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：
