grid_padding_right,0, self.w-self.grid_padding_right,self.h-self.grid_padding_bottom+16) self.paint.drawLine(self.w-self.grid_padding_right+44,0, self.w-self.grid_padding_right+44,self.h-self.grid_padding_bottom+16) self.setPen('yellow') self.paint.drawText(self.w-self.grid_padding_right+5,self.h-self.grid_padding_bottom-4,QtCore.QString(u'成交量')) self.setPen('white') #右下角文字 self.paint.drawText(self.w-self.grid_padding_right+12,self.h-self.grid_padding_bottom+12,QtCore.QString(u'实时')) '''绘制成交量走势表格''' def VolumeGridPaint(self): sum_width = self.grid_padding_left + self.grid_padding_right sum_height = self.grid_padding_top + self.grid_padding_bottom grid_height = self.h-sum_height max_volume = self.parent.stk_data['max_vol'] px_h_radio = max_volume/(grid_height*0.3) self.setPen('red_1px_dashline') grid_num = 6 x = grid_num cnt = grid_height*0.3/grid_num for i in range(0,grid_num): self.setPen('red_1px_dashline') #计算坐标 y1 = self.grid_padding_top+(grid_height*0.7)+i*cnt x1 = self.grid_padding_left x2 = self.grid_padding_left+self.w-sum_width self.paint.drawLine(x1,y1,x2,y1) #画价位虚线 vol_int = int(cnt*x*px_h_radio) vol_str = str(vol_int) fw = self.metrics.width(vol_str) #获得文字宽度 fh = self.metrics.height()/2 #获得文字高度 self.setPen('yellow') self.paint.drawText(x2+40-fw,y1+fh,vol_str) #写入文字 self.setPen('white') self.paint.drawText(x1-2-self.metrics.width(str(x)),y1+fh,str(x)) #写入文字 x-=1 '''绘制左侧信息栏和盘口等内容''' def rightGridPaint(self): self.setPen('red') #绘制信息内容之间的分割线 _h = 0 _x = self.w-self.grid_padding_right+44 self.paint.drawLine(self.w-1,0,self.w-1,self.h-self.grid_padding_bottom+16) self.paint.drawLine(0,0,0,self.h-self.grid_padding_bottom+16) self.paint.drawLine(0,_h,self.w,_h) _h+=23 self.paint.drawLine(_x,_h,self.w,_h) _h+=24 self.paint.drawLine(_x,_h,self.w,_h) _h+=93 self.paint.drawLine(_x,_h,self.w,_h) _h+=20 self.paint.drawLine(_x,_h,self.w,_h) _h+=93 self.paint.drawLine(_x,_h,self.w,_h) _h+=123 self.paint.drawLine(_x,_h,self.w,_h) _h+=23 self.paint.drawLine(_x,_h,self.w,_h) #股票名称和代码 self.setFont('yahei_14_bold') self.setPen('yellow') name_str = QtCore.QString(u'%s %s'%(self.parent.stk_info['code'],self.parent.stk_info['name'])) self.paint.drawText(_x+35,18,name_str) #委比和委差 self.setFont('yahei_14') zx_str = QtCore.QString(u'最新') self.paint.drawText(_x+3 ,156,zx_str) self.setPen('gray') wb_str = QtCore.QString(u'委比') wc_str = QtCore.QString(u'委差') xs_str = QtCore.QString(u'现手') self.paint.drawText(_x+3 ,39,wb_str) self.paint.drawText(_x+100,39,wc_str) self.paint.drawText(_x+100,156,xs_str) fh = self.metrics.height() left_field_list = [u'涨跌',u'涨幅',u'振幅',u'总手',u'总额',u'换手',u'分笔'] i = 1 for field in left_field_list: field_str = QtCore.QString(field) self.paint.drawText(_x+3,253+(i*17),field_str) i+=1 right_field_list = [u'均价',u'前收',u'今开',u'最高',u'最低',u'量比',u'均量'] i = 1 for field in right_field_list: field_str = QtCore.QString(field) self.paint.drawText(_x+100,253+(i*17),field_str) i+=1 wp_str = QtCore.QString(u'外盘') np_str = QtCore.QString(u'内盘') self.paint.drawText(_x+3,395,wp_str) self.paint.drawText(_x+100,395,np_str) #卖①②③④⑤ i = 0 sell_queue = [u'卖⑤',u'卖④',u'卖③',u'卖②',u'卖①'] for sell in sell_queue: sell_str = QtCore.QString(sell) self.paint.drawText(_x+3,62+(i*18),sell_str) i+=1 #买①②③④⑤ buy_queue = [u'买①',u'买②',u'买③',u'买④',u'买⑤'] for buy in buy_queue: buy_str = QtCore.QString(buy) self.paint.drawText(_x+3,87+(i*18),buy_str) i+=1 self.setPen('red_2px') self.paint.drawLine(_x+1,377,_x+99,377) self.paint.drawLine(_x+1,46,_x+65,46) self.setPen('green_2px') self.paint.drawLine(_x+102,377,_x+199,377) self.paint.drawLine(_x+67,46,_x+199,46) self.setFont('default') '''绘制左右侧的价格刻度''' def rulerPaint(self): sum_width = self.grid_padding_left+self.grid_padding_right sum_height = self.grid_padding_top+self.grid_padding_bottom grid_height = self.h-sum_height high = self.parent.stk_data['high'] low = self.parent.stk_data['low'] lastclose = self.parent.stk_data['lastclose'] top = high-lastclose bottom = lastclose-low if top>bottom: padding = top else: padding = bottom limit_top = lastclose+padding limit_low = lastclose-padding px_h_radio = (grid_height*0.7)/((limit_top-limit_low)*100) self.setPen('red_1px_dashline') grid_num = 16 cnt = grid_height*0.7/grid_num for i in range(0,grid_num): self.setPen('red_1px_dashline') #计算坐标 y1 = self.grid_padding_top+i*cnt x1 = self.grid_padding_left x2 = self.grid_padding_left+self.w-sum_width self.paint.drawLine(x1,y1,x2,y1) #画价位虚线 price_float = (limit_top - ((i*cnt)/px_h_radio/100)) #计算价格 price = '%4.2f'%(price_float) #格式化价格成str fw = self.metrics.width(price) #获得文字宽度 fh = self.metrics.height()/2 #获得文字高度 radio_float = (price_float/lastclose-1)*100 #计算波动百分比 radio_str = "%2.2f%%"%(radio_float) #格式化百分比成str r_fw = self.metrics.width(radio_str) r_fh = self.metrics.height()/2 #判断文字使用的颜色 if price_float == lastclose: self.setPen('white') if price_float < lastclose: self.setPen('green') self.paint.drawText(x1-fw-2,y1+fh,price) #写入文字 self.paint.drawText(x2+40-r_fw,y1+r_fh,radio_str) #写入文字 '''绘制x,y准星''' def xyPaint(self): if self.parent.m_x >= self.grid_padding_left and self.parent.m_x<=self.w-self.grid_padding_right and self.parent.m_y>=self.grid_padding_top and self.parent.m_y<=self.h-self.grid_padding_bottom: self.setPen('gray') x1 = self.grid_padding_left x2 = self.w-self.grid_padding_right y1 = self.grid_padding_top y2 = self.h-self.grid_padding_bottom self.paint.drawLine(x1+1,self.parent.m_y,x2-1,self.parent.m_y) self.paint.drawLine(self.parent.m_x,y1+1,self.parent.m_x,y2-1) '''绘制时间轴刻度''' def timelinePaint(self): fw = self.metrics.width(u'00:00') #计算文字的宽度 sum_width = self.grid_padding_left+self.grid_padding_right sum_height = self.grid_padding_top+self.grid_padding_bottom grid_width = self.w-sum_width-2 y1 = self.grid_padding_top y2 = y1+(self.h-sum_height) #时间轴中线 self.setPen('red') x_pos = grid_width/2+self.grid_padding_left self.paint.drawLine(x_pos,y1,x_pos,y2) self.paint.drawText(x_pos-fw/2,y2+12,QtCore.QString(u'13:00')) #时间轴09点30分 x_pos = self.grid_padding_left self.paint.drawText(x_pos,y2+12,QtCore.QString(u'09:30')) #时间轴10点30分 x_pos = grid_width*0.25+self.grid_padding_left self.paint.drawLine(x_pos,y1,x_pos,y2) self.paint.drawText(x_pos-fw/2,y2+12,QtCore.QString(u'10:30')) #时间轴14点00分 x_pos = grid_width*0.75+self.grid_padding_left self.paint.drawLine(x_pos,y1,x_pos,y2) self.paint.drawText(x_pos-fw/2,y2+12,QtCore.QString(u'14:00')) #时间轴15点00分 x_pos = grid_width+self.grid_padding_left self.paint.drawText(x_pos-fw,y2+12,QtCore.QString(u'15:00')) #时间虚线 by 30min self.setPen('red_1px_dashline') x_pos_array = [0.125,0.375,0.625,0.875] for i in x_pos_array: x_pos = grid_width*i+self.grid_padding_left self.paint.drawLine(x_pos,y1,x_pos,y2) '''绘制表格上方的股票信息''' def topInfoPaint(self): self.setPen('yellow') self.paint.drawText(4+self.grid_padding_left,self.grid_padding_top-4 ,QtCore.QString(self.parent.stk_info['name'])) #股票名称 self.paint.drawText(4+self.grid_padding_left+120,self.grid_padding_top-4 ,QtCore.QString(u'均价线:')) #均价线 lastclose = self.parent.stk_data['lastclose'] close = self.parent.stk_data['close'] mma = self.parent.stk_data['list']['mma'][-1] if lastclose>close: self.setPen('green') str_1 = '%.2f -%.2f'%(close,lastclose-close) if lastclose==close: self.setPen('white') str_1 = '%.2f +%.2f'%(close,0.00) if lastclose<close: self.setPen('red') str_1 = '%.2f +%.2f'%(close,close-lastclose) if mma>close: self.setPen('green') if mma==close: self.setPen('white') if mma<close: self.setPen('red') self.paint.drawText(4+self.grid_padding_left+55,self.grid_padding_top-4,QtCore.QString(str_1)) self.paint.drawText(4+self.grid_padding_left+165,self.grid_padding_top-4,QtCore.QString('%.2f'%mma)) #均价 #涨停价 self.setPen('red') self.paint.drawText(4+self.grid_padding_left+200,self.grid_padding_top-4,QtCore.QString(u'涨停价:%.2f'%(lastclose*1.1))) #均价 #跌停价 self.setPen('green') self.paint.drawText(4+self.grid_padding_left+280,self.grid_padding_top-4,QtCore.QString(u'跌停价:%.2f'%(lastclose*0.9))) #均价 '''绘制股价走势''' def pricePaint(self): sum_width = self.grid_padding_left+self.grid_padding_right sum_height = self.grid_padding_top+self.grid_padding_bottom grid_height = self.h-sum_height-2 high = self.parent.stk_data['high'] low = self.parent.stk_data['low'] lastclose = self.parent.stk_data['lastclose'] top = high-lastclose bottom = lastclose-low if top>bottom: padding = top else: padding = bottom limit_top = lastclose+padding limit_low = lastclose-padding h_radio = (grid_height*0.7)/((limit_top-limit_low)*100) w_radio = (self.w-sum_width-2)/240.00 w = self.grid_padding_left self.setPen('white') path = QtGui.QPainterPath() path.moveTo(w,(limit_top-self.parent.stk_data['open'])*100*h_radio+self.grid_padding_top) i = 1 for price in self.parent.stk_data['list']['close']: w = i*w_radio+self.grid_padding_left y = (limit_top-price)*100*h_radio+self.grid_padding_top path.lineTo(w,y) i+=1 self.paint.drawPath(path) self.setPen('cyan_1px_dashline') self.paint.drawLine(self.grid_padding_left+1,y,w-1,y) self.setPen('yellow') path = QtGui.QPainterPath() w = self.grid_padding_left path.moveTo(w,(limit_top-self.parent.stk_data['open'])*100*h_radio+self.grid_padding_top) i = 1 for price in self.parent.stk_data['list']['mma']: w = i*w_radio+self.grid_padding_left y = (limit_top-price)*100*h_radio+self.grid_padding_top path.lineTo(w,y) i+=1 self.paint.drawPath(path) '''绘制成交量''' def volumePaint(self): sum_width = self.grid_padding_left + self.grid_padding_right sum_height = self.grid_padding_top + self.grid_padding_bottom max_volume = self.parent.stk_data['max_vol'] #最大分钟成交量 w_radio = (self.w-sum_width-2)/240.00 h_radio = ((self.h-sum_height-2)*0.3)/max_volume y = (self.h-sum_height)+self.grid_padding_top self.setPen('yellow') for i in range(1,len(self.parent.stk_data['list']['vol'])+1): x = i*w_radio+self.grid_padding_left y2 = h_radio*self.parent.stk_data['list']['vol'][i-1] self.paint.drawLine(x,y-1,x,y-y2) class Test(QtGui.QWidget): def __init__(self, parent=None): QtGui.QWidget.__init__(self, parent) self.setMinimumSize(640, 430) #设置窗口最小尺寸 self.setGeometry(300, 300, 960, 650) self.setWindowTitle(QtCore.QString(u'超级狙击手[内部开发测试版]-行情实时走势')) self.setStyleSheet("QWidget { background-color: black }") self.setWindowIcon(QtGui.QIcon('ruby.png')) self.setMouseTracking(True) self.m_x = 0 #光标x轴位置 self.m_y = 0 #光标y轴位置 self.stk_info = {} self.stk_info['name'] = u'浙江东方' self.stk_info['code'] = u'600120' self.stk_info['market'] = 'SH' self.stk_data = {} self.stk_data['list'] = {} #股价序列 self.stk_data['list']['time'] = [] #时间 self.stk_data['list']['open'] = [] #开盘价 self.stk_data['list']['high'] = [] #最高价 self.stk_data['list']['low'] = [] #最低价 self.stk_data['list']['close'] = [] #收盘价 self.stk_data['list']['vol'] = [] #成交量 self.stk_data['list']['amount']= [] #成交额 self.stk_data['list']['mma']= [] #分时均价 self.stk_data['list']['buy_port'] = [(0.00,0),(0.00,0),(0.00,0),(0.00,0),(0.00,0)] #买盘前五 self.stk_data['list']['sell_port'] = [(0.00,0),(0.00,0),(0.00,0),(0.00,0),(0.00,0)] #卖盘前五 #读取数据 f = open('SH600120.txt','r') data = f.readlines() f.close() for row in data: vars = row.split(' ') self.stk_data['list']['time'].append(vars[1]) self.stk_data['list']['open'].append(float(vars[2])) self.stk_data['list']['high'].append(float(vars[3])) self.stk_data['list']['low'].append(float(vars[4])) self.stk_data['list']['close'].append(float(vars[5])) self.stk_data['list']['vol'].append(int(float(vars[6]))) self.stk_data['list']['amount'].append(int(float(vars[7]))) sum_vol = sum(self.stk_data['list']['vol']) sum_amt = sum(self.stk_data['list']['amount']) self.stk_data['list']['mma'].append(float(sum_amt)/(sum_vol*100.00)) self.stk_data['lastclose'] = 10.12 #上一个交易日收盘价 self.stk_data['open'] = self.stk_data['list']['open'][0] #开盘价 self.stk_data['high'] = max(self.stk_data['list']['high']) #最高价 self.stk_data['low'] = min(self.stk_data['list']['low']) #最低价 self.stk_data['close']= self.stk_data['list']['close'][-1] #收盘价 self.stk_data['max_vol'] = max(self.stk_data['list']['vol']) #当日最高成交量 def mouseMoveEvent(self, event): self.m_x = int(event.x()) self.m_y = int(event.y()) self.repaint() def paintEvent(self, event): report_painter(self) app = QtGui.QApplication(sys.argv) dt = Test() dt.show() app.exec_()
py3 版:
# -*- coding: utf-8 -*- #!/usr/bin/python import sys import random from PyQt4 import QtGui, QtCore,Qt class report_painter: '''绘制行情类''' def __init__(self,parent): #初始化 self.parent = parent self.paint = QtGui.QPainter() self.paint.begin(self.parent) #设置抗锯齿 #self.paint.setRenderHint(QtGui.QPainter.Antialiasing) #度量尺对象 self.metrics = self.paint.fontMetrics() #设置字体库 self.fonts = dict() self.fonts['default'] = QtGui.QFont('Serif', 9, QtGui.QFont.Light) self.fonts['yahei_14_bold']= QtGui.QFont('Serif',12,QtGui.QFont.Bold) self.fonts['yahei_14']= QtGui.QFont('Serif',12,QtGui.QFont.Light) self.setFont('default') #设置笔刷样式库 self.pens = dict() #红色 1px粗 1px点 2px距 线条 self.pens['red_1px_dashline'] = QtGui.QPen( QtCore.Qt.red, 1, QtCore.Qt.DashLine) self.pens['red_1px_dashline'].setDashPattern([1,2]) #红色 1px粗 实线条 self.pens['red'] = QtGui.QPen( QtCore.Qt.red, 1, QtCore.Qt.SolidLine) #红色 3px粗 实线条 self.pens['red_2px'] = QtGui.QPen( QtCore.Qt.red, 2, QtCore.Qt.SolidLine) #红色 2px粗 实线条 self.pens['red_3px'] = QtGui.QPen( QtCore.Qt.red, 3, QtCore.Qt.SolidLine) #黄色 1px粗 实线条 self.pens['yellow'] = QtGui.QPen( QtCore.Qt.yellow, 1, QtCore.Qt.SolidLine) #白色 1px粗 实线条 self.pens['white'] = QtGui.QPen( QtCore.Qt.white , 1, QtCore.Qt.SolidLine) #灰色 1px粗 实线条 self.pens['gray'] = QtGui.QPen( QtCore.Qt.gray, 1, QtCore.Qt.SolidLine) #绿色 1px粗 实线条 self.pens['green'] = QtGui.QPen( QtCore.Qt.green, 1, QtCore.Qt.SolidLine) #绿色 3px粗 实线条 self.pens['green_2px'] = QtGui.QPen( QtCore.Qt.green, 2, QtCore.Qt.SolidLine) #亮蓝 1px粗 1px点 2px距 线条 self.pens['cyan_1px_dashline'] = QtGui.QPen( QtCore.Qt.cyan, 1, QtCore.Qt.DashLine) self.pens['cyan_1px_dashline'].setDashPattern([3,2]) #获得窗口的长和宽 size = self.parent.size() self.w = size.width() self.h = size.height() #设置grid的上下左右补丁边距 self.grid_padding_left = 45 #左侧补丁边距 self.grid_padding_right = 245 #右侧补丁边距 self.grid_padding_top = 25 #顶部补丁边距 self.grid_padding_bottom = 17 #底部补丁边距 #开始绘制 self.start_paint() self.paint.end() #结束 '''绘制流程步骤''' def start_paint(self): self.PriceGridPaint() self.rightGridPaint() self.timelinePaint() self.topInfoPaint() self.rulerPaint() self.VolumeGridPaint() self.volumePaint() self.pricePaint() self.xyPaint() '''设置使用的字体''' def setFont(self,code='default'): self.paint.setFont(self.fonts[code]) '''设置使用的笔刷''' def setPen(self,code='default'): self.paint.setPen(self.pens[code]) '''绘制股价走势表格''' def PriceGridPaint(self): self.setPen('red') self.paint.setBrush(QtCore.Qt.NoBrush) sum_width = self.grid_padding_left+self.grid_padding_right sum_height = self.grid_padding_top+self.grid_padding_bottom grid_height = self.h-sum_height #画边框 self.paint.drawRect(self.grid_padding_left,self.grid_padding_top, self.w-sum_width,self.h-sum_height) #成交量和走势的分界线 self.paint.drawLine(self.grid_padding_left,grid_height*0.7+self.grid_padding_top, self.w-self.grid_padding_right,grid_height*0.7+self.grid_padding_top) #股票昨收中间线 self.paint.drawLine(self.grid_padding_left+1, grid_height*0.35+self.grid_padding_top, self.w-self.grid_padding_right ,grid_height*0.35+self.grid_padding_top) #其他线条 self.paint.drawLine(0,self.h-self.grid_padding_bottom,self.w-self.grid_padding_right+44,self.h-self.grid_padding_bottom) self.paint.drawLine(0,self.h-self.grid_padding_bottom+16,self.w,self.h-self.grid_padding_bottom+16) self.paint.drawLine(self.w-self.grid_padding_right,0, self.w-self.grid_padding_right,self.h-self.grid_padding_bottom+16) self.paint.drawLine(self.w-self.grid_padding_right+44,0, self.w-self.grid_padding_right+44,self.h-self.grid_padding_bottom+16) self.setPen('yellow') self.paint.drawText(self.w-self.grid_padding_right+5,self.h-self.grid_padding_bottom-4,str('成交量')) self.setPen('white') #右下角文字 self.paint.drawText(self.w-self.grid_padding_right+12,self.h-self.grid_padding_bottom+12,str('实时')) '''绘制成交量走势表格''' def VolumeGridPaint(self): sum_width = self.grid_padding_left + self.grid_padding_right sum_height = self.grid_padding_top + self.grid_padding_bottom grid_height = self.h-sum_height max_volume = self.parent.stk_data['max_vol'] px_h_radio = max_volume/(grid_height*0.3) self.setPen('red_1px_dashline') grid_num = 6 x = grid_num cnt = grid_height*0.3/grid_num for i in range(0,grid_num): self.setPen('red_1px_dashline') #计算坐标 y1 = self.grid_padding_top+(grid_height*0.7)+i*cnt x1 = self.grid_padding_left x2 = self.grid_padding_left+self.w-sum_width self.paint.drawLine(x1,y1,x2,y1) #画价位虚线 vol_int = int(cnt*x*px_h_radio) vol_str = str(vol_int) fw = self.metrics.width(vol_str) #获得文字宽度 fh = self.metrics.height()/2 #获得文字高度 self.setPen('yellow') self.paint.drawText(x2+40-fw,y1+fh,vol_str) #写入文字 self.setPen('white') self.paint.drawText(x1-2-self.metrics.width(str(x)),y1+fh,str(x)) #写入文字 x-=1 '''绘制左侧信息栏和盘口等内容''' def rightGridPaint(self): self.setPen('red') #绘制信息内容之间的分割线 _h = 0 _x = self.w-self.grid_padding_right+44 self.paint.drawLine(self.w-1,0,self.w-1,self.h-self.grid_padding_bottom+16) self.paint.drawLine(0,0,0,self.h-self.grid_padding_bottom+16) self.paint.drawLine(0,_h,self.w,_h) _h+=23 self.paint.drawLine(_x,_h,self.w,_h) _h+=24 self.paint.drawLine(_x,_h,self.w,_h) _h+=93 self.paint.drawLine(_x,_h,self.w,_h) _h+=20 self.paint.drawLine(_x,_h,self.w,_h) _h+=93 self.paint.drawLine(_x,_h,self.w,_h) _h+=123 self.paint.drawLine(_x,_h,self.w,_h) _h+=23 self.paint.drawLine(_x,_h,self.w,_h) #股票名称和代码 self.setFont('yahei_14_bold') self.setPen('yellow') name_str = str('%s %s'%(self.parent.stk_info['code'],self.parent.stk_info['name'])) self.paint.drawText(_x+35,18,name_str) #委比和委差 self.setFont('yahei_14') zx_str = str('最新') self.paint.drawText(_x+3 ,156,zx_str) self.setPen('gray') wb_str = str('委比') wc_str = str('委差') xs_str = str('现手') self.paint.drawText(_x+3 ,39,wb_str) self.paint.drawText(_x+100,39,wc_str) self.paint.drawText(_x+100,156,xs_str) fh = self.metrics.height() left_field_list = ['涨跌','涨幅','振幅','总手','总额','换手','分笔'] i = 1 for field in left_field_list: field_str = str(field) self.paint.drawText(_x+3,253+(i*17),field_str) i+=1 right_field_list = ['均价','前收','今开','最高','最低','量比','均量'] i = 1 for field in right_field_list: field_str = str(field) self.paint.drawText(_x+100,253+(i*17),field_str) i+=1 wp_str = str('外盘') np_str = str('内盘') self.paint.drawText(_x+3,395,wp_str) self.paint.drawText(_x+100,395,np_str) #卖①②③④⑤ i = 0 sell_queue = ['卖⑤','卖④','卖③','卖②','卖①'] for sell in sell_queue: sell_str = str(sell) self.paint.drawText(_x+3,62+(i*18),sell_str) i+=1 #买①②③④⑤ buy_queue = ['买①','买②','买③','买④','买⑤'] for buy in buy_queue: buy_str = str(buy) self.paint.drawText(_x+3,87+(i*18),buy_str) i+=1 self.setPen('red_2px') self.paint.drawLine(_x+1,377,_x+99,377) self.paint.drawLine(_x+1,46,_x+65,46) self.setPen('green_2px') self.paint.drawLine(_x+102,377,_x+199,377) self.paint.drawLine(_x+67,46,_x+199,46) self.setFont('default') '''绘制左右侧的价格刻度''' def rulerPaint(self): sum_width = self.grid_padding_left+self.grid_padding_right sum_height = self.grid_padding_top+self.grid_padding_bottom grid_height = self.h-sum_height high = self.parent.stk_data['high'] low = self.parent.stk_data['low'] lastclose = self.parent.stk_data['lastclose'] top = high-lastclose bottom = lastclose-low if top>bottom: padding = top else: padding = bottom limit_top = lastclose+padding limit_low = lastclose-padding px_h_radio = (grid_height*0.7)/((limit_top-limit_low)*100) self.setPen('red_1px_dashline') grid_num = 16 cnt = grid_height*0.7/grid_num for i in range(0,grid_num): self.setPen('red_1px_dashline') #计算坐标 y1 = self.grid_padding_top+i*cnt x1 = self.grid_padding_left x2 = self.grid_padding_left+self.w-sum_width self.paint.drawLine(x1,y1,x2,y1) #画价位虚线 price_float = (limit_top - ((i*cnt)/px_h_radio/100)) #计算价格 price = '%4.2f'%(price_float) #格式化价格成str fw = self.metrics.width(price) #获得文字宽度 fh = self.metrics.height()/2 #获得文字高度 radio_float = (price_float/lastclose-1)*100 #计算波动百分比 radio_str = "%2.2f%%"%(radio_float) #格式化百分比成str r_fw = self.metrics.width(radio_str) r_fh = self.metrics.height()/2 #判断文字使用的颜色 if price_float == lastclose: self.setPen('white') if price_float < lastclose: self.setPen('green') self.paint.drawText(x1-fw-2,y1+fh,price) #写入文字 self.paint.drawText(x2+40-r_fw,y1+r_fh,radio_str) #写入文字 '''绘制x,y准星''' def xyPaint(self): if self.parent.m_x >= self.grid_padding_left and self.parent.m_x<=self.w-self.grid_padding_right and self.parent.m_y>=self.grid_padding_top and self.parent.m_y<=self.h-self.grid_padding_bottom: self.setPen('gray') x1 = self.grid_padding_left x2 = self.w-self.grid_padding_right y1 = self.grid_padding_top y2 = self.h-self.grid_padding_bottom self.paint.drawLine(x1+1,self.parent.m_y,x2-1,self.parent.m_y) self.paint.drawLine(self.parent.m_x,y1+1,self.parent.m_x,y2-1) '''绘制时间轴刻度''' def timelinePaint(self): fw = self.metrics.width('00:00') #计算文字的宽度 sum_width = self.grid_padding_left+self.grid_padding_right sum_height = self.grid_padding_top+self.grid_padding_bottom grid_width = self.w-sum_width-2 y1 = self.grid_padding_top y2 = y1+(self.h-sum_height) #时间轴中线 self.setPen('red') x_pos = grid_width/2+self.grid_padding_left self.paint.drawLine(x_pos,y1,x_pos,y2) self.paint.drawText(x_pos-fw/2,y2+12,str('13:00')) #时间轴09点30分 x_pos = self.grid_padding_left self.paint.drawText(x_pos,y2+12,str('09:30')) #时间轴10点30分 x_pos = grid_width*0.25+self.grid_padding_left self.paint.drawLine(x_pos,y1,x_pos,y2) self.paint.drawText(x_pos-fw/2,y2+12,str('10:30')) #时间轴14点00分 x_pos = grid_width*0.75+self.grid_padding_left self.paint.drawLine(x_pos,y1,x_pos,y2) self.paint.drawText(x_pos-fw/2,y2+12,str('14:00')) #时间轴15点00分 x_pos = grid_width+self.grid_padding_left self.paint.drawText(x_pos-fw,y2+12,str('15:00')) #时间虚线 by 30min self.setPen('red_1px_dashline') x_pos_array = [0.125,0.375,0.625,0.875] for i in x_pos_array: x_pos = grid_width*i+self.grid_padding_left self.paint.drawLine(x_pos,y1,x_pos,y2) '''绘制表格上方的股票信息''' def topInfoPaint(self): self.setPen('yellow') self.paint.drawText(4+self.grid_padding_left,self.grid_padding_top-4 ,str(self.parent.stk_info['name'])) #股票名称 self.paint.drawText(4+self.grid_padding_left+120,self.grid_padding_top-4 ,str('均价线:')) #均价线 lastclose = self.parent.stk_data['lastclose'] close = self.parent.stk_data['close'] mma = self.parent.stk_data['list']['mma'][-1] if lastclose>close: self.setPen('green') str_1 = '%.2f -%.2f'%(close,lastclose-close) if lastclose==close: self.setPen('white') str_1 = '%.2f +%.2f'%(close,0.00) if lastclose<close: self.setPen('red') str_1 = '%.2f +%.2f'%(close,close-lastclose) if mma>close: self.setPen('green') if mma==close: self.setPen('white') if mma<close: self.setPen('red') self.paint.drawText(4+self.grid_padding_left+55,self.grid_padding_top-4,str(str_1)) self.paint.drawText(4+self.grid_padding_left+165,self.grid_padding_top-4,str('%.2f'%mma)) #均价 #涨停价 self.setPen('red') self.paint.drawText(4+self.grid_padding_left+200,self.grid_padding_top-4,str('涨停价:%.2f'%(lastclose*1.1))) #均价 #跌停价 self.setPen('green') self.paint.drawText(4+self.grid_padding_left+280,self.grid_padding_top-4,str('跌停价:%.2f'%(lastclose*0.9))) #均价 '''绘制股价走势''' def pricePaint(self): sum_width = self.grid_padding_left+self.grid_padding_right sum_height = self.grid_padding_top+self.grid_padding_bottom grid_height = self.h-sum_height-2 high = self.parent.stk_data['high'] low = self.parent.stk_data['low'] lastclose = self.parent.stk_data['lastclose'] top = high-lastclose bottom = lastclose-low if top>bottom: padding = top else: padding = bottom limit_top = lastclose+padding limit_low = lastclose-padding h_radio = (grid_height*0.7)/((limit_top-limit_low)*100) w_radio = (self.w-sum_width-2)/240.00 w = self.grid_padding_left self.setPen('white') path = QtGui.QPainterPath() path.moveTo(w,(limit_top-self.parent.stk_data['open'])*100*h_radio+self.grid_padding_top) i = 1 for price in self.parent.stk_data['list']['close']: w = i*w_radio+self.grid_padding_left y = (limit_top-price)*100*h_radio+self.grid_padding_top path.lineTo(w,y) i+=1 self.paint.drawPath(path) self.setPen('cyan_1px_dashline') self.paint.drawLine(self.grid_padding_left+1,y,w-1,y) self.setPen('yellow') path = QtGui.QPainterPath() w = self.grid_padding_left path.moveTo(w,(limit_top-self.parent.stk_data['open'])*100*h_radio+self.grid_padding_top) i = 1 for price in self.parent.stk_data['list']['mma']: w = i*w_radio+self.grid_padding_left y = (limit_top-price)*100*h_radio+self.grid_padding_top path.lineTo(w,y) i+=1 self.paint.drawPath(path) '''绘制成交量''' def volumePaint(self): sum_width = self.grid_padding_left + self.grid_padding_right sum_height = self.grid_padding_top + self.grid_padding_bottom max_volume = self.parent.stk_data['max_vol'] #最大分钟成交量 w_radio = (self.w-sum_width-2)/240.00 h_radio = ((self.h-sum_height-2)*0.3)/max_volume y = (self.h-sum_height)+self.grid_padding_top self.setPen('yellow') for i in range(1,len(self.parent.stk_data['list']['vol'])+1): x = i*w_radio+self.grid_padding_left y2 = h_radio*self.parent.stk_data['list']['vol'][i-1] self.paint.drawLine(x,y-1,x,y-y2) class Test(QtGui.QWidget): def __init__(self, parent=None): QtGui.QWidget.__init__(self, parent) self.setMinimumSize(640, 430) #设置窗口最小尺寸 self.setGeometry(300, 300, 960, 650) self.setWindowTitle(str('超级狙击手[内部开发测试版]-行情实时走势')) self.setStyleSheet("QWidget { background-color: black }") self.setWindowIcon(QtGui.QIcon('ruby.png')) self.setMouseTracking(True) self.m_x = 0 #光标x轴位置 self.m_y = 0 #光标y轴位置 self.stk_info = {} self.stk_info['name'] = '浙江东方' self.stk_info['code'] = '600120' self.stk_info['market'] = 'SH' self.stk_data = {} self.stk_data['list'] = {} #股价序列 self.stk_data['list']['time'] = [] #时间 self.stk_data['list']['open'] = [] #开盘价 self.stk_data['list']['high'] = [] #最高价 self.stk_data['list']['low'] = [] #最低价 self.stk_data['list']['close'] = [] #收盘价 self.stk_data['list']['vol'] = [] #成交量 self.stk_data['list']['amount']= [] #成交额 self.stk_data['list']['mma']= [] #分时均价 self.stk_data['list']['buy_port'] = [(0.00,0),(0.00,0),(0.00,0),(0.00,0),(0.00,0)] #买盘前五 self.stk_data['list']['sell_port'] = [(0.00,0),(0.00,0),(0.00,0),(0.00,0),(0.00,0)] #卖盘前五 #读取数据 f = open('SH600120.txt','r') data = f.readlines() f.close() for row in data: vars = row.split(' ') self.stk_data['list']['time'].append(vars[1]) self.stk_data['list']['open'].append(float(vars[2])) self.stk_data['list']['high'].append(float(vars[3])) self.stk_data['list']['low'].append(float(vars[4])) self.stk_data['list']['close'].append(float(vars[5])) self.stk_data['list']['vol'].append(int(float(vars[6]))) self.stk_data['list']['amount'].append(int(float(vars[7]))) sum_vol = sum(self.stk_data['list']['vol']) sum_amt = sum(self.stk_data['list']['amount']) self.stk_data['list']['mma'].append(float(sum_amt)/(sum_vol*100.00)) self.stk_data['lastclose'] = 10.12 #上一个交易日收盘价 self.stk_data['open'] = self.stk_data['list']['open'][0] #开盘价 self.stk_data['high'] = max(self.stk_data['list']['high']) #最高价 self.stk_data['low'] = min(self.stk_data['list']['low']) #最低价 self.stk_data['close']= self.stk_data['list']['close'][-1] #收盘价 self.stk_data['max_vol'] = max(self.stk_data['list']['vol']) #当日最高成交量 def mouseMoveEvent(self, event): self.m_x = int(event.x()) self.m_y = int(event.y()) self.repaint() def paintEvent(self, event): report_painter(self) app = QtGui.QApplication(sys.argv) dt = Test() dt.show() app.exec_()
还有这个是生造的数据文件:
600120 2011-07-01 8.430 8.480 8.340 8.360 3149769 26493056 600120 2011-07-04 8.410 8.520 8.340 8.510 4516001 38210836 600120 2011-07-05 8.540 8.560 8.410 8.480 7777481 65878192 600120 2011-07-06 8.490 8.490 8.300 8.420 5242033 43893128 600120 2011-07-07 8.440 8.460 8.300 8.400 6127618 51328900 600120 2011-07-08 8.350 8.650 8.310 8.600 9963714 84713360 600120 2011-07-11 8.560 8.740 8.520 8.580 10380010 89564168 600120 2011-07-12 8.500 8.560 8.380 8.410 5574160 46996364 600120 2011-07-13 8.400 8.520 8.390 8.470 4701829 39824976 600120 2011-07-14 8.500 8.690 8.460 8.630 7504610 64556320 600120 2011-07-15 8.630 8.660 8.530 8.620 5705629 48998892 600120 2011-07-18 8.610 8.730 8.560 8.630 5320452 46000688 600120 2011-07-19 8.580 8.600 8.420 8.450 4248058 36036192 600120 2011-07-20 8.510 8.550 8.340 8.420 4750361 40079624 600120 2011-07-21 8.420 8.460 8.320 8.330 3736405 31312912 600120 2011-07-22 8.320 8.390 8.270 8.310 3932585 32756464 600120 2011-07-25 8.290 8.290 7.830 7.850 6353668 51000860 600120 2011-07-26 7.880 8.010 7.860 7.970 3276690 25987310 600120 2011-07-27 8.010 8.330 8.010 8.230 6893659 56675756 600120 2011-07-28 8.140 8.200 7.900 8.080 4816761 38835260 600120 2011-07-29 8.100 8.110 7.900 7.960 3186438 25420588 600120 2011-08-01 7.990 8.070 7.980 8.020 2529614 20283316 600120 2011-08-02 8.000 8.000 7.660 7.740 8525883 66058452 600120 2011-08-03 7.610 7.790 7.590 7.750 3920131 30255948 600120 2011-08-04 7.760 7.900 7.710 7.870 4260037 33309532 600120 2011-08-05 7.550 7.670 7.540 7.600 4065619 31001802 600120 2011-08-08 7.550 7.590 6.840 7.010 5930435 42265528 600120 2011-08-09 6.780 6.900 6.400 6.850 6868576 45893344 600120 2011-08-10 7.000 7.060 6.900 6.950 4487516 31282836 600120 2011-08-11 6.720 7.350 6.690 7.300 6402779 44976020 600120 2011-08-12 7.260 7.820 7.250 7.810 15766826 120297096 600120 2011-08-15 7.810 8.080 7.700 7.850 12323882 97571784 600120 2011-08-16 7.810 8.230 7.800 8.130 10745946 86445416 600120 2011-08-17 8.100 8.380 8.030 8.370 11671606 95696840 600120 2011-08-18 8.360 8.590 8.150 8.190 11869045 98933000 600120 2011-08-19 8.100 8.290 7.870 8.260 6530784 52682404 600120 2011-08-22 8.240 8.510 8.130 8.210 7418123 62068352 600120 2011-08-23 8.300 8.300 7.930 8.080 6113382 49369428 600120 2011-08-24 8.080 8.170 7.800 7.950 6902403 54820380 600120 2011-08-25 7.990 8.000 7.800 7.950 8225919 65140832 600120 2011-08-26 7.980 8.580 7.870 8.390 14188172 117105688 600120 2011-08-29 8.280 8.600 8.200 8.420 12552920 105444392 600120 2011-08-30 8.470 8.650 8.290 8.310 9278734 78914264 600120 2011-08-31 8.390 8.600 8.260 8.470 6950031 58692324 600120 2011-09-01 8.460 8.530 8.120 8.150 6783869 56098000 600120 2011-09-02 8.130 8.150 7.800 7.930 5169247 41035356 600120 2011-09-05 7.900 7.900 7.610 7.770 3367080 25931720 600120 2011-09-06 7.650 7.840 7.630 7.780 1994604 15473853 600120 2011-09-07 7.800 7.920 7.710 7.890 4524852 35365940 600120 2011-09-08 8.000 8.000 7.750 7.780 3260041 25597960 600120 2011-09-09 7.790 7.850 7.690 7.720 1670190 12958421 600120 2011-09-13 7.610 7.610 7.220 7.350 2993141 22118428 600120 2011-09-14 7.410 7.490 7.280 7.460 1601320 11858549 600120 2011-09-15 7.460 7.540 7.380 7.450 1813248 13502755 600120 2011-09-16 7.490 7.570 7.380 7.500 1562320 11709203 600120 2011-09-19 7.380 7.490 7.350 7.350 1256729 9314678 600120 2011-09-20 7.390 7.420 7.070 7.160 4164802 30099376 600120 2011-09-21 7.160 7.350 7.030 7.310 4105156 29545046 600120 2011-09-22 7.240 7.420 7.180 7.190 2778893 20283590 600120 2011-09-23 6.990 7.260 6.930 7.080 3005942 21362102 600120 2011-09-26 7.140 7.280 7.000 7.030 2440617 17473004 600120 2011-09-27 7.100 7.140 6.970 7.050 1876199 13234751 600120 2011-09-28 7.110 7.130 6.850 6.850 1999641 13997734 600120 2011-09-29 6.850 6.850 6.620 6.680 2083164 14028082 600120 2011-09-30 6.800 6.850 6.660 6.810 1446665 9784671 600120 2011-10-10 6.800 7.490 6.800 7.340 6157517 44781964 600120 2011-10-11 7.410 7.550 7.180 7.330 5113073 37712548 600120 2011-10-12 7.330 7.850 7.200 7.650 7080724 53702240 600120 2011-10-13 7.650 7.700 7.480 7.570 3228726 24474174 600120 2011-10-14 7.520 7.830 7.510 7.610 5204745 40001128 600120 2011-10-17 7.530 7.640 7.420 7.470 3093406 23189326 600120 2011-10-18 7.470 7.470 7.240 7.250 2355012 17249810 600120 2011-10-19 7.300 7.350 7.150 7.250 2051058 14839307 600120 2011-10-20 7.200 7.260 6.950 7.040 2177500 15435905 600120 2011-10-21 7.050 7.050 6.850 6.920 1479998 10263274 600120 2011-10-24 6.920 7.050 6.820 7.040 2828763 19645222 600120 2011-10-25 7.090 7.150 6.900 7.110 5454243 38421580 600120 2011-10-26 7.080 7.250 6.980 7.090 7561318 53553768 600120 2011-10-27 7.090 7.110 6.990 7.020 4331955 30494264 600120 2011-10-28 7.080 7.160 7.050 7.130 5397767 38375040 600120 2011-10-31 7.130 7.230 7.120 7.230 4416981 31686934 600120 2011-11-01 7.190 7.240 7.080 7.090 6221598 44541308 600120 2011-11-02 6.970 7.090 6.790 7.070 6240368 43274104 600120 2011-11-03 7.060 7.250 7.060 7.210 7434887 53413296 600120 2011-11-04 7.190 7.310 7.160 7.260 5469346 39634672 600120 2011-11-07 7.230 7.330 7.170 7.280 4881876 35415712 600120 2011-11-08 7.280 7.300 7.030 7.090 5152319 36909572 600120 2011-11-09 7.090 7.140 6.990 7.120 4396667 31023112 600120 2011-11-10 7.010 7.060 6.950 6.980 2874227 20162434 600120 2011-11-11 6.980 7.040 6.940 6.980 1971345 13774011 600120 2011-11-14 7.020 7.220 7.000 7.200 5005408 35833604 600120 2011-11-15 7.170 7.260 7.160 7.220 3018996 21752406 600120 2011-11-16 7.220 7.250 7.000 7.020 3599804 25579700 600120 2011-11-17 7.020 7.090 6.980 7.030 1793379 12631356 600120 2011-11-18 7.000 7.100 6.910 6.930 3106221 21753164 600120 2011-11-21 6.910 6.970 6.800 6.910 1656054 11393588 600120 2011-11-22 6.850 6.900 6.800 6.870 1683639 11521032 600120 2011-11-23 6.870 6.910 6.760 6.790 1465450 10027905 600120 2011-11-24 6.720 6.790 6.650 6.700 1649095 11059988 600120 2011-11-25 6.710 6.750 6.660 6.710 1086150 7282331 600120 2011-11-28 6.750 6.820 6.720 6.800 1174999 7964600 600120 2011-11-29 6.910 6.920 6.780 6.860 1269253 8674397 600120 2011-11-30 6.810 6.860 6.520 6.550 2604110 17315016 600120 2011-12-01 6.690 6.750 6.630 6.680 2671312 17883724 600120 2011-12-02 6.680 6.680 6.450 6.510 1473269 9605086 600120 2011-12-05 6.480 6.480 6.110 6.200 1719220 10751089 600120 2011-12-06 6.170 6.240 6.140 6.210 1223995 7571687 600120 2011-12-07 6.220 6.260 6.180 6.200 961999 5985017 600120 2011-12-08 6.190 6.260 6.110 6.250 1161611 7200791 600120 2011-12-09 6.230 6.240 6.180 6.230 653267 4056483 600120 2011-12-12 6.200 6.240 6.130 6.230 652932 4044556 600120 2011-12-13 6.170 6.200 5.850 6.010 2992541 17886124 600120 2011-12-14 5.970 6.010 5.720 5.950 1722800 10140223 600120 2011-12-15 5.900 6.120 5.640 5.760 3439396 19840406 600120 2011-12-16 5.710 5.810 5.470 5.800 1940713 10971714 600120 2011-12-19 5.760 5.760 5.500 5.690 2164208 12149138 600120 2011-12-20 5.600 5.740 5.600 5.620 1237797 7036752 600120 2011-12-21 5.680 5.710 5.400 5.450 1137091 6378653 600120 2011-12-22 5.360 5.400 5.100 5.230 1565667 8225734 600120 2011-12-23 5.180 5.340 5.180 5.270 1033967 5447228 600120 2011-12-26 5.280 5.310 5.200 5.290 1060186 5591960 600120 2011-12-27 5.250 5.270 5.020 5.100 1373900 7103701 600120 2011-12-28 5.060 5.060 4.830 5.020 1516885 7513323 600120 2011-12-29 5.020 5.060 4.910 4.960 1074220 5366675 600120 2012-01-04 5.090 5.140 4.920 4.930 1904790 9586652 600120 2012-01-05 4.860 4.870 4.680 4.690 1409501 6735327 600120 2012-01-06 5.020 5.100 4.800 4.880 2230808 10954823 600120 2012-01-09 4.890 5.290 4.880 5.260 3338809 16978868 600120 2012-01-10 5.200 5.500 5.150 5.460 3648924 19501378 600120 2012-01-11 5.440 5.440 5.300 5.340 2861417 15362869 600120 2012-01-12 5.340 5.440 5.310 5.350 1763741 9495084 600120 2012-01-13 5.380 5.380 5.100 5.140 2438074 12696630 600120 2012-01-16 5.010 5.170 4.960 4.970 1145104 5807568 600120 2012-01-17 5.000 5.290 4.970 5.290 2698856 14019675 600120 2012-01-18 5.260 5.350 5.180 5.280 2634491 13893458 600120 2012-01-19 5.270 5.350 5.200 5.310 1816398 9624942 600120 2012-01-20 5.330 5.460 5.310 5.430 2573601 13906017 600120 2012-01-30 5.400 5.480 5.340 5.360 1832939 9883418 600120 2012-01-31 5.350 5.390 5.290 5.350 1242594 6630515 600120 2012-02-01 5.370 5.450 5.320 5.320 1841967 9919325 600120 2012-02-02 5.330 5.570 5.330 5.550 4581848 25035066 600120 2012-02-03 5.550 5.650 5.470 5.560 4893311 27319804 600120 2012-02-06 5.530 5.880 5.500 5.750 5100686 29265454 600120 2012-02-07 5.640 5.690 5.590 5.610 3227565 18154338 600120 2012-02-08 5.610 5.790 5.560 5.780 3591428 20524218 600120 2012-02-09 5.730 5.830 5.730 5.760 3408817 19736262 600120 2012-05-10 6.340 6.340 6.340 6.340 514959 3264840 600120 2012-05-14 6.970 6.970 6.970 6.970 931840 6494925 600120 2012-05-15 7.670 7.670 6.760 6.910 27411760 201302192 600120 2012-05-16 6.610 6.790 6.580 6.630 9972930 66460172 600120 2012-05-17 6.600 6.670 6.400 6.490 8248599 53690064 600120 2012-05-18 6.400 6.610 6.320 6.400 5609795 36256556 600120 2012-05-21 6.410 6.520 6.210 6.290 4537627 28691816 600120 2012-05-22 6.340 6.410 6.290 6.370 4063683 25810310 600120 2012-05-23 6.370 6.450 6.180 6.250 4498954 28159672 600120 2012-05-24 6.270 6.380 6.200 6.350 4195454 26358802 600120 2012-05-25 6.320 6.480 6.230 6.360 6258612 39857576 600120 2012-05-28 6.320 6.400 6.190 6.400 4998941 31474214 600120 2012-05-29 6.400 6.530 6.360 6.440 5729309 37068284 600120 2012-05-30 6.460 6.460 6.350 6.370 3101571 19816940 600120 2012-05-31 6.300 6.350 6.230 6.240 3738985 23467144 600120 2012-06-01 6.210 6.280 6.080 6.110 4507252 27767424 600120 2012-06-04 6.000 6.010 5.840 5.860 3086545 18299630 600120 2012-06-05 5.870 5.960 5.860 5.930 1942942 11496336 600120 2012-06-06 5.960 5.960 5.600 5.760 2998969 17440282 600120 2012-06-07 5.780 5.960 5.770 5.790 2594537 15175500 600120 2012-06-08 5.830 5.880 5.700 5.750 1808774 10501804 600120 2012-06-11 5.800 5.850 5.730 5.830 1861236 10775968 600120 2012-06-12 5.850 5.930 5.800 5.850 2007190 11774889 600120 2012-06-13 5.860 6.130 5.790 6.100 5304235 31868112 600120 2012-06-14 6.050 6.090 5.910 5.930 3202802 19241666 600120 2012-06-15 5.930 5.970 5.810 5.900 1774010 10459891 600120 2012-06-18 5.900 5.990 5.870 5.880 2001440 11862747 600120 2012-06-19 5.880 5.950 5.820 5.830 1968435 11601531 600120 2012-06-20 5.840 5.980 5.810 5.950 2428144 14288923 600120 2012-06-21 5.910 5.910 5.700 5.760 1990001 11558386 600120 2012-06-26 5.530 5.590 5.400 5.480 2086008 11426576 600120 2012-06-27 5.470 5.580 5.450 5.450 1080842 5962850 600120 2012-06-28 5.510 5.520 5.260 5.280 1549881 8344252 600120 2012-06-29 5.250 5.350 5.180 5.330 1597134 8426737 600120 2012-07-02 5.400 5.410 5.320 5.350 1118989 5986785 600120 2012-07-03 5.370 5.430 5.310 5.400 1379692 7439132 600120 2012-07-04 5.400 5.470 5.380 5.410 1395455 7563382 600120 2012-07-05 5.410 5.410 5.280 5.360 1534313 8190450 600120 2012-07-06 5.400 5.520 5.320 5.400 2657751 14387281 600120 2012-07-09 5.400 5.490 5.320 5.330 2448854 13205528 600120 2012-07-10 5.350 5.860 5.340 5.860 7802024 45076920 600120 2012-07-11 6.110 6.330 6.060 6.240 22276192 137748160 600120 2012-07-12 6.130 6.330 6.030 6.320 16639952 103002552 600120 2012-07-13 6.250 6.890 6.170 6.610 19638960 127671288 600120 2012-07-16 6.500 6.750 6.410 6.490 15513604 102224928 600120 2012-07-17 6.360 6.950 6.260 6.820 18767700 124877352 600120 2012-07-18 6.720 7.000 6.560 6.800 18327382 124799976 600120 2012-07-19 6.700 6.860 6.550 6.680 15164894 101237520 600120 2012-07-20 6.550 6.630 6.380 6.440 9728719 63227360 600120 2012-07-23 6.330 6.370 6.160 6.320 7001618 44002316 600120 2012-07-24 6.270 6.450 6.220 6.340 6881987 43810260 600120 2012-07-25 6.250 6.660 6.210 6.500 12923801 83741672 600120 2012-07-26 6.450 6.550 6.150 6.200 7061937 44964776 600120 2012-07-27 6.410 6.420 6.110 6.230 5700987 35619536 600120 2012-07-30 6.170 6.240 5.610 5.610 8543043 49370452 600120 2012-07-31 5.600 5.940 5.480 5.710 7669121 44260340 600120 2012-08-01 5.620 5.830 5.550 5.720 4405627 25176060 600120 2012-08-02 5.730 5.970 5.680 5.890 7018821 41113080 600120 2012-08-03 5.850 5.940 5.830 5.930 3797656 22333222 600120 2012-08-06 5.930 6.050 5.820 6.010 5291014 31587280 600120 2012-08-07 6.010 6.330 6.000 6.190 8028181 49717732 600120 2012-08-08 6.220 6.220 6.060 6.140 4085520 24994584 600120 2012-08-09 6.100 6.120 5.810 6.120 8067361 48357004 600120 2012-08-10 6.050 6.190 6.010 6.060 4016290 24371520 600120 2012-08-13 6.200 6.670 6.200 6.330 15222520 97680776 600120 2012-08-14 6.500 6.900 6.350 6.680 17682586 117190552 600120 2012-08-15 6.690 6.690 6.330 6.390 10266937 66058708 600120 2012-08-16 6.400 6.660 6.150 6.160 9145371 58658368 600120 2012-08-17 6.120 6.160 5.940 6.100 6471054 39053048 600120 2012-08-20 6.100 6.290 5.980 6.270 5655946 34765680 600120 2012-08-21 6.150 6.300 6.120 6.230 6037340 37561272 600120 2012-08-22 6.230 6.230 6.000 6.050 5329714 32457810 600120 2012-08-23 5.940 6.140 5.940 6.090 3759488 22779714 600120 2012-08-24 6.150 6.290 6.050 6.080 6970082 42997304 600120 2012-08-27 6.050 6.050 5.720 5.850 4477758 26282412 600120 2012-08-28 5.860 5.930 5.770 5.900 2470654 14475564 600120 2012-08-29 5.900 5.900 5.750 5.750 3104898 17997202 600120 2012-08-30 5.690 5.850 5.590 5.670 2875641 16442648 600120 2012-08-31 5.670 5.780 5.600 5.750 1848001 10556758 600120 2012-09-03 5.740 5.930 5.680 5.900 3951762 23129270 600120 2012-09-04 5.920 5.920 5.690 5.700 3707201 21481764 600120 2012-09-05 5.690 5.750 5.620 5.720 2782154 15832339 600120 2012-09-06 5.750 5.780 5.640 5.690 2869553 16314120 600120 2012-09-07 5.760 5.970 5.700 5.890 5783402 33932288 600120 2012-09-10 5.890 6.080 5.860 6.060 6170653 37105776 600120 2012-09-11 6.040 6.060 5.910 6.060 3652909 21851064 600120 2012-09-12 6.100 6.130 6.020 6.060 3930014 23842410 600120 2012-09-13 6.080 6.100 5.910 5.910 2957667 17805100 600120 2012-09-14 5.920 6.000 5.830 5.890 2792995 16490332 600120 2012-09-17 5.820 5.890 5.640 5.640 2734759 15720210 600120 2012-09-18 5.640 5.760 5.610 5.650 1748883 9932194 600120 2012-09-19 5.680 5.780 5.680 5.740 1306841 7485999 600120 2012-09-20 5.740 5.740 5.380 5.450 2683344 14859092 600120 2012-09-21 5.440 5.470 5.300 5.430 1612603 8708786 600120 2012-09-24 5.450 5.510 5.380 5.480 1343010 7340127 600120 2012-09-25 5.480 5.490 5.340 5.380 1247570 6757381 600120 2012-09-26 5.360 5.440 5.130 5.180 1431045 7528501 600120 2012-09-27 5.170 5.330 5.140 5.260 1763023 9279980 600120 2012-09-28 5.200 5.360 5.200 5.340 2006394 10612739 600120 2012-10-08 5.360 5.560 5.350 5.460 3435758 18897262 600120 2012-10-09 5.460 5.550 5.450 5.550 2209764 12187114 600120 2012-10-10 5.540 5.580 5.480 5.570 1898232 10539406 600120 2012-10-11 5.550 5.570 5.410 5.460 1873838 10315861 600120 2012-10-12 5.460 5.490 5.360 5.460 1619960 8811500 600120 2012-10-15 5.480 5.480 5.330 5.360 1355799 7280243 600120 2012-10-16 5.390 5.430 5.330 5.430 1427428 7711129 600120 2012-10-17 5.440 5.500 5.400 5.480 1262048 6901247 600120 2012-10-18 5.450 5.560 5.450 5.540 1938503 10716095 600120 2012-10-19 5.500 5.560 5.500 5.550 1629063 9013752 600120 2012-10-22 5.500 5.540 5.460 5.540 1523985 8396867 600120 2012-10-23 5.520 5.550 5.470 5.470 1517978 8364718 600120 2012-10-24 5.480 5.640 5.480 5.620 4318489 24147084 600120 2012-10-25 5.600 5.620 5.460 5.510 2332800 12935266 600120 2012-10-26 5.500 5.520 5.300 5.320 1968179 10570681 600120 2012-10-29 5.300 5.350 5.240 5.280 1184547 6253799 600120 2012-10-30 5.280 5.370 5.270 5.350 861803 4588509 600120 2012-10-31 5.370 5.370 5.240 5.290 1023519 5407486 600120 2012-11-01 5.300 5.420 5.280 5.410 1483984 7962948 600120 2012-11-02 5.410 5.440 5.360 5.440 1308161 7073081 600120 2012-11-05 5.450 5.530 5.400 5.500 2027746 11118383 600120 2012-11-06 5.500 5.500 5.370 5.450 1314405 7139731 600120 2012-11-07 5.360 5.370 5.160 5.270 4737230 24837488 600120 2012-11-08 5.200 5.240 5.140 5.150 2036601 10567178 600120 2012-11-09 5.130 5.220 5.120 5.200 1307604 6757081 600120 2012-11-12 5.200 5.380 5.070 5.360 4254705 22151888 600120 2012-11-13 5.700 5.900 5.530 5.530 15434146 88320960 600120 2012-11-14 5.390 5.430 5.150 5.210 8091713 42871408 600120 2012-11-15 5.200 5.320 5.150 5.180 3814789 19947090 600120 2012-11-16 5.160 5.180 5.000 5.090 3265990 16524752 600120 2012-11-19 5.090 5.140 5.010 5.100 2257164 11403216 600120 2012-11-20 5.100 5.140 5.030 5.030 1571279 7984035 600120 2012-11-21 5.000 5.060 4.880 5.050 3050816 15147326 600120 2012-11-22 5.000 5.020 4.920 4.950 1788547 8869217 600120 2012-11-23 4.960 5.020 4.940 4.960 1372151 6832961 600120 2012-11-26 4.980 4.990 4.880 4.880 1137756 5597685 600120 2012-11-27 4.860 4.950 4.700 4.740 1689649 8158868 600120 2012-11-28 4.740 4.740 4.520 4.590 1284289 5941912 600120 2012-11-29 4.590 4.640 4.480 4.490 969922 4423860 600120 2012-11-30 4.450 4.560 4.450 4.520 1235100 5569609 600120 2012-12-03 4.520 4.550 4.310 4.320 1379623 6147945 600120 2012-12-04 4.320 4.410 4.230 4.380 1667153 7171231 600120 2012-12-05 4.360 4.580 4.360 4.580 2422869 10954693 600120 2012-12-06 4.580 4.620 4.500 4.540 1407798 6396522 600120 2012-12-07 4.510 4.700 4.510 4.680 2462421 11472038 600120 2012-12-10 4.680 4.880 4.680 4.830 3202908 15354105 600120 2012-12-11 4.830 4.910 4.740 4.740 3257935 15748287 600120 2012-12-12 4.740 4.780 4.650 4.730 2110366 9952871 600120 2012-12-13 4.700 4.800 4.650 4.720 2028066 9630472 600120 2012-12-14 4.680 4.930 4.680 4.890 4415891 21475120 600120 2012-12-17 4.870 5.090 4.870 4.940 5758027 28685946 600120 2012-12-18 4.910 5.030 4.900 4.920 3205939 15937582 600120 2012-12-19 4.950 4.970 4.890 4.940 1970912 9721340 600120 2012-12-20 4.910 4.970 4.860 4.960 2776664 13648103 600120 2012-12-21 5.000 5.020 4.930 4.940 2582680 12835924 600120 2012-12-24 4.900 4.990 4.900 4.950 1395443 6916473 600120 2012-12-25 4.910 5.040 4.910 5.030 3671653 18358908 600120 2012-12-26 5.030 5.080 4.990 5.070 3383588 17015576 600120 2012-12-27 5.050 5.100 5.020 5.030 3100081 15631201 600120 2012-12-28 5.020 5.050 5.000 5.040 3738248 18785418 600120 2012-12-31 5.050 5.210 5.020 5.180 5492425 28178540 600120 2013-01-04 5.180 5.250 5.040 5.160 4505342 23113802 600120 2013-01-07 5.140 5.240 5.100 5.230 3740693 19357138 600120 2013-01-08 5.230 5.250 5.160 5.230 3428131 17831330 600120 2013-01-09 5.750 5.750 5.620 5.750 15137964 86851376 600120 2013-01-10 5.890 5.890 5.590 5.680 19259634 109951720 600120 2013-01-11 5.710 6.190 5.600 5.830 17767632 104686280 600120 2013-01-14 5.700 6.140 5.690 6.080 17128604 103055384 600120 2013-01-15 6.050 6.110 5.950 6.080 11690986 70449808 600120 2013-01-16 6.060 6.060 5.810 5.940 9519043 56312520 600120 2013-01-17 5.940 5.940 5.760 5.820 5490334 31996616 600120 2013-01-18 5.840 5.910 5.800 5.870 4441257 26049472 600120 2013-01-21 5.900 5.950 5.830 5.950 4949409 29187012 600120 2013-01-22 5.920 5.960 5.800 5.890 5524068 32470218 600120 2013-01-23 5.890 5.920 5.670 5.760 5285930 30515522 600120 2013-01-24 5.830 5.990 5.700 5.810 8746975 51253216 600120 2013-01-25 5.710 5.780 5.670 5.690 2938357 16774085 600120 2013-01-28 5.720 5.920 5.720 5.920 5615520 32894768 600120 2013-01-29 5.920 6.030 5.870 5.970 7778448 46530040 600120 2013-01-30 5.970 6.170 5.970 6.050 7974663 48414056 600120 2013-01-31 6.450 6.660 6.250 6.330 20167094 129655856 600120 2013-02-01 6.330 6.850 6.260 6.480 15610902 102880336 600120 2013-02-04 6.330 6.330 6.080 6.130 11360102 70013160 600120 2013-02-05 6.030 6.160 6.020 6.140 4903472 29867632 600120 2013-02-06 6.170 6.230 6.120 6.170 3826076 23581090 600120 2013-02-07 6.140 6.220 6.130 6.180 3255472 20093372 600120 2013-02-08 6.200 6.260 6.180 6.190 4681355 29087642 600120 2013-02-18 6.280 6.310 6.200 6.260 4845549 30340608 600120 2013-02-19 6.250 6.320 6.100 6.130 4727252 29183232 600120 2013-02-20 6.170 6.300 6.130 6.300 5596862 34757520 600120 2013-02-21 6.260 6.490 6.200 6.440 12809449 81299392 600120 2013-02-22 6.410 6.510 6.270 6.290 7584128 48492400 600120 2013-02-25 6.280 6.330 6.160 6.310 5220263 32598266 600120 2013-02-26 6.320 6.420 6.220 6.240 6156654 38917012 600120 2013-02-27 6.210 6.320 6.190 6.240 4068847 25377816 600120 2013-02-28 6.270 6.410 6.220 6.370 7447814 47241996 600120 2013-03-01 6.360 6.610 6.260 6.610 14957895 96688696 600120 2013-03-04 6.520 6.780 6.440 6.550 13564858 89516888 600120 2013-03-05 6.500 6.800 6.500 6.720 11519010 76836992 600120 2013-03-06 6.920 7.390 6.800 7.390 26849848 189968432 600120 2013-03-07 7.780 8.130 7.670 8.030 40965844 322816768 600120 2013-03-08 7.950 8.220 7.730 7.790 28627918 228489488 600120 2013-03-11 7.870 7.870 7.180 7.530 18937636 141743744 600120 2013-03-12 7.450 7.620 7.170 7.480 15535111 115027416 600120 2013-03-13 7.410 7.540 7.230 7.420 10559835 78049992 600120 2013-03-14 7.350 7.630 7.340 7.450 11297736 84727264 600120 2013-03-15 7.490 7.710 7.310 7.540 14645334 110597144 600120 2013-03-18 7.400 7.400 6.900 7.010 13872665 98271352 600120 2013-03-19 6.980 7.150 6.770 6.910 10347213 71593536 600120 2013-03-20 6.940 7.260 6.850 7.200 11781630 83496264 600120 2013-03-21 7.150 7.520 7.150 7.490 12763050 94041384 600120 2013-03-22 7.420 7.670 7.280 7.570 13966066 104551368 600120 2013-03-25 7.750 7.990 7.610 7.630 15983236 124691712 600120 2013-03-26 7.630 8.390 7.600 8.380 26428312 211724464 600120 2013-03-27 8.250 8.540 8.090 8.260 24350716 202126576 600120 2013-03-28 8.170 8.770 7.890 8.500 34068656 286435744 600120 2013-03-29 8.370 8.600 8.240 8.240 14159268 118628944 600120 2013-04-01 8.310 8.740 8.280 8.500 17816092 152612272 600120 2013-04-02 8.690 8.700 7.810 8.000 18838794 156627616 600120 2013-04-03 7.990 8.200 7.800 7.920 11452403 91542184 600120 2013-04-08 7.730 8.550 7.560 8.470 13963396 113644160 600120 2013-04-09 8.540 8.970 8.460 8.840 18834228 165475248 600120 2013-04-10 8.790 8.870 8.630 8.770 10376445 90857848 600120 2013-04-11 8.770 8.850 8.560 8.610 8377727 72409424 600120 2013-04-12 8.720 9.080 8.720 8.890 19480370 173966848 600120 2013-04-15 8.910 8.980 8.620 8.630 10265764 89914552 600120 2013-04-16 8.480 8.930 8.190 8.840 13336577 114974256 600120 2013-04-17 8.790 9.040 8.630 8.940 11676056 103827328 600120 2013-04-18 8.860 9.280 8.800 9.080 11847024 107250600 600120 2013-04-19 9.100 9.990 8.980 9.790 33011634 318405920
---- 再次鸣谢 散漫 童鞋的热心。
---- 我在用 matplotlib 的时候有接触过 PyQT 和 wxPython 的概念,另外昨天也稍微股沟了一下。它们之间的关系: matplotlib 是前端,PyQT 或 wxPython 是后端。或者说 matplotlib 相当于 Python,而 PyQT 和 wxPython 相当于 C。
*. 实际用的时候,可以用 matplotlib 绘图,也可以直接用 PyQT 绘图,也可以用 PyQT 做一个 GUI 然后在后台调用 matplotlib 绘图,取舍的考虑也跟 Python 和 C 很像:PyQT 快些,但都是些底层的特性。matplotlib 用起来方便,但速度就不那么可观,只适合做一些不要求实时性的静态任务。
*. 用 matplotlib 绘图的时候可以指定使用哪种后台,比如这个:
import matplotlib # 这个要紧跟在 import matplotlib 之后,而且必须安装了 wxpython 2.8 才行。 matplotlib.use("WXAgg", warn=True)
这个就是指定后台使用 wxPython,当然必须先安装了这个组件才行。
*. matplotlib 代码里可以直接使用 PyQT 等后端的特性,比如捕捉鼠标点击事件,等等。
---- 另外,有一位 伊莱·班德斯基 童鞋(看文章是个大牛)演示了怎样把 PyQT 和 matplotlib 整合在一起,用 PyQT 写图形界面,在后台调用 matplotlib 绘图:
http://eli.thegreenplace.net/2009/01/20/matplotlib-with-pyqt-guis/
---- 最后说明下,PyQT 只有 GPL 授权和商业授权可选。无论屌丝拿它开发了什么唯我独尊的牛B项目,只要还 买不起 不想购买商业许可,那只能门户开放,大家利益均沾。
趋势线
---- 有句话怎么说来着,“只有趋势才是你的朋友”。
---- 对任意一点可以辨认它所处的趋势。算法保证如果 A 点和 B 点的趋势起点都在 O,那么 A、B 之间任意一点的趋势起点也在 O 点。
用 Python / Matplotlib 画出来的股票 K线图 (四)
---- 前一篇在这: 用 Python / Matplotlib 画出来的股票 K线图 (三)
---- 日线与分时对比行情:
---- 下面是绘图脚本与绘图数据合在一起的压缩文件。注意:
1. 是 py3 脚本,matplotlib 已经支持 py3。绝大部分都是中文写的,不想被英文虐出翔了。
2. 是 Linux 下写的,需要在 Linux 下执行。先解压,然后到生成的目录下执行:
python3 绘图.py
就可以了。会生成一个 绘图.log 文件和一个图片文件放在相同目录下。
<补记>:已经证实经过很小的改动就可以在 windows 下运行,输出中文字内容的大小样式有区别,其它一样,得益于 python 和 matplotlib 的跨平台特性。但是我不知道具体改哪些。
---- 解压后的文件结构:
日线分时对比行情/
├Public/
│├Public.py
│└__init__.py
├子图定义/
│├__init__.py
│├公司信息子图.py
│├分时价格子图.py
│├分时手数子图.py
│├实盘价格子图.py
│├实盘手数子图.py
│├日线价格子图.py
│└日线换手子图.py
├绘图.py
└绘图数据.pickle
---- 关于授权:除了特别说明的以外,本博客里的代码都用 “干啥随你便” 协议进行授权。
Unless otherwise noted, all code pieces in this blog are licensed under the "DWYW(Do What the f Whatever You Want)" agreement. Good luck.
---- Download
用 Python / Matplotlib 画出来的股票 K线图 (三)
---- 前一篇在这: 用 Python / Matplotlib 画出来的股票 K线图 (二)
---- 后一篇在这: 用 Python / Matplotlib 画出来的股票 K线图 (四)
---- 就像上回说的,新内容加进来。除此之外,与上一版代码相比最大的改动就是内部重构过,子图全部定义成 class。图中一共包含 5 个子图,从上到下依次是: 基本信息(就是那些文字)、历史价格、历史换手率、价格、换手率。通过输入的绘图数据进行控制,任何一个子图都可以关闭,关闭子图可以节省绘图时间和存储空间。本来还有一个财务信息子图要加进去,但是现在想暂时告一段落,先弄点其它的。
---- 作为输入的 Python pickle file 在 这里。
---- 最后是脚本,仍然是 Python 2 的:
# -*- coding: utf-8 -*- import os import sys import pickle import math import datetime import itertools import matplotlib matplotlib.use("WXAgg", warn=True) # 这个要紧跟在 import matplotlib 之后,而且必须安装了 wxpython 2.8 才行。 import matplotlib.pyplot as pyplot import matplotlib.font_manager as font_manager import numpy from matplotlib.ticker import NullLocator, FixedLocator, MultipleLocator, FuncFormatter, NullFormatter from matplotlib.patches import Ellipse __font_properties__= font_manager.FontProperties(fname='/usr/share/fonts/truetype/wqy/wqy-zenhei.ttc') __color_lightsalmon__= '#ffa07a' __color_pink__= '#ffc0cb' __color_navy__= '#000080' __color_gold__= '#FDDB05' __color_gray30__= '0.3' __color_gray70__= '0.7' __color_lightblue__= 'lightblue' __shrink__= 1.0 / 4 __expbase__= 1.1 class SubPlot_BasicInfo: ''' 公司的基本信息 Note: this is not "real" subplot, no Axes object contained. ''' def __init__(self, pdata, parent, name): self._name= name self._pdata= pdata self._cominfo= self._pdata[u'公司信息'] self._parent= parent self._Axes= None self._xsize, \ self._ysize= self._compute_size() def _compute_size(self): return (300.0, 1.8) def get_size(self): return (self._xsize, self._ysize) def build_axes(self, figobj, rect): axes= figobj.add_axes(rect) axes.set_frame_on(False) self._Axes= axes self.set_xticks() self.set_yticks() def set_xticks(self): axes= self._Axes xaxis= axes.get_xaxis() # 设定 X 轴坐标的范围 #================================================================================================================================================== axes.set_xlim(0, self._xsize) xaxis.set_major_locator(NullLocator()) for mal in axes.get_xticklabels(minor=False): mal.set_visible(False) for mil in axes.get_xticklabels(minor=True): mil.set_visible(False) def set_yticks(self): axes= self._Axes yaxis= axes.get_yaxis() # 设定 X 轴坐标的范围 #================================================================================================================================================== axes.set_ylim(0, self._ysize) yaxis.set_major_locator(NullLocator()) for mal in axes.get_yticklabels(minor=False): mal.set_visible(False) for mil in axes.get_yticklabels(minor=True): mil.set_visible(False) def plot(self): self.plot_codesymbol(xbase=0.0, ybase=self._ysize) self.plot_codesymbol_2(xbase=self._xsize, ybase=self._ysize) self.plot_companyname(xbase=0.0, ybase=self._ysize-0.8) self.plot_companylocation(xbase=48.0, ybase=self._ysize) self.plot_mainbusiness(xbase=48.0, ybase=self._ysize) self.plot_description(xbase=90.0, ybase=self._ysize) self.plot_sortinginfo(xbase=165.0, ybase=self._ysize) def plot_codesymbol(self, xbase, ybase): ''' 交易代码、公司简称 ''' txtstr= self._cominfo[u'代码'] + u' ' + self._cominfo[u'简称'] label= self._Axes.text(xbase, ybase, txtstr, fontproperties=__font_properties__, verticalalignment='top', horizontalalignment='left') label.set_fontsize(16.0) def plot_codesymbol_2(self, xbase, ybase): txtstr= self._cominfo[u'简称二'] label= self._Axes.text(xbase, ybase, txtstr, fontproperties=__font_properties__, verticalalignment='top', horizontalalignment='right') label.set_fontsize(16.0) def plot_companyname(self, xbase, ybase): ''' 曾用名、全名、英文名 ''' txtstr= self._cominfo[u'基本情况'][u'曾用名'] txtlist= txtstr.split('->') if len(txtlist) > 15: txtstr= ' -> '.join(txtlist[:5]) + ' ->\n' + ' -> '.join(txtlist[5:10]) + ' ->\n' + ' -> '.join(txtlist[10:15]) + ' ->\n' + ' -> '.join(txtlist[15:]) + '\n' elif len(txtlist) > 10: txtstr= ' -> '.join(txtlist[:5]) + ' ->\n' + ' -> '.join(txtlist[5:10]) + ' ->\n' + ' -> '.join(txtlist[10:]) + '\n' elif len(txtlist) > 5: txtstr= ' -> '.join(txtlist[:5]) + ' ->\n' + ' -> '.join(txtlist[5:]) + '\n' else: txtstr= ' -> '.join(txtlist) + '\n' txtstr += self._cominfo[u'基本情况'][u'公司名称'] + '\n' txtstr += self._cominfo[u'基本情况'][u'英文名称'] label= self._Axes.text(xbase, ybase, txtstr, fontproperties=__font_properties__, verticalalignment='top', horizontalalignment='left') label.set_fontsize(4.5) def plot_companylocation(self, xbase, ybase): ''' 地域、所属行业、上市日期 ''' txtstr= self._cominfo[u'公司概况'][u'区域'] + ' ' + self._cominfo[u'公司概况'][u'所属行业'] + ' ' + self._cominfo[u'发行相关'][u'上市日期'] label= self._Axes.text(xbase, ybase, txtstr, fontproperties=__font_properties__, verticalalignment='top', horizontalalignment='left') label.set_fontsize(6.5) def plot_mainbusiness(self, xbase, ybase): ''' 主营业务 ''' # 查找表: (<文字长度>, <每行字数>, <字体大小>, <Y轴偏移量>) lookups= ( (20, 10, 12.0, 0.5), (45, 15, 8.2, 0.5), (80, 20, 6.2, 0.5), (125, 25, 5.0, 0.5), (180, 30, 4.1, 0.5), (245, 35, 3.5, 0.4), (999999, 37, 3.4, 0.4) ) txtstr= self._cominfo[u'基本情况'][u'主营业务'] length= len(txtstr) for sizelimit, linelimit, fontsize, yshift in lookups: if length <= sizelimit: txtstr= '\n'.join([txtstr[linelimit*idx : linelimit*(idx+1)] for idx in range(length//linelimit + 1)]) fsize= fontsize ycoord= ybase - yshift break label= self._Axes.text(xbase, ycoord, txtstr, fontproperties=__font_properties__, verticalalignment='top', horizontalalignment='left', color='blue') label.set_fontsize(fsize) def plot_description(self, xbase, ybase): ''' 公司简介 ''' # 查找表: (<文字长度>, <每行字数>, <字体大小>) lookups= ( (150, 30, 7.0), (240, 40, 5.6), (329, 47, 4.8), (432, 54, 4.2), (576, 64, 3.5), (670, 67, 3.4), (792, 72, 3.1), (960, 80, 2.8), (1222, 94, 2.4), (1428, 102, 2.26), (1620, 108, 2.12), (1938, 114, 2.00), (999999, 130, 1.75) ) txtstr= self._cominfo[u'公司概况'][u'公司简介'] # 26 ~ 2600 字符 length= len(txtstr) for sizelimit, linelimit, fontsize in lookups: if length <= sizelimit: txtstr= '\n'.join([txtstr[linelimit*idx : linelimit*(idx+1)] for idx in range(length//linelimit + 1)]) fsize= fontsize break label= self._Axes.text(xbase, ybase, txtstr, fontproperties=__font_properties__, verticalalignment='top', horizontalalignment='left') label.set_fontsize(fsize) def plot_sortinginfo(self, xbase, ybase): ''' 行业板块信息 ''' infolist= self._cominfo[u'行业板块'] for idx in range(len(infolist)//10 + 1): txtstr= '\n'.join(infolist[10*idx : 10*(idx+1)]) if not txtstr: break xcoord= xbase + 25.0*idx label= self._Axes.text(xcoord, ybase, txtstr, fontproperties=__font_properties__, verticalalignment='top', horizontalalignment='left', color='blue') label.set_fontsize(3.4) class SubPlot_Financial: ''' 换手率子图 ''' pass class SubPlot_PriceBase: ''' ''' def __init__(self, pdata, parent, xparams, name): ''' ''' self._name= name # 派生类自己设置 self._pdata= pdata self._parent= parent self._expbase= __expbase__ self._xparams= xparams self._shrink= __shrink__ if name == 'pricefs' else 1.0 # 绘图数据 quotes= pdata[u'行情'] if name == 'pricefs': self._dates= quotes[u'日期'] self._open= quotes[u'开盘'] self._close= quotes[u'收盘'] self._high= quotes[u'最高'] self._low= quotes[u'最低'] if u'简化' in quotes: self._simple= quotes[u'简化'] # if u'换手率' in quotes: self._torate= quotes[u'换手率'] # if u'成交量' in quotes: self._volume= quotes[u'成交量'] # if u'成交额' in quotes: self._turnover= quotes[u'成交额'] if u'3日均' in quotes: self._average3= quotes[u'3日均'] if u'5日均' in quotes: self._average5= quotes[u'5日均'] if u'10日均' in quotes: self._average10= quotes[u'10日均'] if u'30日均' in quotes: self._average30= quotes[u'30日均'] if u'60日均' in quotes: self._average60= quotes[u'60日均'] if u'开盘二' in quotes: self._open_2= quotes[u'开盘二'] self._close_2= quotes[u'收盘二'] self._high_2= quotes[u'最高二'] self._low_2= quotes[u'最低二'] if u'简化二' in quotes: self._simple_2= quotes[u'简化二'] # if u'换手率二' in quotes: self._torate_2= quotes[u'换手率二'] # if u'成交量二' in quotes: self._volume_2= quotes[u'成交量二'] # if u'成交额二' in quotes: self._turnover_2= quotes[u'成交额二'] if u'3日均二' in quotes: self._average3_2= quotes[u'3日均二'] if u'5日均二' in quotes: self._average5_2= quotes[u'5日均二'] if u'10日均二' in quotes: self._average10_2= quotes[u'10日均二'] if u'30日均二' in quotes: self._average30_2= quotes[u'30日均二'] if u'60日均二' in quotes: self._average60_2= quotes[u'60日均二'] else: sidx, eidx= pdata[u'任务描述'][u'起始偏移'], pdata[u'任务描述'][u'结束偏移'] self._dates= quotes[u'日期'][sidx:eidx] self._open= quotes[u'开盘'][sidx:eidx] self._close= quotes[u'收盘'][sidx:eidx] self._high= quotes[u'最高'][sidx:eidx] self._low= quotes[u'最低'][sidx:eidx] if u'简化' in quotes: self._simple= quotes[u'简化'][sidx:eidx] # if u'换手率' in quotes: self._torate= quotes[u'换手率'][sidx:eidx] # if u'成交量' in quotes: self._volume= quotes[u'成交量'][sidx:eidx] # if u'成交额' in quotes: self._turnover= quotes[u'成交额'][sidx:eidx] if u'3日均' in quotes: self._average3= quotes[u'3日均'][sidx:eidx] if u'5日均' in quotes: self._average5= quotes[u'5日均'][sidx:eidx] if u'10日均' in quotes: self._average10= quotes[u'10日均'][sidx:eidx] if u'30日均' in quotes: self._average30= quotes[u'30日均'][sidx:eidx] if u'60日均' in quotes: self._average60= quotes[u'60日均'][sidx:eidx] if u'开盘二' in quotes: self._open_2= quotes[u'开盘二'][sidx:eidx] self._close_2= quotes[u'收盘二'][sidx:eidx] self._high_2= quotes[u'最高二'][sidx:eidx] self._low_2= quotes[u'最低二'][sidx:eidx] if u'简化二' in quotes: self._simple_2= quotes[u'简化二'][sidx:eidx] # if u'换手率二' in quotes: self._torate_2= quotes[u'换手率二'][sidx:eidx] # if u'成交量二' in quotes: self._volume_2= quotes[u'成交量二'][sidx:eidx] # if u'成交额二' in quotes: self._turnover_2= quotes[u'成交额二'][sidx:eidx] if u'3日均二' in quotes: self._average3_2= quotes[u'3日均二'][sidx:eidx] if u'5日均二' in quotes: self._average5_2= quotes[u'5日均二'][sidx:eidx] if u'10日均二' in quotes: self._average10_2= quotes[u'10日均二'][sidx:eidx] if u'30日均二' in quotes: self._average30_2= quotes[u'30日均二'][sidx:eidx] if u'60日均二' in quotes: self._average60_2= quotes[u'60日均二'][sidx:eidx] self._length= len(self._dates) # XXX: 由派生类设定 # 衍生数据 #============================================================================================================== self._xindex= numpy.arange(self._length) # X 轴上的 index,一个辅助数据 self._zipoc= zip(self._open, self._close) self._up= numpy.array( [ True if po < pc and po is not None else False for po, pc in self._zipoc] ) # 标示出该天股价日内上涨的一个序列 self._down= numpy.array( [ True if po > pc and po is not None else False for po, pc in self._zipoc] ) # 标示出该天股价日内下跌的一个序列 self._side= numpy.array( [ True if po == pc and po is not None else False for po, pc in self._zipoc] ) # 标示出该天股价日内走平的一个序列 if u'开盘二' in quotes: self._zipoc_2= zip(self._open_2, self._close_2) self._up_2= numpy.array( [ True if po < pc and po is not None else False for po, pc in self._zipoc_2] ) # 标示出该天股价日内上涨的一个序列 self._down_2= numpy.array( [ True if po > pc and po is not None else False for po, pc in self._zipoc_2] ) # 标示出该天股价日内下跌的一个序列 self._side_2= numpy.array( [ True if po == pc and po is not None else False for po, pc in self._zipoc_2] ) # 标示出该天股价日内走平的一个序列 self._Axes= None self._AxisX= None self._AxisY= None self._xsize= 0.0 self._ysize= 0.0 self._yhighlim= 0 # Y 轴最大坐标 self._ylowlim= 0 # Y 轴最小坐标 if u'开盘二' in self._pdata[u'行情']: self._Axes_2= None # 如果有第二个行情数据,就建立另一个 Axes 对象 self._AxisX_2= None self._AxisY_2= None self._yhighlim_2= 0 # Y 轴最大坐标 self._ylowlim_2= 0 # Y 轴最小坐标 self._compute_size() self._ytickset= self._compute_ytickset() # 需放在前一句后面 def _compute_size(self): ''' 根据绘图数据 pdata 计算出本子图的尺寸,修改数据成员 ''' quotes= self._pdata[u'行情'] popen= self._open[0] # int 类型 phigh= max( [ph for ph in self._high if ph is not None] ) # 最高价 plow= min( [pl for pl in self._low if pl is not None] ) # 最低价 # Y 轴范围 if self._name == 'pricefs': yhighlim= phigh * 1.2 # K线子图 Y 轴最大坐标 ylowlim= plow / 1.2 # K线子图 Y 轴最小坐标 else: yhighlim= phigh * 1.1 # K线子图 Y 轴最大坐标 ylowlim= plow / 1.1 # K线子图 Y 轴最小坐标 self._yhighlim= yhighlim self._ylowlim= ylowlim if u'开盘二' in quotes: popen_2= self._open_2[0] # 同上 phigh_2= max( [ph for ph in self._high_2 if ph is not None] ) # 第二个行情的最高价 phigh= max(phigh, int(phigh_2 * popen / float(popen_2))) # 以第一个行情为基准修正出的总最高价 plow_2= min( [pl for pl in self._low_2 if pl is not None] ) # 最低价 plow= min(plow, int(plow_2 * popen / float(popen_2))) # 以第一个行情为基准修正出的总最低价 if self._name == 'pricefs': yhighlim= phigh * 1.2 # K线子图 Y 轴最大坐标 ylowlim= plow / 1.2 # K线子图 Y 轴最小坐标 else: yhighlim= phigh * 1.1 # K线子图 Y 轴最大坐标 ylowlim= plow / 1.1 # K线子图 Y 轴最小坐标 ylowlim_2= ylowlim * popen_2 / float(popen) yhighlim_2= yhighlim * popen_2 / float(popen) self._yhighlim= yhighlim self._ylowlim= ylowlim self._yhighlim_2= yhighlim_2 self._ylowlim_2= ylowlim_2 # XXX: 价格在 Y 轴上的 “份数”。注意,虽然最高与最低价是以第一个行情为基准修正出来的,但其中包含的倍数因子对结果无影响,即: # log(base, num1) - log(base, num2) == # log(base, num1/num2) == # log(base, k*num1/k*num2) == # log(base, k*num1) - log(base, k*num2) # ,这是对数运算的性质。 xmargin= self._xparams['xmargin'] self._xsize= (self._length + xmargin*2) * self._shrink # int, 所有数据的长度,就是天数 self._ysize= (math.log(yhighlim, self._expbase) - math.log(ylowlim, self._expbase)) * self._shrink # float def get_size(self): return (self._xsize, self._ysize) def get_ylimits(self): return (self._yhighlim, self._ylowlim) def build_axes(self, figobj, rect): ''' 初始化 self._Axes 对象 ''' # 添加 Axes 对象 #================================================================================================================================================== if self._name == 'price' and 'torate' in self._parent._subplots: sharex= self._parent._subplots['torate'].get_axes() axes= figobj.add_axes(rect, axis_bgcolor='black', sharex=sharex) elif self._name == 'pricefs' and 'toratefs' in self._parent._subplots: sharex= self._parent._subplots['toratefs'].get_axes() axes= figobj.add_axes(rect, axis_bgcolor='black', sharex=sharex) else: axes= figobj.add_axes(rect, axis_bgcolor='black') axes.set_axisbelow(True) # 网格线放在底层 # axes.set_zorder(1) # XXX: 不顶用 # axes.patch.set_visible(False) # hide the 'canvas' axes.set_yscale('log', basey=self._expbase) # 使用对数坐标 # 改变坐标线的颜色 #================================================================================================================================================== for child in axes.get_children(): if isinstance(child, matplotlib.spines.Spine): child.set_color(__color_gold__) # 得到 X 轴 和 Y 轴 的两个 Axis 对象 #================================================================================================================================================== xaxis= axes.get_xaxis() yaxis= axes.get_yaxis() # 设置两个坐标轴上的网格线 #================================================================================================================================================== xaxis.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.2) xaxis.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1) if self._name == 'pricefs': # 如果是小图,就不设辅助网格线 yaxis.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.1) else: yaxis.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.2) yaxis.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1) yaxis.set_label_position('left') self._Axes= axes self._AxisX= xaxis self._AxisY= yaxis if u'开盘二' in self._pdata[u'行情']: # 添加 Axes 对象。注意,设置 axes_2 而不是 axes 的网格线,从而不会跑到 axes 边框上边的做法不顶用。 #================================================================================================================================================== axes_2= axes.twinx() # twinx is problematic, no use no more. # XXX: 下面这三行把第一个 axes 放在上面,这样不会被第二个 axes 的图形遮盖。用 zorder 不顶用。 axes.figure.axes[-2:]= [axes_2, axes] # XXX: axes.set_frame_on(False) # 如果不做此设定,axes_2 的内容会看不见 axes_2.set_frame_on(True) axes_2.set_axis_bgcolor('black') axes_2.set_axisbelow(True) # 网格线放在底层 axes_2.set_yscale('log', basey=self._expbase) # 使用对数坐标 # 改变坐标线的颜色 #================================================================================================================================================== for child in axes_2.get_children(): if isinstance(child, matplotlib.spines.Spine): child.set_color(__color_gold__) # 得到 X 轴 和 Y 轴 的两个 Axis 对象 #================================================================================================================================================== xaxis_2= axes_2.get_xaxis() yaxis_2= axes_2.get_yaxis() # 设置两个坐标轴上的网格线 #================================================================================================================================================== # xaxis_2.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.2) # xaxis_2.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1) # if self._name == 'pricefs': # 如果是小图,就不设辅助网格线 # yaxis_2.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.1) # else: # yaxis_2.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.2) # yaxis_2.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1) yaxis_2.set_label_position('right') self._Axes_2= axes_2 self._AxisX_2= xaxis_2 self._AxisY_2= yaxis_2 def set_xticks(self): xMajorLocator= self._xparams['xMajorLocator'] xMinorLocator= self._xparams['xMinorLocator'] axes= self._Axes xaxis= self._AxisX # 设定 X 轴坐标的范围 #================================================================================================================================================== xmargin= self._xparams['xmargin'] axes.set_xlim(-xmargin, self._length + xmargin) # 先设置 label 位置,再将 X 轴上的坐标设为不可见。因为与 成交量子图 共用 X 轴 #================================================================================================================================================== # 设定 X 轴的 Locator 和 Formatter xaxis.set_major_locator(xMajorLocator) # xaxis.set_major_formatter(xMajorFormatter) xaxis.set_minor_locator(xMinorLocator) # xaxis.set_minor_formatter(xMinorFormatter) # 将 X 轴上的坐标设为不可见。 for mal in axes.get_xticklabels(minor=False): mal.set_visible(False) for mil in axes.get_xticklabels(minor=True): mil.set_visible(False) def set_xticks_2(self): quotes= self._pdata[u'行情'] axes= self._Axes_2 xaxis= self._AxisX_2 xMajorLocator= self._xparams['xMajorLocator'] xMinorLocator= self._xparams['xMinorLocator'] # 设定 X 轴坐标的范围 #================================================================================================================================================== xmargin= self._xparams['xmargin'] axes.set_xlim(-xmargin, self._length + xmargin) # 先设置 label 位置,再将 X 轴上的坐标设为不可见。因为与 成交量子图 共用 X 轴 #================================================================================================================================================== # 设定 X 轴的 Locator 和 Formatter xaxis.set_major_locator(xMajorLocator) # xaxis.set_major_formatter(xMajorFormatter) xaxis.set_minor_locator(xMinorLocator) # xaxis.set_minor_formatter(xMinorFormatter) # 将 X 轴上的坐标设为不可见。 for mal in axes.get_xticklabels(minor=False): mal.set_visible(False) for mil in axes.get_xticklabels(minor=True): mil.set_visible(False) def _compute_ytickset(self): ''' 计算 Y 轴坐标点的位置,包括第二个行情 ''' quotes= self._pdata[u'行情'] expbase= self._expbase ytickset= {} yhighlim= self._yhighlim ylowlim= self._ylowlim if u'开盘二' in quotes: yhighlim_2= self._yhighlim_2 ylowlim_2= self._ylowlim_2 if self._name == 'price' and 'pricefs' in self._parent._subplots: tsetfs= self._parent._subplots['pricefs'].get_ytickset() majors= tsetfs['major'] while majors[-1] < yhighlim: majors.append(majors[-1] * expbase) while majors[0] > ylowlim: majors.insert(0, majors[0] / expbase) minors= tsetfs['minor'] while minors[-1] < yhighlim: minors.append(minors[-1] * expbase) while minors[0] > ylowlim: minors.insert(0, minors[0] / expbase) ytickset['major']= [loc for loc in majors if loc > ylowlim and loc < yhighlim] ytickset['minor']= [loc for loc in minors if loc > ylowlim and loc < yhighlim] else: # 主要坐标点 #---------------------------------------------------------------------------- majors= [ylowlim] while majors[-1] < yhighlim: majors.append(majors[-1] * 1.1) # 辅助坐标点 #---------------------------------------------------------------------------- minors= [ylowlim * 1.1**0.5] while minors[-1] < yhighlim: minors.append(minors[-1] * 1.1) ytickset['major']= [loc for loc in majors if loc > ylowlim and loc < yhighlim] # 注意,第一项(ylowlim)被排除掉了 ytickset['minor']= [loc for loc in minors if loc > ylowlim and loc < yhighlim] if u'开盘二' in quotes: popen= self._open[0] # int 类型 popen_2= self._open_2[0] # 同上 ytickset['major_2']= [loc * popen_2 / popen for loc in ytickset['major']] ytickset['minor_2']= [loc * popen_2 / popen for loc in ytickset['minor']] return ytickset def get_ytickset(self): return self._ytickset def set_yticks(self): ''' 设置第一只行情的 Y 轴坐标,包括坐标值在图中间的显示 ''' axes= self._Axes ylowlim= self._ylowlim yhighlim= self._yhighlim yaxis= self._AxisY majorticks= self._ytickset['major'] minorticks= self._ytickset['minor'] # 设定 Y 轴坐标的范围 #================================================================================================================================================== axes.set_ylim(ylowlim, yhighlim) # 设定 Y 轴上的坐标 #================================================================================================================================================== # 主要坐标点 #---------------------------------------------------------------------------- yMajorLocator= FixedLocator(numpy.array(majorticks)) # 确定 Y 轴的 MajorFormatter def y_major_formatter(num, pos=None): return str(round(num/1000.0, 2)) yMajorFormatter= FuncFormatter(y_major_formatter) # 设定 X 轴的 Locator 和 Formatter yaxis.set_major_locator(yMajorLocator) yaxis.set_major_formatter(yMajorFormatter) # 设定 Y 轴主要坐标点与辅助坐标点的样式 fsize= 4 if self._name == 'pricefs' else 6 for mal in axes.get_yticklabels(minor=False): mal.set_fontsize(fsize) # 辅助坐标点 #---------------------------------------------------------------------------- yMinorLocator= FixedLocator(numpy.array(minorticks)) # 确定 Y 轴的 MinorFormatter def y_minor_formatter(num, pos=None): return str(round(num/1000.0, 2)) yMinorFormatter= FuncFormatter(y_minor_formatter) # 设定 X 轴的 Locator 和 Formatter yaxis.set_minor_locator(yMinorLocator) yaxis.set_minor_formatter(yMinorFormatter) # 设定 Y 轴辅助坐标点的样式 if self._name == 'pricefs': for mil in axes.get_yticklabels(minor=True): mil.set_visible(False) else: for mil in axes.get_yticklabels(minor=True): mil.set_fontsize(5) mil.set_color('blue') def set_yticks_2(self): ''' 子图右侧的 Y 轴坐标 ''' axes= self._Axes_2 yaxis= self._AxisY_2 yhighlim_2= self._yhighlim_2 ylowlim_2= self._ylowlim_2 majorticks_2= self._ytickset['major_2'] minorticks_2= self._ytickset['minor_2'] # 设定 Y 轴坐标的范围 #================================================================================================================================================== axes.set_ylim(ylowlim_2, yhighlim_2) # 设定 Y 轴上的坐标 #================================================================================================================================================== # 主要坐标点 #---------------------------------------------------------------------------- yMajorLocator= FixedLocator(numpy.array(majorticks_2)) # 确定 Y 轴的 MajorFormatter def y_major_formatter(num, pos=None): return str(round(num/1000.0, 2)) yMajorFormatter= FuncFormatter(y_major_formatter) # 设定 X 轴的 Locator 和 Formatter yaxis.set_major_locator(yMajorLocator) yaxis.set_major_formatter(yMajorFormatter) # 设定 Y 轴主要坐标点与辅助坐标点的样式 fsize= 4 if self._name == 'pricefs' else 6 for mal in axes.get_yticklabels(minor=False): mal.set_fontsize(fsize) # 辅助坐标点 #---------------------------------------------------------------------------- yMinorLocator= FixedLocator(numpy.array(minorticks_2)) # XXX minor ticks 已经在上面一并设置,这里不需要了。 # 确定 Y 轴的 MinorFormatter def y_minor_formatter(num, pos=None): return str(round(num/1000.0, 2)) yMinorFormatter= FuncFormatter(y_minor_formatter) # 设定 X 轴的 Locator 和 Formatter yaxis.set_minor_locator(yMinorLocator) yaxis.set_minor_formatter(yMinorFormatter) # 设定 Y 轴主要坐标点与辅助坐标点的样式 if self._name == 'pricefs': for mil in axes.get_yticklabels(minor=True): mil.set_visible(False) else: for mil in axes.get_yticklabels(minor=True): mil.set_fontsize(5) mil.set_color('blue') def plot(self): ''' 由派生类自己定义 ''' pass def plot_candlestick(self): ''' 绘制 K 线 ''' axes= self._Axes xindex= self._xindex up= self._up down= self._down side= self._side # 绘制 K 线部分 #================================================================================================================================================== # 对开收盘价进行视觉修正 for idx, poc in enumerate(self._zipoc): if poc[0] == poc[1] and None not in poc: variant= int(round((poc[1]+1000)/2000.0, 0)) self._open[idx]= poc[0] - variant # 稍微偏离一点,使得在图线上不致于完全看不到 self._close[idx]= poc[1] + variant rarray_open= numpy.array(self._open) rarray_close= numpy.array(self._close) rarray_high= numpy.array(self._high) rarray_low= numpy.array(self._low) # XXX: 如果 up, down, side 里有一个全部为 False 组成,那么 vlines() 会报错。 # XXX: 可以使用 alpha 参数调节透明度 if True in up: axes.vlines(xindex[up], rarray_low[up], rarray_high[up], edgecolor='red', linewidth=0.6, label='_nolegend_', alpha=0.5) axes.vlines(xindex[up], rarray_open[up], rarray_close[up], edgecolor='red', linewidth=3.0, label='_nolegend_', alpha=0.5) if True in down: axes.vlines(xindex[down], rarray_low[down], rarray_high[down], edgecolor='green', linewidth=0.6, label='_nolegend_', alpha=0.5) axes.vlines(xindex[down], rarray_open[down], rarray_close[down], edgecolor='green', linewidth=3.0, label='_nolegend_', alpha=0.5) if True in side: axes.vlines(xindex[side], rarray_low[side], rarray_high[side], edgecolor='0.7', linewidth=0.6, label='_nolegend_', alpha=0.5) axes.vlines(xindex[side], rarray_open[side], rarray_close[side], edgecolor='0.7', linewidth=3.0, label='_nolegend_', alpha=0.5) def plot_simplified(self): ''' 绘制简化行情 ''' xindex= self._xindex axes= self._Axes rarray_simple= numpy.array(self._simple) axes.plot(xindex, rarray_simple, 'o-', color='white', linewidth=0.3, label='simple', \ markersize=0.3, markeredgecolor='white', markeredgewidth=0.1, alpha=0.3) # 简化行情 def plot_average(self): ''' 绘制均线 ''' # 绘制均线部分 #================================================================================================================================================== quotes= self._pdata[u'行情'] xindex= self._xindex axes= self._Axes if self._name == 'pricefs': widthw= 0.1 widthn= 0.07 mksize= 0.07 mkwidth= 0.1 alpha= 1.0 else: widthw= 0.2 widthn= 0.1 mksize= 0.3 mkwidth= 0.1 alpha= 1.0 if u'3日均' in quotes: rarray_3dayave= numpy.array(self._average3) axes.plot(xindex, rarray_3dayave, 'o-', color='white', linewidth=widthw, label='avg_3', \ markersize=mksize, markeredgecolor='white', markeredgewidth=mkwidth, alpha=alpha) # 3日均线 if u'5日均' in quotes: rarray_5dayave= numpy.array(self._average5) axes.plot(xindex, rarray_5dayave, 'o-', color='white', linewidth=widthn, label='avg_5', \ markersize=mksize, markeredgecolor='white', markeredgewidth=mkwidth, alpha=alpha) # 5日均线 if u'10日均' in quotes: rarray_10dayave= numpy.array(self._average10) axes.plot(xindex, rarray_10dayave, 'o-', color='yellow', linewidth=widthn, label='avg_10', \ markersize=mksize, markeredgecolor='yellow', markeredgewidth=mkwidth, alpha=alpha) # 10日均线 if u'30日均' in quotes: rarray_30dayave= numpy.array(self._average30) axes.plot(xindex, rarray_30dayave, 'o-', color='cyan', linewidth=widthn, label='avg_30', \ markersize=mksize, markeredgecolor='cyan', markeredgewidth=mkwidth, alpha=alpha) # 30日均线 if u'60日均' in quotes: rarray_60dayave= numpy.array(self._average60) axes.plot(xindex, rarray_60dayave, 'o-', color='magenta', linewidth=widthn, label='avg_60', \ markersize=mksize, markeredgecolor='magenta', markeredgewidth=mkwidth, alpha=alpha) # 60日均线 def plot_adjustnotes(self): ''' 绘制复权提示 ''' quotes= self._pdata[u'行情'] firstday= self._dates[0] lastday= self._dates[-1] ylowlim= self._ylowlim yhighlim= self._yhighlim axes= self._Axes # 使用 annotate() 进行标注。不用了,留作纪念。 #=========================================================================================================================== # adjdict= dict(quotes[u'相对复权']) # key 是 date string,value 是相对复权因子(float 类型) # el= Ellipse((2, -1), 0.5, 0.5) # for idx, dstr in enumerate(self._dates): # if dstr in adjdict: # axes.plot([idx, idx], [ylowlim, yhighlim], '-', color='purple', linewidth=0.1) # axes.annotate(u'复权\n' + str(adjdict[dstr]), # fontproperties=__font_properties__, # xy=(idx, yhighlim/1.1), xycoords='data', # xytext=(10, 5), textcoords='offset points', size=5, verticalalignment="center", # bbox=dict(boxstyle="round", facecolor='white', edgecolor='purple'), # arrowprops=dict(arrowstyle="wedge,tail_width=1.", # facecolor='white', edgecolor='purple', # patchA=None, # patchB=el, # relpos=(0.2, 0.8), # connectionstyle="arc3,rad=-0.1"), # alpha=0.5 # ) adjrecs= [rec for rec in quotes[u'相对复权'] if rec[0] >= firstday and rec[0] <= lastday] if self._name == 'pricefs': fsize= 3.0 ycoord= yhighlim/1.3 alpha= 1.0 else: fsize= 5.0 ycoord= yhighlim/1.12 alpha= 1.0 for dstr, afac in adjrecs: idx= self._dates.index(dstr) axes.plot([idx, idx], [ylowlim, yhighlim], '-', color='purple', linewidth=0.1) label= axes.text( \ idx, ycoord, \ u'复权 ' + str(afac) + u'\n' + dstr, \ fontproperties=__font_properties__, \ horizontalalignment='left', \ verticalalignment='top', \ color='purple', \ alpha=alpha ) label.set_fontsize(fsize) def plot_capchangenotes(self): ''' 绘制流通股本变更提示 注意两个问题: 1. 流通股本变更提示中的日期可能不是交易日期 2. 流通股本变更提示涵盖个股的所有历史,有些内容可能在绘图目标区间以外 ''' pdata= self._pdata axes= self._Axes ylowlim= self._ylowlim yhighlim= self._yhighlim firstday= self._dates[0] lastday= self._dates[-1] # 把目标区间之外的记录滤掉 changerecs= [rec for rec in pdata[u'公司信息'][u'流通股变更'] if rec[u'变更日期'] >= firstday and rec[u'变更日期'] <= lastday] # 使用 annotate() 进行标注。不用了,留作纪念。 #=========================================================================================================================== # el= Ellipse((2, -1), 0.5, 0.5) # for datestr, chrate in changerecs: # dstr= [ds for ds in self._dates if ds >= datestr][0] # idx= self._dates.index(dstr) # axes.plot([idx, idx], [ylowlim, yhighlim], '-', color='yellow', linewidth=0.1) # axes.annotate(u'流通股\n' + str(chrate), # fontproperties=__font_properties__, # xy=(idx, yhighlim/1.1), xycoords='data', # xytext=(10, 5), textcoords='offset points', size=5, verticalalignment="center", # bbox=dict(boxstyle="round", facecolor='white', edgecolor='yellow'), # arrowprops=dict(arrowstyle="wedge,tail_width=1.", # facecolor='white', edgecolor='yellow', # patchA=None, # patchB=el, # relpos=(0.2, 0.8), # connectionstyle="arc3,rad=-0.1"), # alpha=0.5 # ) if self._name == 'pricefs': fsize= 3.0 ycoord= yhighlim/1.1 alpha= 1.0 else: fsize= 5.0 ycoord= yhighlim/1.05 alpha= 0.8 for record in changerecs: datestr= record[u'变更日期'] chrate= record[u'变更比'] dstr= [ds for ds in self._dates if ds >= datestr][0] idx= self._dates.index(dstr) axes.plot([idx, idx], [ylowlim, yhighlim], '-', color='yellow', linewidth=0.1) label= axes.text( \ idx, ycoord, \ u'流通股 ' + str(chrate) + u'\n' + datestr, \ fontproperties=__font_properties__, \ horizontalalignment='left', \ verticalalignment='top', \ color='yellow', \ alpha=alpha ) label.set_fontsize(fsize) def plot_candlestick_2(self): ''' 绘制第二条 K 线 ''' xindex= self._xindex axes= self._Axes_2 up= self._up_2 down= self._down_2 side= self._side_2 # 对开收盘价进行视觉修正 for idx, poc in enumerate( self._zipoc_2 ): if poc[0] == poc[1] and None not in poc: self._open_2[idx]= poc[0] - 5 # 稍微偏离一点,使得在图线上不致于完全看不到 self._close_2[idx]= poc[1] + 5 rarray_open= numpy.array(self._open_2) rarray_close= numpy.array(self._close_2) rarray_high= numpy.array(self._high_2) rarray_low= numpy.array(self._low_2) # XXX: 如果 up, down, side 里有一个全部为 False 组成,那么 vlines() 会报错。 # XXX: 可以使用 alpha 参数调节透明度 if True in up: axes.vlines(xindex[up], rarray_low[up], rarray_high[up], edgecolor='0.7', linewidth=0.6, label='_nolegend_', alpha=0.5) axes.vlines(xindex[up], rarray_open[up], rarray_close[up], edgecolor='0.7', linewidth=3.0, label='_nolegend_', alpha=0.5) if True in down: axes.vlines(xindex[down], rarray_low[down], rarray_high[down], edgecolor='0.3', linewidth=0.6, label='_nolegend_', alpha=0.5) axes.vlines(xindex[down], rarray_open[down], rarray_close[down], edgecolor='0.3', linewidth=3.0, label='_nolegend_', alpha=0.5) if True in side: axes.vlines(xindex[side], rarray_low[side], rarray_high[side], edgecolor='1.0', linewidth=0.6, label='_nolegend_', alpha=1.0) axes.vlines(xindex[side], rarray_open[side], rarray_close[side], edgecolor='1.0', linewidth=3.0, label='_nolegend_', alpha=1.0) def plot_capitalinfo(self): ''' 绘制行情首日和尾日的股本信息 ''' def find_biggestblank(didx): ''' 找出 X 轴某个位置图中最大的空隙 ''' pstart= self._open[0] ptarget= self._open[didx] compseq= [yhighlim, ptarget, ylowlim] if u'开盘二' in quotes: pstart_2= self._open_2[0] ptarget_2= self._open_2[didx] padjust= int(ptarget_2 * pstart / float(pstart_2)) compseq.append(padjust) compseq.sort(reverse=True) # 图中的三个或四个关键点,高到底排序 diff, hi, low= max([(math.log(compseq[idx]/float(compseq[idx+1]), expbase), compseq[idx], compseq[idx+1]) for idx in range(len(compseq)-1)]) # XXX: for debugging # print(compseq) # print([diff, hi, low]) return (hi*low)**0.5 # 相乘再开平方,在 log 坐标系里看起来就是在中间位置。 def repr_int(intnum): ''' 123456789 --> '1,2345,6789' ''' if type(intnum) not in (int, long): return str(intnum) if intnum == 0: return '0' if intnum < 0: intnum= -intnum isminus= True else: isminus= False intstr= str(intnum) intstr= '0'*((4-len(intstr)%4)%4) + intstr intlist= [intstr[i:i+4] for i in range(0, len(intstr), 4)] intlist[0]= intlist[0].lstrip('0') return ('-' + ','.join(intlist)) if isminus else ','.join(intlist) pdata= self._pdata quotes= pdata[u'行情'] ylowlim= self._ylowlim yhighlim= self._yhighlim length= self._length expbase= self._expbase capinfo= pdata[u'公司信息'][u'股本变更记录'] axes= self._Axes firstday, lastday= self._dates[0], self._dates[-1] fsize= 5.0 if self._name == 'price' else 3.0 # 首日总股本与流通股信息 #==================================================================================================================================== chunk= [rec for rec in capinfo if rec[u'变更日期'] <= firstday] if chunk: allshares= repr_int(chunk[-1][u'总股本']) circulating= repr_int(chunk[-1][u'流通股']) else: allshares= 'None' circulating= 'None' infostr= u'总股本: ' + allshares + '\n' + u'流通股: ' + circulating label= axes.text(0, find_biggestblank(didx=0), infostr, fontproperties=__font_properties__, color='0.7') label.set_fontsize(fsize) # label.set_zorder(0) # XXX: 放在底层 # 尾日总股本与流通股信息 #==================================================================================================================================== chunk= [rec for rec in capinfo if rec[u'变更日期'] <= lastday] if chunk: allshares= repr_int(chunk[-1][u'总股本']) circulating= repr_int(chunk[-1][u'流通股']) else: allshares= 'None' circulating= 'None' infostr= u'总股本: ' + allshares + '\n' + u'流通股: ' + circulating label= axes.text(length-1, find_biggestblank(didx=length-1), infostr, fontproperties=__font_properties__, horizontalalignment='right', color='0.7') label.set_fontsize(fsize) # label.set_zorder(0) # XXX: 放在底层 def plot_usernotes(self): ''' ''' pdata= self._pdata quotes= pdata[u'行情'] sidx= self._pdata[u'任务描述'][u'起始偏移'] eidx= self._pdata[u'任务描述'][u'结束偏移'] axes= self._Axes usernotes= pdata[u'用户标记'] alldates= quotes[u'日期'][sidx:eidx] lowprices= quotes[u'最低'][sidx:eidx] expbase= self._expbase # 绘制短线买点标记 for note in usernotes: if note[u'类型'] == u'筛选结果': dstr= note[u'日期'] # 日期不在绘图区间范围内,忽略 if dstr not in alldates: continue # 决定箭头的颜色 result= note[u'结果'] color= 'magenta' if result == u'上涨' else 'cyan' if result == u'下跌' else 'white' # 箭头的起始位置 idx= alldates.index(dstr) xpos= idx ypos= lowprices[idx] / expbase # 箭头的长度 dx= 0.0 dy= ypos * (expbase-1) * 0.9 # 头端的长度 head_length= dy * 0.2 # 绘制箭头 arrow_params={'length_includes_head':True, 'shape':'full', 'head_starts_at_zero':False} axes.arrow(xpos, ypos, dx, dy, facecolor=color, edgecolor=color, linewidth=0.7, head_width=0.6, head_length=head_length, **arrow_params) def plot_vlines(self): xindex= self._xindex up= self._up down= self._down side= self._side axes= self._Axes lwidth= 2.4 * self._shrink # 绘制 K 线部分 #================================================================================================================================================== rarray_high= numpy.array(self._high) rarray_low= numpy.array(self._low) # XXX: 如果 up, down, side 里有一个全部为 False 组成,那么 vlines() 会报错。 # XXX: 可以使用 alpha 参数调节透明度 if True in up: axes.vlines(xindex[up], rarray_low[up], rarray_high[up], edgecolor='red', linewidth=lwidth, label='_nolegend_', alpha=0.5) if True in down: axes.vlines(xindex[down], rarray_low[down], rarray_high[down], edgecolor='green', linewidth=lwidth, label='_nolegend_', alpha=0.5) if True in side: axes.vlines(xindex[side], rarray_low[side], rarray_high[side], edgecolor='0.7', linewidth=lwidth, label='_nolegend_', alpha=0.5) def plot_vlines_2(self): xindex= self._xindex axes= self._Axes_2 up= self._up_2 down= self._down_2 side= self._side_2 lwidth= 2.4 * self._shrink rarray_high= numpy.array(self._high_2) rarray_low= numpy.array(self._low_2) # XXX: 如果 up, down, side 里有一个全部为 False 组成,那么 vlines() 会报错。 # XXX: 可以使用 alpha 参数调节透明度 if True in up: axes.vlines(xindex[up], rarray_low[up], rarray_high[up], edgecolor='0.7', linewidth=lwidth, label='_nolegend_', alpha=0.5) if True in down: axes.vlines(xindex[down], rarray_low[down], rarray_high[down], edgecolor='0.3', linewidth=lwidth, label='_nolegend_', alpha=0.5) if True in side: axes.vlines(xindex[side], rarray_low[side], rarray_high[side], edgecolor='1.0', linewidth=lwidth, label='_nolegend_', alpha=1.0) def plot_datenotes(self): ''' 内部的日期注释,由派生类定义 ''' pass def plot_pricenotes(self): ''' 内部的价格注释,由派生类定义 ''' pass class SubPlot_PriceFullSpan(SubPlot_PriceBase): ''' ''' def plot(self): ''' 绘图 ''' pdata= self._pdata # if u'简化' in pdata: # self.plot_simplified() # else: # self.plot_candlestick() self.plot_vlines() self.plot_average() if u'相对复权' in pdata[u'行情']: self.plot_adjustnotes() if u'流通股变更' in pdata[u'公司信息']: self.plot_capchangenotes() if u'股本变更记录' in pdata[u'公司信息']: self.plot_capitalinfo() self.set_xticks() self.set_yticks() if u'开盘二' in pdata[u'行情']: self.plot_vlines_2() self.set_xticks_2() self.set_yticks_2() self.plot_datenotes() if 'price' in self._parent._subplots: self.plot_windowspan() def plot_datenotes(self): ''' 日期在图中间的显示 ''' ylowlim= self._ylowlim axes= self._Axes sdindex= self._xparams['sdindex'] mdindex= self._xparams['mdindex'] # 每季度第一个交易日 for ix in sdindex: newlab= axes.text(ix - (1/self._shrink), ylowlim*1.03, self._dates[ix]) newlab.set_font_properties(__font_properties__) newlab.set_color('0.3') newlab.set_fontsize(4) newlab.set_rotation('45') # newlab.set_rotation('vertical') # newlab.set_horizontalalignment('left') # newlab.set_verticalalignment('bottom') # newlab.set_verticalalignment('center') newlab.set_zorder(0) # XXX: 放在底层 # 每月第一个交易日 for ix in mdindex: newlab= axes.text(ix - (0.8/self._shrink), ylowlim * 1.03, self._dates[ix]) newlab.set_font_properties(__font_properties__) newlab.set_color('0.3') newlab.set_fontsize(3) newlab.set_rotation('45') # newlab.set_rotation('vertical') # newlab.set_horizontalalignment('left') # newlab.set_verticalalignment('top') # 不行 # newlab.set_verticalalignment('center') # newlab.set_verticalalignment('bottom') newlab.set_zorder(0) # XXX: 放在底层 def plot_windowspan(self): axes= self._Axes jobstat= self._pdata[u'任务描述'] sindex, eindex= jobstat[u'起始偏移'], jobstat[u'结束偏移'] hibdry, lobdry= self._parent._subplots['price'].get_ylimits() xcoord= sindex - 1 width= eindex - sindex + 1 ycoord= lobdry height= hibdry - lobdry window= matplotlib.patches.Rectangle((xcoord, ycoord), width, height, fill=False, edgecolor=__color_lightblue__, linewidth=0.3, alpha=0.7) window.set_zorder(-1) # 放在底层 axes.add_patch(window) class SubPlot_Price(SubPlot_PriceBase): ''' ''' def plot(self): ''' 绘图 ''' pdata= self._pdata # if u'简化' in pdata: # self.plot_simplified() # else: # self.plot_candlestick() self.plot_candlestick() self.plot_average() if u'相对复权' in pdata[u'行情']: self.plot_adjustnotes() if u'流通股变更' in pdata[u'公司信息']: self.plot_capchangenotes() if u'股本变更记录' in pdata[u'公司信息']: self.plot_capitalinfo() if u'用户标记' in pdata: self.plot_usernotes() self.set_xticks() self.set_yticks() if u'开盘二' in pdata[u'行情']: self.plot_candlestick_2() self.set_xticks_2() self.set_yticks_2() self.plot_pricenotes() self.plot_datenotes() def plot_datenotes(self): ''' 日期在图中间的显示 ''' ylowlim= self._ylowlim yhighlim= self._yhighlim axes= self._Axes mdindex= self._xparams['mdindex'] wdindex= self._xparams['wdindex'] # 每月第一个交易日 for iy in [ylowlim*1.1, yhighlim/1.21]: for ix in mdindex: newlab= axes.text(ix-1, iy, self._dates[ix]) newlab.set_font_properties(__font_properties__) newlab.set_color('0.3') newlab.set_fontsize(4) newlab.set_rotation('vertical') # newlab.set_horizontalalignment('left') # newlab.set_verticalalignment('bottom') # newlab.set_verticalalignment('center') newlab.set_zorder(0) # XXX: 放在底层 # 每周第一个交易日,根据这个可以推算出全部确切的日期。 # for iy in minorticks[0:-1:6]: for iy in [ylowlim*1.01, yhighlim/1.09]: for ix in wdindex: newlab= axes.text(ix-0.8, iy, self._dates[ix]) newlab.set_font_properties(__font_properties__) newlab.set_color('0.3') newlab.set_fontsize(3) newlab.set_rotation('vertical') # newlab.set_horizontalalignment('left') # newlab.set_verticalalignment('top') # 不行 # newlab.set_verticalalignment('center') # newlab.set_verticalalignment('bottom') newlab.set_zorder(0) # XXX: 放在底层 def plot_pricenotes(self): # 价格数值在图中间的显示 #================================================================================================================================================== quotes= self._pdata[u'行情'] axes= self._Axes majorticks= self._ytickset['major'] minorticks= self._ytickset['minor'] mdindex= self._xparams['mdindex'] def price_note(num): return str(round(num/1000.0, 2)) if u'开盘二' in quotes: majorticks_2= self._ytickset['major_2'] minorticks_2= self._ytickset['minor_2'] for iy, iy2 in zip(sorted(majorticks[:-1] + minorticks[1:-1]), sorted(majorticks_2[:-1] + minorticks_2[1:-1])): for ix in mdindex[1:-1:3]: newlab= axes.text(ix+6, iy*1.001, price_note(iy) + ' / ' + price_note(iy2)) newlab.set_font_properties(__font_properties__) newlab.set_color('0.3') newlab.set_fontsize(3) newlab.set_zorder(0) # XXX: 放在底层 else: for iy in sorted(majorticks[:-1] + minorticks[1:-1]): for ix in mdindex[1:-1:3]: newlab= axes.text(ix+9, iy*1.001, price_note(iy)) newlab.set_font_properties(__font_properties__) newlab.set_color('0.3') newlab.set_fontsize(3) newlab.set_zorder(0) # XXX: 放在底层 class SubPlot_TORateBase: ''' 换手率子图 ''' def __init__(self, pdata, parent, xparams, name): self._name= name self._pdata= pdata self._parent= parent self._xparams= xparams self._shrink= __shrink__ if name == 'toratefs' else 1.0 self._tostep= 0 # 每一格代表的换手率数值 self._yrange= 0 self._xsize= 0 # int self._ysize= 0 # int self._Axes= None self._AxisX= None self._AxisY= None if u'换手率二' in pdata[u'行情']: self._Axes_2= None self._AxisX_2= None self._AxisY_2= None self._tostep_2= 0 # 绘图数据 quotes= pdata[u'行情'] if name == 'toratefs': self._dates= quotes[u'日期'] self._open= quotes[u'开盘'] self._close= quotes[u'收盘'] self._high= quotes[u'最高'] self._low= quotes[u'最低'] if u'简化' in quotes: self._simple= quotes[u'简化'] if u'换手率' in quotes: self._torate= quotes[u'换手率'] if u'成交量' in quotes: self._volume= quotes[u'成交量'] if u'成交额' in quotes: self._turnover= quotes[u'成交额'] if u'开盘二' in quotes: self._open_2= quotes[u'开盘二'] self._close_2= quotes[u'收盘二'] self._high_2= quotes[u'最高二'] self._low_2= quotes[u'最低二'] if u'简化二' in quotes: self._simple_2= quotes[u'简化二'] if u'换手率二' in quotes: self._torate_2= quotes[u'换手率二'] if u'成交量二' in quotes: self._volume_2= quotes[u'成交量二'] if u'成交额二' in quotes: self._turnover_2= quotes[u'成交额二'] else: sidx, eidx= pdata[u'任务描述'][u'起始偏移'], pdata[u'任务描述'][u'结束偏移'] self._dates= quotes[u'日期'][sidx:eidx] self._open= quotes[u'开盘'][sidx:eidx] self._close= quotes[u'收盘'][sidx:eidx] self._high= quotes[u'最高'][sidx:eidx] self._low= quotes[u'最低'][sidx:eidx] if u'简化' in quotes: self._simple= quotes[u'简化'][sidx:eidx] if u'换手率' in quotes: self._torate= quotes[u'换手率'][sidx:eidx] if u'成交量' in quotes: self._volume= quotes[u'成交量'][sidx:eidx] if u'成交额' in quotes: self._turnover= quotes[u'成交额'][sidx:eidx] if u'开盘二' in quotes: self._open_2= quotes[u'开盘二'][sidx:eidx] self._close_2= quotes[u'收盘二'][sidx:eidx] self._high_2= quotes[u'最高二'][sidx:eidx] self._low_2= quotes[u'最低二'][sidx:eidx] if u'简化二' in quotes: self._simple_2= quotes[u'简化二'][sidx:eidx] if u'换手率二' in quotes: self._torate_2= quotes[u'换手率二'][sidx:eidx] if u'成交量二' in quotes: self._volume_2= quotes[u'成交量二'][sidx:eidx] if u'成交额二' in quotes: self._turnover_2= quotes[u'成交额二'][sidx:eidx] # 衍生数据 #============================================================================================================== self._length= len(self._dates) self._xindex= numpy.arange(self._length) # X 轴上的 index,一个辅助数据 self._zipoc= zip(self._open, self._close) self._up= numpy.array( [ True if po < pc and po is not None else False for po, pc in self._zipoc] ) # 标示出该天股价日内上涨的一个序列 self._down= numpy.array( [ True if po > pc and po is not None else False for po, pc in self._zipoc] ) # 标示出该天股价日内下跌的一个序列 self._side= numpy.array( [ True if po == pc and po is not None else False for po, pc in self._zipoc] ) # 标示出该天股价日内走平的一个序列 if u'开盘二' in quotes: self._zipoc_2= zip(self._open_2, self._close_2) self._up_2= numpy.array( [ True if po < pc and po is not None else False for po, pc in self._zipoc_2] ) # 标示出该天股价日内上涨的一个序列 self._down_2= numpy.array( [ True if po > pc and po is not None else False for po, pc in self._zipoc_2] ) # 标示出该天股价日内下跌的一个序列 self._side_2= numpy.array( [ True if po == pc and po is not None else False for po, pc in self._zipoc_2] ) # 标示出该天股价日内走平的一个序列 self._compute_size() def _compute_size(self): ''' 根据 pdata 计算自身尺寸 ''' def _compute_step(maxto): ''' maxto 是 换手率 最大值。返回每格单位(最小 500, 代表 0.5%)以及格数 ''' for i in range(9): if maxto > (4 * 500 * (2**i)): # 换手率最大是 100000, 代表 100% continue else: tostep= 500 * (2**i) tosize= int(round((maxto + tostep/2.0 - 1) / float(tostep), 0)) break return (tostep, tosize) quotes= self._pdata[u'行情'] xmargin= self._xparams['xmargin'] self._xsize= (self._length + xmargin*2) * self._shrink maxto= max(self._torate) self._tostep, self._yrange= _compute_step(maxto=maxto) if u'换手率二' in quotes: maxto_2= max(self._torate_2) self._tostep_2, yrange_2= _compute_step(maxto=maxto_2) self._yrange= max(self._yrange, yrange_2) # 成交量部分在 Y 轴所占的 “份数” self._ysize= self._yrange * self._shrink def get_size(self): return (self._xsize, self._ysize) def build_axes(self, figobj, rect): # 第一只:添加 Axes 对象 #================================================================================================================================================== axes= figobj.add_axes(rect, axis_bgcolor='black') axes.set_axis_bgcolor('black') axes.set_axisbelow(True) # 网格线放在底层 # 第一只:改变坐标线的颜色 #================================================================================================================================================== for child in axes.get_children(): if isinstance(child, matplotlib.spines.Spine): child.set_color(__color_gold__) # child.set_zorder(3) # XXX: 放在上层,好像没什么用。 # 得到 X 轴 和 Y 轴 的两个 Axis 对象 #================================================================================================================================================== xaxis= axes.get_xaxis() yaxis= axes.get_yaxis() # 设置两个坐标轴上的 grid #================================================================================================================================================== xaxis.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.2) xaxis.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1) yaxis.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.2) yaxis.grid(True, 'minor', color='0.3', linestyle='solid', linewidth=0.1) self._Axes= axes self._AxisX= xaxis self._AxisY= yaxis if u'换手率二' in self._pdata[u'行情']: # 添加 Axes 对象 #================================================================================================================================================== axes_2= axes.twinx() # XXX: 下面这三行把第一个 axes 放在上面,这样不会被第二个 axes 的图形遮盖。用 zorder 不顶用。 axes.figure.axes[-2:]= [axes_2, axes] # XXX: 把第一个 axes 放在上面,用 zorder 不顶用。 axes.set_frame_on(False) # 如果不做此设定,axes_2 的内容会看不见 axes_2.set_frame_on(True) axes_2.set_axis_bgcolor('black') axes_2.set_axisbelow(True) # 网格线放在底层 # 改变坐标线的颜色 #================================================================================================================================================== for child in axes_2.get_children(): if isinstance(child, matplotlib.spines.Spine): child.set_color(__color_gold__) # 得到 X 轴 和 Y 轴 的两个 Axis 对象 #================================================================================================================================================== xaxis_2= axes_2.get_xaxis() yaxis_2= axes_2.get_yaxis() # 设置网格线 #================================================================================================================================================== # xaxis_2.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.2) # xaxis_2.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1) # yaxis_2.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.2) # yaxis_2.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1) self._Axes_2= axes_2 self._AxisX_2= xaxis_2 self._AxisY_2= yaxis_2 def get_axes(self): return self._Axes def plot(self): ''' 绘制换手率图形 ''' self.plot_torate() self.set_xticks() self.set_yticks() if u'换手率二' in self._pdata[u'行情']: self.plot_torate_2() self.set_xticks_2() self.set_yticks_2() def plot_torate(self): ''' 绘制换手率 ''' xindex= self._xindex stopset= self._xparams['mdindex'] if self._name == 'torate' else self._xparams['sdindex'] axes= self._Axes up= self._up down= self._down side= self._side rarray_to= numpy.array(self._torate) tozeros= numpy.zeros(self._length) # 辅助数据 lwidth= 3.0 if self._name == 'torate' else 2.4 * self._shrink # XXX: 如果 up/down/side 各项全部为 False,那么 vlines() 会报错。 if True in up: axes.vlines(xindex[up], tozeros[up], rarray_to[up], edgecolor='red', linewidth=lwidth, label='_nolegend_', alpha=0.5) if True in down: axes.vlines(xindex[down], tozeros[down], rarray_to[down], edgecolor='green', linewidth=lwidth, label='_nolegend_', alpha=0.5) if True in side: axes.vlines(xindex[side], tozeros[side], rarray_to[side], edgecolor='0.7', linewidth=lwidth, label='_nolegend_', alpha=0.5) # 绘制平均换手率(直线) toeffect= [num for num in self._torate if num is not None] toaverage= sum(toeffect) / float(len(toeffect)) axes.plot([-1, self._length], [toaverage, toaverage], '-', color='yellow', linewidth=0.2, alpha=0.7) # 换手率数值在图中间的显示 #================================================================================================================================================== for ix in stopset[2:-1:3]: newlab= axes.text(ix+8, toaverage, str(round(toaverage/1000.0, 2)) + '%') newlab.set_font_properties(__font_properties__) newlab.set_color('yellow') newlab.set_fontsize(3) # newlab.set_zorder(0) # XXX: 放在底层 # newlab.set_verticalalignment('center') def plot_torate_2(self): ''' 绘制第二条换手率柱状图 ''' quotes= self._pdata[u'行情'] xindex= self._xindex axes= self._Axes_2 up= self._up_2 down= self._down_2 side= self._side_2 rarray_to= numpy.array(self._torate_2) tozeros= numpy.zeros(self._length) # 辅助数据 lwidth, alpha= (0.7, 0.5) if self._name == 'torate' else (0.3, 0.7) # XXX: 如果 up/down/side 各项全部为 False,那么 vlines() 会报错。 if True in up: axes.vlines(xindex[up], tozeros[up], rarray_to[up], edgecolor='0.7', linewidth=lwidth, label='_nolegend_', alpha=alpha) if True in down: axes.vlines(xindex[down], tozeros[down], rarray_to[down], edgecolor='0.3', linewidth=lwidth, label='_nolegend_', alpha=alpha) if True in side: axes.vlines(xindex[side], tozeros[side], rarray_to[side], edgecolor='0.7', linewidth=lwidth, label='_nolegend_', alpha=1.0) def set_xticks(self): ''' X 轴坐标 ''' length= self._length xmargin= self._xparams['xmargin'] axes= self._Axes xaxis= self._AxisX # xaxis.set_tick_params(which='both', direction='out') # XXX: 坐标点设到外面去,也可以用 Axes.tick_params(),好像 matplotlib 1.0.1 才有 # 设定 X 轴坐标的范围 #================================================================================================================================================== axes.set_xlim(-xmargin, length + xmargin) xMajorLocator= self._xparams['xMajorLocator'] xMinorLocator= self._xparams['xMinorLocator'] xMajorFormatter= self._xparams['xMajorFormatter'] xMinorFormatter= self._xparams['xMinorFormatter'] # 设定 X 轴的 Locator 和 Formatter xaxis.set_major_locator(xMajorLocator) xaxis.set_minor_locator(xMinorLocator) if self._name == 'torate': xaxis.set_major_formatter(xMajorFormatter) xaxis.set_minor_formatter(xMinorFormatter) # 设定 X 轴主要坐标点与辅助坐标点的样式 for mal in axes.get_xticklabels(minor=False): mal.set_fontsize(4) mal.set_horizontalalignment('right') mal.set_rotation('45') for mil in axes.get_xticklabels(minor=True): mil.set_fontsize(4) mil.set_color('blue') mil.set_horizontalalignment('right') mil.set_rotation('45') else: # 设为不可见 for mal in axes.get_xticklabels(minor=False): mal.set_visible(False) for mil in axes.get_xticklabels(minor=True): mil.set_visible(False) def set_xticks_2(self): length= self._length xmargin= self._xparams['xmargin'] axes= self._Axes_2 xaxis= self._AxisX_2 # xaxis.set_tick_params(which='both', direction='out') # XXX: 坐标点设到外面去,也可以用 Axes.tick_params(),好像 matplotlib 1.0.1 才有 # 设定 X 轴坐标的范围 #================================================================================================================================================== axes.set_xlim(-xmargin, length + xmargin) xMajorLocator= self._xparams['xMajorLocator'] xMinorLocator= self._xparams['xMinorLocator'] # 设定 X 轴的 Locator 和 Formatter xaxis.set_major_locator(xMajorLocator) xaxis.set_minor_locator(xMinorLocator) # 设为不可见 for mal in axes.get_xticklabels(minor=False): mal.set_visible(False) for mil in axes.get_xticklabels(minor=True): mil.set_visible(False) def set_yticks(self): ''' 设置 Y 轴坐标 ''' axes= self._Axes yaxis= self._AxisY tostep= self._tostep yrange= self._yrange stopset= self._xparams['mdindex'] if self._name == 'torate' else self._xparams['sdindex'] # 设定换手率 Y 轴坐标的范围 #================================================================================================================================================== axes.set_ylim(0, tostep*yrange) # 主要坐标点 #================================================================================================================================================== majorticks= [tostep*i for i in range(yrange)] yMajorLocator= FixedLocator(numpy.array(majorticks)) # 确定 Y 轴的 MajorFormatter def y_major_formatter(num, pos=None): return str(round(num/1000.0, 2)) + '%' yMajorFormatter= FuncFormatter(y_major_formatter) # 确定 Y 轴的 MinorFormatter yMinorFormatter= NullFormatter() # 第一只:设定 X 轴的 Locator 和 Formatter yaxis.set_major_locator(yMajorLocator) yaxis.set_major_formatter(yMajorFormatter) # 设定 Y 轴主要坐标点的样式 for mal in axes.get_yticklabels(minor=False): mal.set_font_properties(__font_properties__) mal.set_fontsize(5) # 这个必须放在前一句后面,否则作用会被覆盖 # 辅助坐标点 #================================================================================================================================================== if self._name == 'torate': minorticks= list( itertools.chain.from_iterable( mi for mi in [[ma + (tostep/4.0)*i for i in range(1, 4)] for ma in majorticks] ) ) yMinorLocator= FixedLocator(numpy.array(minorticks)) yaxis.set_minor_locator(yMinorLocator) def y_minor_formatter(num, pos=None): return str(round(num/1000.0, 3)) + '%' yMinorFormatter= FuncFormatter(y_minor_formatter) yaxis.set_minor_formatter(yMinorFormatter) # 设定 Y 轴主要坐标点的样式 for mil in axes.get_yticklabels(minor=True): mil.set_font_properties(__font_properties__) mil.set_fontsize(4) # 这个必须放在前一句后面,否则作用会被覆盖 else: # minorticks= list( itertools.chain.from_iterable( mi for mi in [[ma + (tostep/4.0)*i for i in range(1, 4)] for ma in majorticks] ) ) minorticks= list( [ma + (tostep/2.0) for ma in majorticks] ) yMinorLocator= FixedLocator(numpy.array(minorticks)) yaxis.set_minor_locator(yMinorLocator) # 设定 Y 轴主要坐标点的样式 for mil in axes.get_yticklabels(minor=True): mil.set_visible(False) # 换手率数值在图中间的显示 #================================================================================================================================================== for iy in range(int(tostep/2.0), tostep*yrange, int(tostep/2.0)): for ix in stopset[1:-1:3]: newlab= axes.text(ix+8, iy, y_major_formatter(iy)) newlab.set_font_properties(__font_properties__) newlab.set_color('0.3') newlab.set_fontsize(3) newlab.set_zorder(0) # XXX: 放在底层 # newlab.set_verticalalignment('center') def set_yticks_2(self): ''' 设置 Y 轴坐标 ''' axes= self._Axes_2 yaxis= self._AxisY_2 tostep= self._tostep_2 yrange= self._yrange # 与 1 是一样的 # 设定换手率 Y 轴坐标的范围 #================================================================================================================================================== axes.set_ylim(0, tostep*yrange) # 主要坐标点 #================================================================================================================================================== majorticks= [tostep*i for i in range(yrange)] yMajorLocator= FixedLocator(numpy.array(majorticks)) # 确定 Y 轴的 MajorFormatter def y_major_formatter(num, pos=None): return str(round(num/1000.0, 2)) + '%' yMajorFormatter= FuncFormatter(y_major_formatter) # 确定 Y 轴的 MinorFormatter yMinorFormatter= NullFormatter() # 第一只:设定 X 轴的 Locator 和 Formatter yaxis.set_major_locator(yMajorLocator) yaxis.set_major_formatter(yMajorFormatter) # 设定 Y 轴主要坐标点的样式 for mal in axes.get_yticklabels(minor=False): mal.set_font_properties(__font_properties__) mal.set_fontsize(5) # 这个必须放在前一句后面,否则作用会被覆盖 # 辅助坐标点 #================================================================================================================================================== if self._name == 'torate': minorticks= list( itertools.chain.from_iterable( mi for mi in [[ma + (tostep/4.0)*i for i in range(1, 4)] for ma in majorticks] ) ) yMinorLocator= FixedLocator(numpy.array(minorticks)) def y_minor_formatter(num, pos=None): return str(round(num/1000.0, 3)) + '%' yMinorFormatter= FuncFormatter(y_minor_formatter) yaxis.set_minor_locator(yMinorLocator) yaxis.set_minor_formatter(yMinorFormatter) # 设定 Y 轴主要坐标点的样式 for mil in axes.get_yticklabels(minor=True): mil.set_font_properties(__font_properties__) mil.set_fontsize(4) # 这个必须放在前一句后面,否则作用会被覆盖 else: minorticks= list( [ma + (tostep/2.0) for ma in majorticks] ) yMinorLocator= FixedLocator(numpy.array(minorticks)) yaxis.set_minor_locator(yMinorLocator) # 设定 Y 轴主要坐标点的样式 for mil in axes.get_yticklabels(minor=True): mil.set_visible(False) class SubPlot_TORate(SubPlot_TORateBase): pass class SubPlot_TORateFullSpan(SubPlot_TORateBase): pass class MyFigure: ''' ''' def __init__(self, pdata): self._pdata= pdata # 绘图数据 self._figfacecolor= __color_pink__ self._figedgecolor= __color_navy__ self._figdpi= 300 self._figlinewidth= 1.0 self._xfactor= 10.0 / 230.0 # x size * x factor = x length self._yfactor= 0.3 # y size * y factor = y length jobstat= pdata[u'任务描述'] self._xsize_left= 12.0 # left blank self._xsize_right= 12.0 # right blank self._ysize_top= 0.3 # top blank self._ysize_bottom= 1.2 # bottom blank self._ysize_gap1= 0.2 self._ysize_gap2= 0.3 if (jobstat[u'历史价格子图'] or jobstat[u'历史换手率子图'] or jobstat[u'财务指标子图']) else 0.0 # 建立 X 轴参数 #=============================================================================================================== if jobstat[u'价格子图'] or jobstat[u'换手率子图']: xparams= {'xmargin': 1} xparams.update(self._compute_xparams()) # 与 X 轴坐标点相关的数据结构 if jobstat[u'历史价格子图'] or jobstat[u'历史换手率子图'] or jobstat[u'财务指标子图']: xparams_fs= {'xmargin': 3} xparams_fs.update(self._compute_xparams_fullspan()) # 建立子图对象 #=============================================================================================================== self._subplots= {} if jobstat[u'公司信息子图']: name= 'basic' self._subplots[name]= SubPlot_BasicInfo(pdata=pdata, parent=self, name=name) if jobstat[u'历史价格子图']: # XXX: 这个要放在 价格子图 前面,因为后者可能会用到它的 Y 轴坐标点位置 name= 'pricefs' self._subplots[name]= SubPlot_PriceFullSpan(pdata=pdata, parent=self, xparams=xparams_fs, name=name) if jobstat[u'价格子图']: name= 'price' self._subplots[name]= SubPlot_Price(pdata=pdata, parent=self, xparams=xparams, name=name) if jobstat[u'财务指标子图']: name= 'financial' self._subplots[name]= SubPlot_Financial(pdata=pdata, parent=self, xparams=xparams_fs, name=name) if jobstat[u'换手率子图']: name= 'torate' self._subplots[name]= SubPlot_TORate(pdata=pdata, parent=self, xparams=xparams, name=name) if jobstat[u'历史换手率子图']: name= 'toratefs' self._subplots[name]= SubPlot_TORateFullSpan(pdata=pdata, parent=self, xparams=xparams_fs, name=name) # 根据子图对象的尺寸计算自身的尺寸 #=============================================================================================================== self._xsize, \ self._ysize= self._compute_size() self._xlength= self._xsize * self._xfactor self._ylength= self._ysize * self._yfactor # 根据计算出的尺寸建立 Figure 对象 #=============================================================================================================== self._Fig= pyplot.figure(figsize=(self._xlength, self._ylength), dpi=self._figdpi, facecolor=self._figfacecolor, \ edgecolor=self._figedgecolor, linewidth=self._figlinewidth) # Figure 对象 # 用新建立的 Figure 对象交给子图对象,完成子图对象的初始化 #=============================================================================================================== rects= self._compute_rect() if 'basic' in self._subplots: self._subplots['basic'].build_axes(figobj=self._Fig, rect=rects['basic']) # XXX: 这个要放在 price 前面,因为后者要用到它的 Axes 对象 if 'torate' in self._subplots: self._subplots['torate'].build_axes(figobj=self._Fig, rect=rects['torate']) if 'price' in self._subplots: self._subplots['price'].build_axes(figobj=self._Fig, rect=rects['price']) # XXX: 这个要放在 pricefs 前面 if 'toratefs' in self._subplots: self._subplots['toratefs'].build_axes(figobj=self._Fig, rect=rects['toratefs']) if 'pricefs' in self._subplots: self._subplots['pricefs'].build_axes(figobj=self._Fig, rect=rects['pricefs']) def _compute_size(self): ''' 根据子图的尺寸计算自身尺寸 ''' pdata= self._pdata jobstat= pdata[u'任务描述'] x_left, x_right= self._xsize_left, self._xsize_right y_top, y_bottom= self._ysize_top, self._ysize_bottom y_gap1= self._ysize_gap1 y_gap2= self._ysize_gap2 x_basic, y_basic= self._subplots['basic'].get_size() if 'basic' in self._subplots else (0.0, 0.0) x_price, y_price= self._subplots['price'].get_size() if 'price' in self._subplots else (0.0, 0.0) x_pricefs, y_pricefs= self._subplots['pricefs'].get_size() if 'pricefs' in self._subplots else (0.0, 0.0) x_torate, y_torate= self._subplots['torate'].get_size() if 'torate' in self._subplots else (0.0, 0.0) x_toratefs, y_toratefs= self._subplots['toratefs'].get_size() if 'toratefs' in self._subplots else (0.0, 0.0) x_financial, y_financial= self._subplots['financial'].get_size() if 'financial' in self._subplots else (0.0, 0.0) x_all= x_left + max(x_price, x_basic, x_pricefs) + x_right y_all= y_top + y_basic + y_gap1 + y_pricefs + y_toratefs + y_financial + y_gap2 + y_price + y_torate + y_bottom return (x_all, y_all) def get_sizeset(self): sizeset= { 'x': self._xsize, 'y': self._ysize, 'top': self._ysize_top, 'bottom': self._ysize_bottom, 'left': self._xsize_left, 'right': self._xsize_right } return sizeset def _compute_rect(self): ''' ''' pdata= self._pdata jobstat= pdata[u'任务描述'] x_left= self._xsize_left x_right= self._xsize_right y_top= self._ysize_top y_bottom= self._ysize_bottom x_all= self._xsize y_all= self._ysize y_gap1= self._ysize_gap1 # basic 与 financial 之间的空隙 y_gap2= self._ysize_gap2 # toratefs 与 price 之间的空隙 x_basic, y_basic= self._subplots['basic'].get_size() if 'basic' in self._subplots else (0.0, 0.0) x_price, y_price= self._subplots['price'].get_size() if 'price' in self._subplots else (0.0, 0.0) x_pricefs, y_pricefs= self._subplots['pricefs'].get_size() if 'pricefs' in self._subplots else (0.0, 0.0) x_torate, y_torate= self._subplots['torate'].get_size() if 'torate' in self._subplots else (0.0, 0.0) x_toratefs, y_toratefs= self._subplots['toratefs'].get_size() if 'toratefs' in self._subplots else (0.0, 0.0) x_financial, y_financial= self._subplots['financial'].get_size() if 'financial' in self._subplots else (0.0, 0.0) rects= {} if 'basic' in self._subplots: rect= ((x_left + (x_all-x_left-x_right-x_basic)/2) / x_all, (y_all - y_top - y_basic)/y_all, x_basic/x_all, y_basic/y_all) # K线图部分 rects['basic']= rect if 'price' in self._subplots: rect= ((x_left + (x_all-x_left-x_right-x_price)/2) / x_all, (y_bottom + y_torate)/y_all, x_price/x_all, y_price/y_all) # K线图部分 rects['price']= rect if 'torate' in self._subplots: rect= ((x_left + (x_all-x_left-x_right-x_torate)/2)/x_all, y_bottom/y_all, x_torate/x_all, y_torate/y_all) # 成交量部分 rects['torate']= rect if 'pricefs' in self._subplots: rect= ((x_left + (x_all-x_left-x_right-x_pricefs)/2)/x_all, (y_all - y_top - y_basic - y_gap1 - y_pricefs)/y_all, x_pricefs/x_all, y_pricefs/y_all) rects['pricefs']= rect if 'toratefs' in self._subplots: rect= ((x_left + (x_all-x_left-x_right-x_toratefs)/2)/x_all, (y_bottom + y_torate + y_price + y_gap2)/y_all, x_toratefs/x_all, y_toratefs/y_all) rects['toratefs']= rect return rects def _compute_xparams(self): ''' 主要坐标点是每月第一个交易日,辅助坐标点是每周第一个交易日 ''' quotes= self._pdata[u'行情'] sidx= self._pdata[u'任务描述'][u'起始偏移'] eidx= self._pdata[u'任务描述'][u'结束偏移'] # 设定 X 轴上的坐标 #================================================================================================================================================== datelist= [ datetime.date(int(ys), int(ms), int(ds)) for ys, ms, ds in [ dstr.split('-') for dstr in quotes[u'日期'][sidx:eidx] ] ] # 确定 X 轴的 MajorLocator mdindex= [] # 每个月第一个交易日在所有日期列表中的 index allyears= set([d.year for d in datelist]) # 所有的交易年份 for yr in sorted(allyears): allmonths= set([d.month for d in datelist if d.year == yr]) # 当年所有的交易月份 for mon in sorted(allmonths): monthday= min([dt for dt in datelist if dt.year==yr and dt.month==mon]) # 当月的第一个交易日 mdindex.append(datelist.index(monthday)) xMajorLocator= FixedLocator(numpy.array(mdindex)) # 确定 X 轴的 MinorLocator wdindex= {} # value: 每周第一个交易日在所有日期列表中的 index; key: 当周的序号 week number(当周是第几周) for d in datelist: isoyear, weekno= d.isocalendar()[0:2] dmark= isoyear*100 + weekno if dmark not in wdindex: wdindex[dmark]= datelist.index(d) wdindex= sorted(wdindex.values()) xMinorLocator= FixedLocator(numpy.array(wdindex)) # 确定 X 轴的 MajorFormatter 和 MinorFormatter def x_major_formatter(idx, pos=None): return datelist[idx].strftime('%Y-%m-%d') def x_minor_formatter(idx, pos=None): return datelist[idx].strftime('%m-%d') xMajorFormatter= FuncFormatter(x_major_formatter) xMinorFormatter= FuncFormatter(x_minor_formatter) return {'xMajorLocator': xMajorLocator, 'xMinorLocator': xMinorLocator, 'xMajorFormatter': xMajorFormatter, 'xMinorFormatter': xMinorFormatter, 'mdindex': mdindex, 'wdindex': wdindex } def _compute_xparams_fullspan(self): ''' 主要坐标点是每季第一个交易日,辅助坐标点是每月第一个交易日。是给宏观子图用的。 ''' quotes= self._pdata[u'行情'] datelist= [ datetime.date(int(ys), int(ms), int(ds)) for ys, ms, ds in [ dstr.split('-') for dstr in quotes[u'日期'] ] ] # 确定 X 轴的 MinorLocator mdindex= [] # 每个月第一个交易日在所有日期列表中的 index sdindex= [] # 每季度第一个交易日在所有日期列表中的 index ydindex= [] # 每年第一个交易日在所有日期列表中的 index allyears= set([d.year for d in datelist]) # 所有的交易年份 for yr in sorted(allyears): allmonths= set([d.month for d in datelist if d.year == yr]) # 当年所有的交易月份 for mon in sorted(allmonths): monthday= min([dt for dt in datelist if dt.year==yr and dt.month==mon]) # 当月的第一个交易日 idx= datelist.index(monthday) if mon in (1, 4, 7, 10): sdindex.append(idx) if mon == 1: ydindex.append(idx) else: mdindex.append(idx) xMajorLocator= FixedLocator(numpy.array(sdindex)) xMinorLocator= FixedLocator(numpy.array(mdindex)) # 确定 X 轴的 MajorFormatter 和 MinorFormatter def x_major_formatter(idx, pos=None): return datelist[idx].strftime('%Y-%m-%d') def x_minor_formatter(idx, pos=None): return datelist[idx].strftime('%m-%d') xMajorFormatter= FuncFormatter(x_major_formatter) xMinorFormatter= FuncFormatter(x_minor_formatter) return {'xMajorLocator': xMajorLocator, 'xMinorLocator': xMinorLocator, 'xMajorFormatter': xMajorFormatter, 'xMinorFormatter': xMinorFormatter, 'sdindex': sdindex, 'mdindex': mdindex, 'ydindex': ydindex } def plot(self): ''' ''' # self.plot_title() # 调用子图对象的绘图函数 if 'basic' in self._subplots: self._subplots['basic'].plot() if 'price' in self._subplots: self._subplots['price'].plot() if 'torate' in self._subplots: self._subplots['torate'].plot() if 'pricefs' in self._subplots: self._subplots['pricefs'].plot() if 'toratefs' in self._subplots: self._subplots['toratefs'].plot() def plot_title(self): ''' 绘制整个 Figure 的标题 ''' info= self._pdata[u'公司信息'] figobj= self._Fig # 整个 figure 的标题 subtitle= (info[u'代码'] + ' ' if u'代码' in info else '') + info[u'简称'] subtitle_2= (info[u'代码二'] + ' ' if u'代码二' in info else '') + info[u'简称二'] figobj.suptitle(subtitle + ' / ' + subtitle_2, fontsize=12, fontproperties=__font_properties__) def savefig(self, figpath): ''' 保存图片 ''' self._Fig.savefig(figpath, dpi=self._figdpi, facecolor=self._figfacecolor, edgecolor=self._figedgecolor, linewidth=self._figlinewidth) if __name__ == '__main__': # pfile 指明存放绘图数据的 pickle file,figpath 指定图片需存放的路径 pfile= sys.argv[1] figpath= sys.argv[2] # 绘图数据 pdata fileobj= open(name=pfile, mode='rb') pdata= pickle.load(fileobj) fileobj.close() os.remove(pfile) myfig= MyFigure(pdata=pdata) myfig.plot() myfig.savefig(figpath=figpath)
用 Python / Matplotlib 画出来的股票 K线图 (二)
---- 最新的在这里: 用 Python / Matplotlib 画出来的股票 K线图 (四)
---- 下一篇在这里: 用 Python / Matplotlib 画出来的股票 K线图 (三)
---- 上一版的改进,双股同列 + 无数细小改进,如下图。dpi= 300。明的一条是个股走势,暗的是同期的指数走势。这大概是近期最强的一只。
---- 要想培养对走势的感觉,采用固定比例尺的图形是必须的。一般股票软件里的图形都为显示方便而做了变形处理,用处不大。
---- 图形感觉差不多了,告一段落。接下来的目标是 股本结构、历史分配、行业板块、股东研究 这些信息,还包括个股资讯。实时的数据仍然暂时不碰。
---- 源码贴出来。因为 Matplotlib 还不支持 Python3, 所以单写了一个 Python2 脚本。注意绘图数据是用 pickle file 传递的。
[补记:我决定放弃线性坐标了。这个脚本只支持对数坐标。]
# -*- coding: utf-8 -*- import os import sys import pickle import math import datetime import matplotlib matplotlib.use("WXAgg", warn=True) # 这个要紧跟在 import matplotlib 之后,而且必须安装了 wxpython 2.8 才行。 import matplotlib.pyplot as pyplot import matplotlib.font_manager as font_manager import numpy from matplotlib.ticker import FixedLocator, MultipleLocator, FuncFormatter, NullFormatter __font_properties__=font_manager.FontProperties(fname='/usr/share/fonts/truetype/wqy/wqy-zenhei.ttc') __color_lightsalmon__= '#ffa07a' __color_pink__= '#ffc0cb' __color_navy__= '#000080' def Plot(pfile, figpath): ''' pfile 指明存放绘图数据的 pickle file,figpath 指定图片需存放的路径 ''' fileobj= open(name=pfile, mode='rb') pdata= pickle.load(fileobj) fileobj.close() os.remove(pfile) # 计算图片的尺寸(单位英寸) # 注意:Python2 里面, "1 / 10" 结果是 0, 必须写成 "1.0 / 10" 才会得到 0.1 #================================================================================================================================================== length= len(pdata[u'日期']) # 所有数据的长度,就是天数 open_price_pri= pdata[u'开盘'][0] # int 类型 open_price_sec= pdata[u'开盘二'][0] # 同上 highest_price_pri= max( [phigh for phigh in pdata[u'最高'] if phigh != None] ) # 第一个行情的最高价 highest_price_sec= max( [phigh for phigh in pdata[u'最高二'] if phigh != None] ) # 第二个行情的最高价 highest_price= max(highest_price_pri, highest_price_sec*open_price_pri/open_price_sec) # 以第一个行情为基准修正出的总最高价 lowest_price_pri= min( [plow for plow in pdata[u'最低'] if plow != None] ) # 最低价 lowest_price_sec= min( [plow for plow in pdata[u'最低二'] if plow != None] ) # 最低价 lowest_price= min(lowest_price_pri, lowest_price_sec*open_price_pri/open_price_sec) # 以第一个行情为基准修正出的总最低价 yhighlim_price= int(highest_price * 1.1) # K线子图 Y 轴最大坐标 ylowlim_price= int(lowest_price / 1.1) # K线子图 Y 轴最小坐标 xfactor= 10.0/230.0 # 一条 K 线的宽度在 X 轴上所占距离(英寸) yfactor= 0.3 # Y 轴上每一个距离单位的长度(英寸),这个单位距离是线性坐标和对数坐标通用的 expbase= 1.1 # 底数,取得小一点,比较接近 1。股价 3 元到 4 元之间有大约 3 个单位距离 # XXX: 价格在 Y 轴上的 “份数”。注意,虽然最高与最低价是以第一个行情为基准修正出来的,但其中包含的倍数因子对结果无影响,即: # log(base, num1) - log(base, num2) == # log(base, num1/num2) == # log(base, k*num1/k*num2) == # log(base, k*num1) - log(base, k*num2) # ,这是对数运算的性质。 ymulti_price= math.log(yhighlim_price, expbase) - math.log(ylowlim_price, expbase) ymulti_vol= 3.0 # 成交量部分在 Y 轴所占的 “份数” ymulti_top= 1.2 # 顶部空白区域在 Y 轴所占的 “份数” ymulti_bot= 1.2 # 底部空白区域在 Y 轴所占的 “份数” xmulti_left= 12.0 # 左侧空白区域所占的 “份数” xmulti_right= 12.0 # 右侧空白区域所占的 “份数” xmulti_all= length + xmulti_left + xmulti_right xlen_fig= xmulti_all * xfactor # 整个 Figure 的宽度 ymulti_all= ymulti_price + ymulti_vol + ymulti_top + ymulti_bot ylen_fig= ymulti_all * yfactor # 整个 Figure 的高度 rect_1= (xmulti_left/xmulti_all, (ymulti_bot+ymulti_vol)/ymulti_all, length/xmulti_all, ymulti_price/ymulti_all) # K线图部分 rect_2= (xmulti_left/xmulti_all, ymulti_bot/ymulti_all, length/xmulti_all, ymulti_vol/ymulti_all) # 成交量部分 # 建立 Figure 对象 #================================================================================================================================================== figfacecolor= __color_pink__ figedgecolor= __color_navy__ figdpi= 300 figlinewidth= 1.0 figobj= pyplot.figure(figsize=(xlen_fig, ylen_fig), dpi=figdpi, facecolor=figfacecolor, edgecolor=figedgecolor, linewidth=figlinewidth) # Figure 对象 # 整个 figure 的标题 title_pri= (pdata[u'代码'] + ' ' if u'代码' in pdata else '') + pdata[u'简称'] title_sec= (pdata[u'代码二'] + ' ' if u'代码二' in pdata else '') + pdata[u'简称二'] figobj.suptitle(title_pri + ' / ' + title_sec, fontsize=12, fontproperties=__font_properties__) #================================================================================================================================================== #================================================================================================================================================== #======= #======= XXX: 第一只:成交量部分 #======= #================================================================================================================================================== #================================================================================================================================================== # 第一只:添加 Axes 对象 #================================================================================================================================================== axes_2= figobj.add_axes(rect_2, axis_bgcolor='black') axes_2.set_axisbelow(True) # 网格线放在底层 # 第一只:改变坐标线的颜色 #================================================================================================================================================== for child in axes_2.get_children(): if isinstance(child, matplotlib.spines.Spine): child.set_color('lightblue') # 第一只:得到 X 轴 和 Y 轴 的两个 Axis 对象 #================================================================================================================================================== xaxis_2= axes_2.get_xaxis() yaxis_2= axes_2.get_yaxis() # 第一只:设置两个坐标轴上的 grid #================================================================================================================================================== xaxis_2.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.2) xaxis_2.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1) yaxis_2.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.2) yaxis_2.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1) #================================================================================================================================================== #======= 第一只:成交量绘图 #================================================================================================================================================== xindex= numpy.arange(length) # X 轴上的 index,一个辅助数据 zipoc= zip(pdata[u'开盘'], pdata[u'收盘']) up= numpy.array( [ True if po < pc and po != None else False for po, pc in zipoc] ) # 标示出该天股价日内上涨的一个序列 down= numpy.array( [ True if po > pc and po != None else False for po, pc in zipoc] ) # 标示出该天股价日内下跌的一个序列 side= numpy.array( [ True if po == pc and po != None else False for po, pc in zipoc] ) # 标示出该天股价日内走平的一个序列 if u'成交额' in pdata: volume= pdata[u'成交额'] else: volume= pdata[u'成交量'] rarray_vol= numpy.array(volume) volzeros= numpy.zeros(length) # 辅助数据 # XXX: 如果 up/down/side 各项全部为 False,那么 vlines() 会报错。 if True in up: axes_2.vlines(xindex[up], volzeros[up], rarray_vol[up], edgecolor='red', linewidth=3.0, label='_nolegend_') if True in down: axes_2.vlines(xindex[down], volzeros[down], rarray_vol[down], edgecolor='green', linewidth=3.0, label='_nolegend_') if True in side: axes_2.vlines(xindex[side], volzeros[side], rarray_vol[side], edgecolor='0.7', linewidth=3.0, label='_nolegend_') # 第一只:设定 X 轴坐标的范围 #================================================================================================================================================== axes_2.set_xlim(-1, length) # 第一只:设定 X 轴上的坐标 #================================================================================================================================================== datelist= [ datetime.date(int(ys), int(ms), int(ds)) for ys, ms, ds in [ dstr.split('-') for dstr in pdata[u'日期'] ] ] # 确定 X 轴的 MajorLocator mdindex= [] # 每个月第一个交易日在所有日期列表中的 index years= set([d.year for d in datelist]) # 所有的交易年份 for y in sorted(years): months= set([d.month for d in datelist if d.year == y]) # 当年所有的交易月份 for m in sorted(months): monthday= min([dt for dt in datelist if dt.year==y and dt.month==m]) # 当月的第一个交易日 mdindex.append(datelist.index(monthday)) xMajorLocator= FixedLocator(numpy.array(mdindex)) # 第一只:确定 X 轴的 MinorLocator wdindex= {} # value: 每周第一个交易日在所有日期列表中的 index; key: 当周的序号 week number(当周是第几周) for d in datelist: isoyear, weekno= d.isocalendar()[0:2] dmark= isoyear*100 + weekno if dmark not in wdindex: wdindex[dmark]= datelist.index(d) xMinorLocator= FixedLocator(numpy.array( sorted(wdindex.values()) )) # 第一只:确定 X 轴的 MajorFormatter 和 MinorFormatter def x_major_formatter_2(idx, pos=None): return datelist[idx].strftime('%Y-%m-%d') def x_minor_formatter_2(idx, pos=None): return datelist[idx].strftime('%m-%d') xMajorFormatter= FuncFormatter(x_major_formatter_2) xMinorFormatter= FuncFormatter(x_minor_formatter_2) # 第一只:设定 X 轴的 Locator 和 Formatter xaxis_2.set_major_locator(xMajorLocator) xaxis_2.set_major_formatter(xMajorFormatter) xaxis_2.set_minor_locator(xMinorLocator) xaxis_2.set_minor_formatter(xMinorFormatter) # 第一只:设定 X 轴主要坐标点与辅助坐标点的样式 for malabel in axes_2.get_xticklabels(minor=False): malabel.set_fontsize(4) malabel.set_horizontalalignment('right') malabel.set_rotation('45') for milabel in axes_2.get_xticklabels(minor=True): milabel.set_fontsize(4) milabel.set_color('blue') milabel.set_horizontalalignment('right') milabel.set_rotation('45') # 第一只:设定成交量 Y 轴坐标的范围 #================================================================================================================================================== maxvol= max(volume) # 注意是 int 类型 axes_2.set_ylim(0, maxvol) # 第一只:设定成交量 Y 轴上的坐标 #================================================================================================================================================== vollen= len(str(maxvol)) volstep_pri= int(round(maxvol/10.0+5000, -4)) yMajorLocator_2= MultipleLocator(volstep_pri) # 第一只:确定 Y 轴的 MajorFormatter dimsuffix= u'元' if u'成交额' in pdata else u'股' def y_major_formatter_2(num, pos=None): if num >= 10**8: # 大于 1 亿 return (str(round(num/10.0**8, 2)) + u'亿' + dimsuffix) if num != 0 else '0' else: return (str(num/10.0**4) + u'万' + dimsuffix) if num != 0 else '0' # def y_major_formatter_2(num, pos=None): # return int(num) yMajorFormatter_2= FuncFormatter(y_major_formatter_2) # 确定 Y 轴的 MinorFormatter # def y_minor_formatter_2(num, pos=None): # return int(num) # yMinorFormatter_2= FuncFormatter(y_minor_formatter_2) yMinorFormatter_2= NullFormatter() # 第一只:设定 X 轴的 Locator 和 Formatter yaxis_2.set_major_locator(yMajorLocator_2) yaxis_2.set_major_formatter(yMajorFormatter_2) # yaxis_2.set_minor_locator(yMinorLocator_2) yaxis_2.set_minor_formatter(yMinorFormatter_2) # 第一只:设定 Y 轴主要坐标点与辅助坐标点的样式 for malab in axes_2.get_yticklabels(minor=False): malab.set_font_properties(__font_properties__) malab.set_fontsize(4.5) # 这个必须放在前一句后面,否则作用会被覆盖 # 第一只:成交量数值在图中间的显示 #================================================================================================================================================== for iy in range(volstep_pri, maxvol, volstep_pri): for ix in mdindex[1:-1:3]: newlab= axes_2.text(ix+8, iy, y_major_formatter_2(iy)) newlab.set_font_properties(__font_properties__) newlab.set_color('0.3') newlab.set_fontsize(3) newlab.set_zorder(0) # XXX: 放在底层 # newlab.set_verticalalignment('center') #================================================================================================================================================== #================================================================================================================================================== #======= #======= XXX: 第二条成交量图线 #======= #================================================================================================================================================== #================================================================================================================================================== # 添加 Axes 对象 #================================================================================================================================================== axes_2_sec= axes_2.twinx() # axes_2_sec.set_axisbelow(True) # 网格线放在底层 axes_2_sec.set_axisbelow(True) # 网格线放在底层 # 改变坐标线的颜色 #================================================================================================================================================== # for child in axes_2_sec.get_children(): # if isinstance(child, matplotlib.spines.Spine): # child.set_color('lightblue') # 得到 X 轴 和 Y 轴 的两个 Axis 对象 #================================================================================================================================================== xaxis_2_sec= axes_2_sec.get_xaxis() yaxis_2_sec= axes_2_sec.get_yaxis() # 设置两个坐标轴上的 grid #================================================================================================================================================== # xaxis_2_sec.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.2) # xaxis_2_sec.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1) # yaxis_2_sec.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.2) # yaxis_2_sec.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1) #================================================================================================================================================== #======= 绘图 #================================================================================================================================================== if u'成交额二' in pdata: volume_sec= pdata[u'成交额二'] else: volume_sec= pdata[u'成交量二'] zipoc_sec= zip(pdata[u'开盘二'], pdata[u'收盘二']) up_sec= numpy.array( [ True if po < pc and po != None else False for po, pc in zipoc_sec] ) # 标示出该天股价日内上涨的一个序列 down_sec= numpy.array( [ True if po > pc and po != None else False for po, pc in zipoc_sec] ) # 标示出该天股价日内下跌的一个序列 side_sec= numpy.array( [ True if po == pc and po != None else False for po, pc in zipoc_sec] ) # 标示出该天股价日内走平的一个序列 rarray_vol_sec= numpy.array(volume_sec) volzeros_sec= numpy.zeros(length) # 辅助数据 # XXX: 如果 up_sec/down_sec/side_sec 各项全部为 False,那么 vlines() 会报错。 if True in up_sec: axes_2_sec.vlines(xindex[up_sec], volzeros_sec[up_sec], rarray_vol_sec[up_sec], edgecolor='pink', linewidth=1.0, label='_nolegend_', alpha=0.3) if True in down_sec: axes_2_sec.vlines(xindex[down_sec], volzeros_sec[down_sec], rarray_vol_sec[down_sec], edgecolor='lightgreen', linewidth=1.0, label='_nolegend_', alpha=0.3) if True in side_sec: axes_2_sec.vlines(xindex[side_sec], volzeros_sec[side_sec], rarray_vol_sec[side_sec], edgecolor='0.7', linewidth=1.0, label='_nolegend_', alpha=0.3) # 设定 X 轴坐标的范围 #================================================================================================================================================== # XXX: 不用了,与 axes_2 共用。 # 设定 Y 轴坐标的范围 #================================================================================================================================================== maxvol_sec= max(volume_sec) # 注意是 int 类型 axes_2_sec.set_ylim(0, maxvol_sec) # 设定 Y 轴上的坐标 #================================================================================================================================================== volstep_sec= volstep_pri*maxvol_sec/float(maxvol) yMajorLocator_2_sec= MultipleLocator(volstep_sec) # 确定 Y 轴的 MajorFormatter dimsuffix_sec= u'元' if u'成交额二' in pdata else u'股' def y_major_formatter_2_sec(num, pos=None): if num >= 10**8: # 大于 1 亿 print(('num= ' + str(num) + ', result= ' + str(round(num/10.0**8, 3)) + u'亿' + dimsuffix_sec).encode('utf8')) return (str(round(num/10.0**8, 3)) + u'亿' + dimsuffix_sec) if num != 0 else '0' else: return (str(round(num/10.0**4, 2)) + u'万' + dimsuffix_sec) if num != 0 else '0' # def y_major_formatter_2_sec(num, pos=None): # return int(num) yMajorFormatter_2_sec= FuncFormatter(y_major_formatter_2_sec) # 确定 Y 轴的 MinorFormatter # def y_minor_formatter_2(num, pos=None): # return int(num) # yMinorFormatter_2_sec= FuncFormatter(y_minor_formatter_2) yMinorFormatter_2_sec= NullFormatter() # 设定 X 轴的 Locator 和 Formatter yaxis_2_sec.set_major_locator(yMajorLocator_2_sec) yaxis_2_sec.set_major_formatter(yMajorFormatter_2_sec) # yaxis_2_sec.set_minor_locator(yMinorLocator_2_sec) yaxis_2_sec.set_minor_formatter(yMinorFormatter_2_sec) # 设定 Y 轴主要坐标点与辅助坐标点的样式 for malab in axes_2_sec.get_yticklabels(minor=False): malab.set_font_properties(__font_properties__) malab.set_fontsize(4.5) # 这个必须放在前一句后面,否则作用会被覆盖 #================================================================================================================================================== #================================================================================================================================================== #======= #======= XXX: K 线图部分 #======= #================================================================================================================================================== #================================================================================================================================================== # 添加 Axes 对象 #================================================================================================================================================== axes_1= figobj.add_axes(rect_1, axis_bgcolor='black', sharex=axes_2) axes_1.set_axisbelow(True) # 网格线放在底层 axes_1.set_yscale('log', basey=expbase) # 使用对数坐标 # 改变坐标线的颜色 #================================================================================================================================================== for child in axes_1.get_children(): if isinstance(child, matplotlib.spines.Spine): child.set_color('lightblue') # 得到 X 轴 和 Y 轴 的两个 Axis 对象 #================================================================================================================================================== xaxis_1= axes_1.get_xaxis() yaxis_1= axes_1.get_yaxis() # 设置两个坐标轴上的 grid #================================================================================================================================================== xaxis_1.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.2) xaxis_1.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1) yaxis_1.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.2) yaxis_1.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1) #================================================================================================================================================== #======= 绘图 #================================================================================================================================================== # 绘制 K 线部分 #================================================================================================================================================== # 对开收盘价进行视觉修正 for idx, poc in enumerate( zip(pdata[u'开盘'], pdata[u'收盘']) ): if poc[0] == poc[1] and None not in poc: variant= round((poc[1]+1000)/2000, 0) pdata[u'开盘'][idx]= poc[0] - variant # 稍微偏离一点,使得在图线上不致于完全看不到 pdata[u'收盘'][idx]= poc[1] + variant rarray_open= numpy.array(pdata[u'开盘']) rarray_close= numpy.array(pdata[u'收盘']) rarray_high= numpy.array(pdata[u'最高']) rarray_low= numpy.array(pdata[u'最低']) # XXX: 如果 up, down, side 里有一个全部为 False 组成,那么 vlines() 会报错。 # XXX: 可以使用 alpha 参数调节透明度 if True in up: axes_1.vlines(xindex[up], rarray_low[up], rarray_high[up], edgecolor='red', linewidth=0.6, label='_nolegend_') axes_1.vlines(xindex[up], rarray_open[up], rarray_close[up], edgecolor='red', linewidth=3.0, label='_nolegend_') if True in down: axes_1.vlines(xindex[down], rarray_low[down], rarray_high[down], edgecolor='green', linewidth=0.6, label='_nolegend_') axes_1.vlines(xindex[down], rarray_open[down], rarray_close[down], edgecolor='green', linewidth=3.0, label='_nolegend_') if True in side: axes_1.vlines(xindex[side], rarray_low[side], rarray_high[side], edgecolor='0.7', linewidth=0.6, label='_nolegend_') axes_1.vlines(xindex[side], rarray_open[side], rarray_close[side], edgecolor='0.7', linewidth=3.0, label='_nolegend_') # 绘制均线部分 #================================================================================================================================================== if u'5日均' in pdata: rarray_5dayave= numpy.array(pdata[u'5日均']) axes_1.plot(xindex, rarray_5dayave, 'o-', color='white', linewidth=0.1, label='ave_5', \ markersize=0.7, markeredgecolor='white', markeredgewidth=0.1) # 5日均线 if u'10日均' in pdata: rarray_10dayave= numpy.array(pdata[u'10日均']) axes_1.plot(xindex, rarray_10dayave, 'o-', color='yellow', linewidth=0.1, label='ave_10', \ markersize=0.7, markeredgecolor='yellow', markeredgewidth=0.1) # 10日均线 if u'30日均' in pdata: rarray_30dayave= numpy.array(pdata[u'30日均']) axes_1.plot(xindex, rarray_30dayave, 'o-', color='cyan', linewidth=0.1, label='ave_30', \ markersize=0.7, markeredgecolor='cyan', markeredgewidth=0.1) # 30日均线 # 绘制 复权提示 #================================================================================================================================================== if u'复权' in pdata: adjdict= dict(pdata[u'复权']) for idx, dstr in enumerate(pdata[u'日期']): if dstr in adjdict: axes_1.plot([idx, idx], [ylowlim_price, yhighlim_price], '-', color='purple', linewidth=0.3) # 设定 X 轴坐标的范围 #================================================================================================================================================== axes_1.set_xlim(-1, length) # 先设置 label 位置,再将 X 轴上的坐标设为不可见。因为与 成交量子图 共用 X 轴 #================================================================================================================================================== # 设定 X 轴的 Locator 和 Formatter xaxis_1.set_major_locator(xMajorLocator) xaxis_1.set_major_formatter(xMajorFormatter) xaxis_1.set_minor_locator(xMinorLocator) xaxis_1.set_minor_formatter(xMinorFormatter) # 将 X 轴上的坐标设为不可见。 for malab in axes_1.get_xticklabels(minor=False): malab.set_visible(False) for milab in axes_1.get_xticklabels(minor=True): milab.set_visible(False) # 用这一段效果也一样 # pyplot.setp(axes_1.get_xticklabels(minor=False), visible=False) # pyplot.setp(axes_1.get_xticklabels(minor=True), visible=False) # 设定 Y 轴坐标的范围 #================================================================================================================================================== axes_1.set_ylim(ylowlim_price, yhighlim_price) # 设定 Y 轴上的坐标 #================================================================================================================================================== # XXX: 不用 LogLocator 了,因为不能控制坐标点的位置。 # 主要坐标点 #---------------------------------------------------------------------------- yticks_major_pri= [] for i in range(1, 999): newloc= ylowlim_price * (expbase**i) if newloc <= yhighlim_price: yticks_major_pri.append(newloc) else: break yMajorLocator_1= FixedLocator(numpy.array(yticks_major_pri)) # 确定 Y 轴的 MajorFormatter def y_major_formatter_1(num, pos=None): return str(round(num/1000.0, 2)) yMajorFormatter_1= FuncFormatter(y_major_formatter_1) # 设定 X 轴的 Locator 和 Formatter yaxis_1.set_major_locator(yMajorLocator_1) yaxis_1.set_major_formatter(yMajorFormatter_1) # 设定 Y 轴主要坐标点与辅助坐标点的样式 for mal in axes_1.get_yticklabels(minor=False): mal.set_fontsize(6) # 辅助坐标点 #---------------------------------------------------------------------------- yticks_minor_pri= [] mtstart= ylowlim_price * (1.0+(expbase-1.0)/2) for i in range(999): newloc= mtstart * (expbase**i) if newloc <= yhighlim_price: yticks_minor_pri.append(newloc) else: break yMinorLocator_1= FixedLocator(numpy.array(yticks_minor_pri)) # XXX minor ticks 已经在上面一并设置,这里不需要了。 # 确定 Y 轴的 MinorFormatter def y_minor_formatter_1(num, pos=None): return str(round(num/1000.0, 2)) yMinorFormatter_1= FuncFormatter(y_minor_formatter_1) # 设定 X 轴的 Locator 和 Formatter yaxis_1.set_minor_locator(yMinorLocator_1) yaxis_1.set_minor_formatter(yMinorFormatter_1) # 设定 Y 轴主要坐标点与辅助坐标点的样式 for mal in axes_1.get_yticklabels(minor=True): mal.set_fontsize(5) mal.set_color('blue') # 第一只:价格数值在图中间的显示 #================================================================================================================================================== for iy in yticks_major_pri: for ix in mdindex[1:-1:3]: newlab= axes_1.text(ix+8, iy*1.001, y_major_formatter_1(iy)) newlab.set_font_properties(__font_properties__) newlab.set_color('0.3') newlab.set_fontsize(3) newlab.set_zorder(0) # XXX: 放在底层 # newlab.set_verticalalignment('center') # 第一只:日期在图中间的显示 #================================================================================================================================================== for iy in yticks_minor_pri[1:-1:5]: for ix in mdindex: newlab= axes_1.text(ix-1, iy, pdata[u'日期'][ix]) newlab.set_font_properties(__font_properties__) newlab.set_color('0.3') newlab.set_fontsize(4) newlab.set_rotation('vertical') # newlab.set_horizontalalignment('left') # newlab.set_verticalalignment('bottom') newlab.set_zorder(0) # XXX: 放在底层 # newlab.set_verticalalignment('center') #================================================================================================================================================== #================================================================================================================================================== #======= #======= XXX: 第二条 K 线图 #======= #================================================================================================================================================== #================================================================================================================================================== # 添加 Axes 对象 #================================================================================================================================================== axes_1_sec= axes_1.twinx() # axes_1_sec.set_axisbelow(True) # 网格线放在底层 axes_1_sec.set_yscale('log', basey=expbase) # 使用对数坐标 # 得到 X 轴 和 Y 轴 的两个 Axis 对象 #================================================================================================================================================== xaxis_1_sec= axes_1_sec.get_xaxis() yaxis_1_sec= axes_1_sec.get_yaxis() #================================================================================================================================================== #======= 绘图 #================================================================================================================================================== # 绘制 K 线部分 #================================================================================================================================================== # 对开收盘价进行视觉修正 for idx, poc in enumerate( zipoc_sec ): if poc[0] == poc[1] and None not in poc: pdata[u'开盘二'][idx]= poc[0] - 5 # 稍微偏离一点,使得在图线上不致于完全看不到 pdata[u'收盘二'][idx]= poc[1] + 5 rarray_open= numpy.array(pdata[u'开盘二']) rarray_close= numpy.array(pdata[u'收盘二']) rarray_high= numpy.array(pdata[u'最高二']) rarray_low= numpy.array(pdata[u'最低二']) # XXX: 如果 up_sec, down_sec, side_sec 里有一个全部为 False 组成,那么 vlines() 会报错。 # XXX: 可以使用 alpha 参数调节透明度 if True in up_sec: axes_1_sec.vlines(xindex[up_sec], rarray_low[up_sec], rarray_high[up_sec], edgecolor='red', linewidth=0.6, label='_nolegend_', alpha=0.3) axes_1_sec.vlines(xindex[up_sec], rarray_open[up_sec], rarray_close[up_sec], edgecolor='red', linewidth=3.0, label='_nolegend_', alpha=0.3) if True in down_sec: axes_1_sec.vlines(xindex[down_sec], rarray_low[down_sec], rarray_high[down_sec], edgecolor='green', linewidth=0.6, label='_nolegend_', alpha=0.3) axes_1_sec.vlines(xindex[down_sec], rarray_open[down_sec], rarray_close[down_sec], edgecolor='green', linewidth=3.0, label='_nolegend_', alpha=0.3) if True in side_sec: axes_1_sec.vlines(xindex[side_sec], rarray_low[side_sec], rarray_high[side_sec], edgecolor='0.7', linewidth=0.6, label='_nolegend_', alpha=0.3) axes_1_sec.vlines(xindex[side_sec], rarray_open[side_sec], rarray_close[side_sec], edgecolor='0.7', linewidth=3.0, label='_nolegend_', alpha=0.3) # 设定 X 轴坐标的范围 #================================================================================================================================================== axes_1_sec.set_xlim(-1, length) # 先设置 label 位置,再将 X 轴上的坐标设为不可见。因为与 成交量子图 共用 X 轴 #================================================================================================================================================== # 设定 X 轴的 Locator 和 Formatter xaxis_1_sec.set_major_locator(xMajorLocator) xaxis_1_sec.set_major_formatter(xMajorFormatter) xaxis_1_sec.set_minor_locator(xMinorLocator) xaxis_1_sec.set_minor_formatter(xMinorFormatter) # 将 X 轴上的坐标设为不可见。 for malab in axes_1_sec.get_xticklabels(minor=False): malab.set_visible(False) for milab in axes_1_sec.get_xticklabels(minor=True): milab.set_visible(False) # 设定 Y 轴坐标的范围 #================================================================================================================================================== axes_1_sec.set_ylim(ylowlim_price*open_price_sec/open_price_pri, yhighlim_price*open_price_sec/open_price_pri) # 设定 Y 轴上的坐标 #================================================================================================================================================== # 主要坐标点 #---------------------------------------------------------------------------- yticks_major_sec= [] ylowlim_price_sec= ylowlim_price*open_price_sec/open_price_pri yhighlim_price_sec= yhighlim_price*open_price_sec/open_price_pri for i in range(1, 999): newloc= ylowlim_price_sec * (expbase**i) if newloc <= yhighlim_price_sec: yticks_major_sec.append(newloc) else: break yMajorLocator_1_sec= FixedLocator(numpy.array(yticks_major_sec)) # 确定 Y 轴的 MajorFormatter def y_major_formatter_1_sec(num, pos=None): return str(round(num/1000.0, 2)) yMajorFormatter_1_sec= FuncFormatter(y_major_formatter_1_sec) # 设定 X 轴的 Locator 和 Formatter yaxis_1_sec.set_major_locator(yMajorLocator_1_sec) yaxis_1_sec.set_major_formatter(yMajorFormatter_1_sec) # 设定 Y 轴主要坐标点与辅助坐标点的样式 for mal in axes_1_sec.get_yticklabels(minor=False): mal.set_fontsize(6) # 辅助坐标点 #---------------------------------------------------------------------------- yticks_minor_sec= [] mtstart_sec= ylowlim_price_sec * (1.0+(expbase-1.0)/2) for i in range(999): newloc= mtstart_sec * (expbase**i) if newloc <= yhighlim_price_sec: yticks_minor_sec.append(newloc) else: break yMinorLocator_1_sec= FixedLocator(numpy.array(yticks_minor_sec)) # XXX minor ticks 已经在上面一并设置,这里不需要了。 # 确定 Y 轴的 MinorFormatter def y_minor_formatter_1_sec(num, pos=None): return str(round(num/1000.0, 2)) yMinorFormatter_1_sec= FuncFormatter(y_minor_formatter_1_sec) # 设定 X 轴的 Locator 和 Formatter yaxis_1_sec.set_minor_locator(yMinorLocator_1_sec) yaxis_1_sec.set_minor_formatter(yMinorFormatter_1_sec) # 设定 Y 轴主要坐标点与辅助坐标点的样式 for mal in axes_1_sec.get_yticklabels(minor=True): mal.set_fontsize(5) mal.set_color('blue') # 显示图片 #================================================================================================================================================== # pyplot.show() # 保存图片 #================================================================================================================================================== figobj.savefig(figpath, dpi=figdpi, facecolor=figfacecolor, edgecolor=figedgecolor, linewidth=figlinewidth) if __name__ == '__main__': Plot(pfile=sys.argv[1], figpath=sys.argv[2])
用来画股票 K线图 的 Python 脚本
---- <补记>:
最新的在这里: 用 Python / Matplotlib 画出来的股票 K线图 (四)
下一篇在这里: 用 Python / Matplotlib 画出来的股票 K线图 (三)
---- 花了 20 个小时左右的时间才从新浪下载完复权日线数据,把复权日线表建起来。这速度也太慢了,还有首次下载网页失败的比例居然这么高,一定有问题,印象中以前不是这么慢的,下载几千只股票的数据也只有几十个页面会首次下载失败吧。但昨天晚上更新最新数据的时候把下载任务之间的延迟扩大了一些,好像好一些,速度还可以,而且失败率不高。我开的是 5 个线程,下载页面之间的间隔是 0.2 ~ 0.3 秒。
---- 另外,把那个画 K 线图的脚本贴出来。这个脚本是通过研究 Matplotlib 官网里的示例并且借助 Google,用大概 1 周的时间改出来的。简单介绍一下:
1. 由两个子图(subplot)构成,上面一个显示价格(K 线),下面一个显示成交量。
2. K 线子图可以使用线性坐标或者对数坐标(由 Plot() 函数第三个参数控制)。使用线性坐标的时候,每个单位价格区间所占高度是固定的;使用对数坐标的时候,每个单位涨幅区间(比如 10%)所占高度是固定的。成交量子图的高度总是固定,不论成交量数值大小。
3. 对 X 轴来说,每根 K 线的宽度固定,整个图形的宽度决定于行情的天数。只要把行情数据文件作为参数传递过去就可以,图片尺寸由程序自主计算。
4. 另外,figdpi 这个变量控制图片的分辨率(解析度),可以随意调大调小。上一篇文章里贴的图使用的 dpi 值是 300。另外,X 轴和 Y 轴上的坐标点也是程序自主决定的。
---- 整个脚本还是一个 work-in-progress,目前的局限主要在于使用对数坐标时,Y 轴坐标点的确定。前一篇里所贴的那个图,可以看见价格上限在 20 块左右,如果换一只价格 90 块上下的股票,或者用来画几千点的指数行情,那 Y 轴的坐标点就会太密集。解决办法是根据取值区间来自主选择合适的 Y 轴坐标间距,但是这个目前还没有做。
---- 任何意见或建议都许多欢迎 !
---- <补记>:已经有了大幅改进的版本,在下一篇里。
# -*- coding: utf-8 -*- import sys import pickle import math import datetime import matplotlib matplotlib.use("WXAgg", warn=True) # 这个要紧跟在 import matplotlib 之后,而且必须安装了 wxpython 2.8 才行。 import matplotlib.pyplot as pyplot import numpy from matplotlib.ticker import FixedLocator, MultipleLocator, LogLocator, FuncFormatter, NullFormatter, LogFormatter def Plot(pfile, figpath, useexpo=True): ''' pfile 指明存放绘图数据的 pickle file,figpath 指定图片需存放的路径 ''' fileobj= open(name=pfile, mode='rb') pdata= pickle.load(fileobj) fileobj.close() # 计算图片的尺寸(单位英寸) # 注意:Python2 里面, "1 / 10" 结果是 0, 必须写成 "1.0 / 10" 才会得到 0.1 #================================================================================================================================================== length= len(pdata[u'日期']) # 所有数据的长度,就是天数 highest_price= max(pdata[u'最高']) # 最高价 lowest_price= min( [plow for plow in pdata[u'最低'] if plow != None] ) # 最低价 yhighlim_price= round(highest_price+50, -2) # K线子图 Y 轴最大坐标 ylowlim_price= round(lowest_price-50, -2) # K线子图 Y 轴最小坐标 xfactor= 10.0/230.0 # 一条 K 线的宽度在 X 轴上所占距离(英寸) yfactor= 0.3 # Y 轴上每一个距离单位的长度(英寸),这个单位距离是线性坐标和对数坐标通用的 if useexpo: # 要使用对数坐标 expbase= 1.1 # 底数,取得小一点,比较接近 1。股价 3 元到 4 元之间有大约 3 个单位距离 ymulti_price= math.log(yhighlim_price, expbase) - math.log(ylowlim_price, expbase) # 价格在 Y 轴上的 “份数” else: ymulti_price= (yhighlim_price - ylowlim_price) / 100 # 价格在 Y 轴上的 “份数” ymulti_vol= 3.0 # 成交量部分在 Y 轴所占的 “份数” ymulti_top= 0.2 # 顶部空白区域在 Y 轴所占的 “份数” ymulti_bot= 0.8 # 底部空白区域在 Y 轴所占的 “份数” xmulti_left= 10.0 # 左侧空白区域所占的 “份数” xmulti_right= 3.0 # 右侧空白区域所占的 “份数” xmulti_all= length + xmulti_left + xmulti_right xlen_fig= xmulti_all * xfactor # 整个 Figure 的宽度 ymulti_all= ymulti_price + ymulti_vol + ymulti_top + ymulti_bot ylen_fig= ymulti_all * yfactor # 整个 Figure 的高度 rect_1= (xmulti_left/xmulti_all, (ymulti_bot+ymulti_vol)/ymulti_all, length/xmulti_all, ymulti_price/ymulti_all) # K线图部分 rect_2= (xmulti_left/xmulti_all, ymulti_bot/ymulti_all, length/xmulti_all, ymulti_vol/ymulti_all) # 成交量部分 # 建立 Figure 对象 #================================================================================================================================================== figfacecolor= 'white' figedgecolor= 'black' figdpi= 600 figlinewidth= 1.0 figobj= pyplot.figure(figsize=(xlen_fig, ylen_fig), dpi=figdpi, facecolor=figfacecolor, edgecolor=figedgecolor, linewidth=figlinewidth) # Figure 对象 #================================================================================================================================================== #================================================================================================================================================== #======= 成交量部分 #================================================================================================================================================== #================================================================================================================================================== # 添加 Axes 对象 #================================================================================================================================================== axes_2= figobj.add_axes(rect_2, axis_bgcolor='black') axes_2.set_axisbelow(True) # 网格线放在底层 # 改变坐标线的颜色 #================================================================================================================================================== for child in axes_2.get_children(): if isinstance(child, matplotlib.spines.Spine): child.set_color('lightblue') # 得到 X 轴 和 Y 轴 的两个 Axis 对象 #================================================================================================================================================== xaxis_2= axes_2.get_xaxis() yaxis_2= axes_2.get_yaxis() # 设置两个坐标轴上的 grid #================================================================================================================================================== xaxis_2.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.2) xaxis_2.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1) yaxis_2.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.2) yaxis_2.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1) #================================================================================================================================================== #======= 绘图 #================================================================================================================================================== xindex= numpy.arange(length) # X 轴上的 index,一个辅助数据 zipoc= zip(pdata[u'开盘'], pdata[u'收盘']) up= numpy.array( [ True if po < pc and po != None else False for po, pc in zipoc] ) # 标示出该天股价日内上涨的一个序列 down= numpy.array( [ True if po > pc and po != None else False for po, pc in zipoc] ) # 标示出该天股价日内下跌的一个序列 side= numpy.array( [ True if po == pc and po != None else False for po, pc in zipoc] ) # 标示出该天股价日内走平的一个序列 volume= pdata[u'成交量'] rarray_vol= numpy.array(volume) volzeros= numpy.zeros(length) # 辅助数据 # XXX: 如果 up/down/side 各项全部为 False,那么 vlines() 会报错。 if True in up: axes_2.vlines(xindex[up], volzeros[up], rarray_vol[up], color='red', linewidth=3.0, label='_nolegend_') if True in down: axes_2.vlines(xindex[down], volzeros[down], rarray_vol[down], color='green', linewidth=3.0, label='_nolegend_') if True in side: axes_2.vlines(xindex[side], volzeros[side], rarray_vol[side], color='0.7', linewidth=3.0, label='_nolegend_') # 设定 X 轴坐标的范围 #================================================================================================================================================== axes_2.set_xlim(-1, length) # 设定 X 轴上的坐标 #================================================================================================================================================== datelist= [ datetime.date(int(ys), int(ms), int(ds)) for ys, ms, ds in [ dstr.split('-') for dstr in pdata[u'日期'] ] ] # 确定 X 轴的 MajorLocator mdindex= [] # 每个月第一个交易日在所有日期列表中的 index years= set([d.year for d in datelist]) # 所有的交易年份 for y in sorted(years): months= set([d.month for d in datelist if d.year == y]) # 当年所有的交易月份 for m in sorted(months): monthday= min([dt for dt in datelist if dt.year==y and dt.month==m]) # 当月的第一个交易日 mdindex.append(datelist.index(monthday)) xMajorLocator= FixedLocator(numpy.array(mdindex)) # 确定 X 轴的 MinorLocator wdindex= [] # 每周第一个交易日在所有日期列表中的 index for d in datelist: if d.weekday() == 0: wdindex.append(datelist.index(d)) xMinorLocator= FixedLocator(numpy.array(wdindex)) # 确定 X 轴的 MajorFormatter 和 MinorFormatter def x_major_formatter_2(idx, pos=None): return datelist[idx].strftime('%Y-%m-%d') def x_minor_formatter_2(idx, pos=None): return datelist[idx].strftime('%m-%d') xMajorFormatter= FuncFormatter(x_major_formatter_2) xMinorFormatter= FuncFormatter(x_minor_formatter_2) # 设定 X 轴的 Locator 和 Formatter xaxis_2.set_major_locator(xMajorLocator) xaxis_2.set_major_formatter(xMajorFormatter) xaxis_2.set_minor_locator(xMinorLocator) xaxis_2.set_minor_formatter(xMinorFormatter) # 设定 X 轴主要坐标点与辅助坐标点的样式 for malabel in axes_2.get_xticklabels(minor=False): malabel.set_fontsize(3) malabel.set_horizontalalignment('right') malabel.set_rotation('30') for milabel in axes_2.get_xticklabels(minor=True): milabel.set_fontsize(2) milabel.set_horizontalalignment('right') milabel.set_rotation('30') # 设定 Y 轴坐标的范围 #================================================================================================================================================== maxvol= max(volume) # 注意是 int 类型 axes_2.set_ylim(0, maxvol) # 设定 Y 轴上的坐标 #================================================================================================================================================== vollen= len(str(maxvol)) yMajorLocator_2= MultipleLocator(10**(vollen-1)) yMinorLocator_2= MultipleLocator((10**(vollen-2))*5) # 确定 Y 轴的 MajorFormatter # def y_major_formatter_2(num, pos=None): # numtable= {'1':u'一', '2':u'二', '3':u'三', '4':u'四', '5':u'五', '6':u'六', '7':u'七', '8':u'八', '9':u'九', } # dimtable= {3:u'百', 4:u'千', 5:u'万', 6:u'十万', 7:u'百万', 8:u'千万', 9:u'亿', 10:u'十亿', 11:u'百亿'} # return numtable[str(num)[0]] + dimtable[vollen] if num != 0 else '0' def y_major_formatter_2(num, pos=None): return int(num) yMajorFormatter_2= FuncFormatter(y_major_formatter_2) # 确定 Y 轴的 MinorFormatter # def y_minor_formatter_2(num, pos=None): # return int(num) # yMinorFormatter_2= FuncFormatter(y_minor_formatter_2) yMinorFormatter_2= NullFormatter() # 设定 X 轴的 Locator 和 Formatter yaxis_2.set_major_locator(yMajorLocator_2) yaxis_2.set_major_formatter(yMajorFormatter_2) yaxis_2.set_minor_locator(yMinorLocator_2) yaxis_2.set_minor_formatter(yMinorFormatter_2) # 设定 Y 轴主要坐标点与辅助坐标点的样式 for malab in axes_2.get_yticklabels(minor=False): malab.set_fontsize(3) for milab in axes_2.get_yticklabels(minor=True): milab.set_fontsize(2) #================================================================================================================================================== #================================================================================================================================================== #======= K 线图部分 #================================================================================================================================================== #================================================================================================================================================== # 添加 Axes 对象 #================================================================================================================================================== axes_1= figobj.add_axes(rect_1, axis_bgcolor='black', sharex=axes_2) axes_1.set_axisbelow(True) # 网格线放在底层 if useexpo: axes_1.set_yscale('log', basey=expbase) # 使用对数坐标 # 改变坐标线的颜色 #================================================================================================================================================== for child in axes_1.get_children(): if isinstance(child, matplotlib.spines.Spine): child.set_color('lightblue') # 得到 X 轴 和 Y 轴 的两个 Axis 对象 #================================================================================================================================================== xaxis_1= axes_1.get_xaxis() yaxis_1= axes_1.get_yaxis() # 设置两个坐标轴上的 grid #================================================================================================================================================== xaxis_1.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.2) xaxis_1.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1) yaxis_1.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.2) yaxis_1.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1) #================================================================================================================================================== #======= 绘图 #================================================================================================================================================== # 绘制 K 线部分 #================================================================================================================================================== rarray_open= numpy.array(pdata[u'开盘']) rarray_close= numpy.array(pdata[u'收盘']) rarray_high= numpy.array(pdata[u'最高']) rarray_low= numpy.array(pdata[u'最低']) # XXX: 如果 up, down, side 里有一个全部为 False 组成,那么 vlines() 会报错。 if True in up: axes_1.vlines(xindex[up], rarray_low[up], rarray_high[up], color='red', linewidth=0.6, label='_nolegend_') axes_1.vlines(xindex[up], rarray_open[up], rarray_close[up], color='red', linewidth=3.0, label='_nolegend_') if True in down: axes_1.vlines(xindex[down], rarray_low[down], rarray_high[down], color='green', linewidth=0.6, label='_nolegend_') axes_1.vlines(xindex[down], rarray_open[down], rarray_close[down], color='green', linewidth=3.0, label='_nolegend_') if True in side: axes_1.vlines(xindex[side], rarray_low[side], rarray_high[side], color='0.7', linewidth=0.6, label='_nolegend_') axes_1.vlines(xindex[side], rarray_open[side], rarray_close[side], color='0.7', linewidth=3.0, label='_nolegend_') # 绘制均线部分 #================================================================================================================================================== rarray_1dayave= numpy.array(pdata[u'1日权均']) rarray_5dayave= numpy.array(pdata[u'5日均']) rarray_30dayave= numpy.array(pdata[u'30日均']) axes_1.plot(xindex, rarray_1dayave, 'o-', color='white', linewidth=0.1, markersize=0.7, markeredgecolor='white', markeredgewidth=0.1) # 1日加权均线 axes_1.plot(xindex, rarray_5dayave, 'o-', color='yellow', linewidth=0.1, markersize=0.7, markeredgecolor='yellow', markeredgewidth=0.1) # 5日均线 axes_1.plot(xindex, rarray_30dayave, 'o-', color='green', linewidth=0.1, markersize=0.7, markeredgecolor='green', markeredgewidth=0.1) # 30日均线 # 设定 X 轴坐标的范围 #================================================================================================================================================== axes_1.set_xlim(-1, length) # 先设置 label 位置,再将 X 轴上的坐标设为不可见。因为与 成交量子图 共用 X 轴 #================================================================================================================================================== # 设定 X 轴的 Locator 和 Formatter xaxis_1.set_major_locator(xMajorLocator) xaxis_1.set_major_formatter(xMajorFormatter) xaxis_1.set_minor_locator(xMinorLocator) xaxis_1.set_minor_formatter(xMinorFormatter) # 将 X 轴上的坐标设为不可见。 for malab in axes_1.get_xticklabels(minor=False): malab.set_visible(False) for milab in axes_1.get_xticklabels(minor=True): milab.set_visible(False) # 用这一段效果也一样 # pyplot.setp(axes_1.get_xticklabels(minor=False), visible=False) # pyplot.setp(axes_1.get_xticklabels(minor=True), visible=False) # 设定 Y 轴坐标的范围 #================================================================================================================================================== axes_1.set_ylim(ylowlim_price, yhighlim_price) # 设定 Y 轴上的坐标 #================================================================================================================================================== if useexpo: # 主要坐标点 #----------------------------------------------------- yMajorLocator_1= LogLocator(base=expbase) yMajorFormatter_1= NullFormatter() # 设定 X 轴的 Locator 和 Formatter yaxis_1.set_major_locator(yMajorLocator_1) yaxis_1.set_major_formatter(yMajorFormatter_1) # 设定 Y 轴主要坐标点与辅助坐标点的样式 # for mal in axes_1.get_yticklabels(minor=False): # mal.set_fontsize(3) # 辅助坐标点 #----------------------------------------------------- minorticks= range(int(ylowlim_price), int(yhighlim_price)+1, 100) yMinorLocator_1= FixedLocator(numpy.array(minorticks)) # 确定 Y 轴的 MinorFormatter def y_minor_formatter_1(num, pos=None): return str(num/100.0) + '0' yMinorFormatter_1= FuncFormatter(y_minor_formatter_1) # 设定 X 轴的 Locator 和 Formatter yaxis_1.set_minor_locator(yMinorLocator_1) yaxis_1.set_minor_formatter(yMinorFormatter_1) # 设定 Y 轴主要坐标点与辅助坐标点的样式 for mil in axes_1.get_yticklabels(minor=True): mil.set_fontsize(3) else: # 如果使用线性坐标,那么只标主要坐标点 yMajorLocator_1= MultipleLocator(100) def y_major_formatter_1(num, pos=None): return str(num/100.0) + '0' yMajorFormatter_1= FuncFormatter(y_major_formatter_1) # 设定 Y 轴的 Locator 和 Formatter yaxis_1.set_major_locator(yMajorLocator_1) yaxis_1.set_major_formatter(yMajorFormatter_1) # 设定 Y 轴主要坐标点与辅助坐标点的样式 for mal in axes_1.get_yticklabels(minor=False): mal.set_fontsize(3) # 保存图片 #================================================================================================================================================== figobj.savefig(figpath, dpi=figdpi, facecolor=figfacecolor, edgecolor=figedgecolor, linewidth=figlinewidth) if __name__ == '__main__': Plot(pfile=sys.argv[1], figpath=sys.argv[2], useexpo=True)
用 Python / Matplotlib 画出来的股票 K线图
---- 过年后开始真正学用 Matplotlib 画一些实际的图形,以下是最新的改进结果:
---- 股票是 600644,原始数据来自网络。就不总结要点了,Matplotlib 十分给力!
---- 下一步打算在标示价格的 Y 轴上使用对数坐标。得精确计算图片的尺寸,使代表相同涨幅的图线看起来具有相同的长度,而且要精确定位坐标点。另外还可以加上一定的注释和图例。
补记:已实现,如下图,注意 Y 轴对数坐标: