用matplotlib制作的比较满意的蜡烛图
2D图形制作包, 功能强大, 习练了很久, 终于搞定了一个比较满意的脚本.
特点:
- 使用方面要非常简单
- 绘制出来的图要非常的满意, 具有如下的特点
- 时间和空间的比例尺需要固定, 就是说图件的大小需要依据数据的长度和价格的变动幅度自动调整, 至少时间轴上应该如此.
- 时间轴的刻度: 对于日线图而言, 年/月/日/星期几 都应该一目了然.
- Y轴: 对数刻度, 10%等比刻度线, 刻度值的标签应该能反应绝对的股价, 支持双Y轴(右侧的Y轴度量大盘的变化)
- 蜡烛非白即黑, 只要两种颜色(包括边界线)
- 分辨率要足够高, 至少300DPI, 方便原样(无伸缩)打印
- 应该支持非常方便地抽取子集, 然后制图
版本持续升级:
2017.12 的备忘录
在以前的函数式代码的基础上, OOP方式重构代码, 方便以后扩展功能, 也让程序运行得更健硕
结果展示
主块代码
绘图模块的代码
结果展示:
png file from my github:
https://github.com/duanqingshan/learngit/blob/master/%E5%9D%87%E8%83%9C%E7%94%B5%E5%AD%90_20171230_182515__468000.png
gif file from my cnblogs:
https://files.cnblogs.com/files/duan-qs/%E5%9D%87%E8%83%9C%E7%94%B5%E5%AD%90_20171226_220616__255000.gif
主代码块:
# -*- coding: utf-8 -*-u''' 研究K线形态: 从单个K线做起, 然后K线组合, 然后K线形态
# 1. 定义两个实例
# 2. 加载数据
# 3. 前复权处理
# 4. 计算指标
# 5. 形态研究之: 提取与显示
# 6. 绘图 主图+成交量图
'''import amipy as ami
import plotter as pl
import pattern as pa
reload(pa)
reload(ami)context = ami.Context('600699.SH') # 000911
#context = ami.Context('002242.SZ') # 000911
stk = ami.Stock(context)stk.grab_data_tdxlday(context, num_days=None)
stk.load_tdx_qx()stk.qfq()stk.ma20 = ami.TTR.sma(stk.ohlc.close, 20)
stk.cyc61 = ami.TTR.sma(stk.ohlc.close, 120)pattern = pa.Pattern(stk)
pattern.study_csyx(roc1=0.3/100)#subset = slice(-250*3, None) # '2017-07' '2017'
subset = slice(-120,None) # '2017-07' '2017'
plotter = pl.Plotter(context,stk,subset,quanxi=None)
# plotter.plot_candle_vol()
#plotter.plot_candle_vol(savefig=True)#plotter.plot_timing(timing=pattern.csyx)
#plotter.plot_timing(timing=pattern.szx)
plotter.plot_timing(timing=pattern.upgap, savefig=True)
#plotter.plot_timing(timing=pattern.dngap) 绘图代码:
# -*- coding: utf-8 -*-#import sysimport numpy as np
import pandas as pdimport datetimeimport matplotlib
import matplotlib.pyplot as plt
from matplotlib.ticker import (FixedLocator, #MultipleLocator, #LogLocator, #NullFormatter, FuncFormatter, #LogFormatter )
from matplotlib.font_manager import FontProperties
from matplotlib.text import Text
myfont = FontProperties(fname=r"c:\windows\fonts\msyh.ttf") #size可不用指定
matplotlib.rcParams['axes.unicode_minus'] = False#import amipy as ami
import ttr as TTR#==============================================================================
# Python中的作用域及global用法 - Summer_cool - 博客园
# https://www.cnblogs.com/summer-cool/p/3884595.html
#
# 函数定义了本地作用域，而模块定义的是全局作用域。
# 如果想要在函数内定义全局作用域，需要加上global修饰符。
#
# 变量名解析：LEGB原则
# 当在函数中使用未认证的变量名时，Python搜索４个作用域:
# [本地作用域(L-local)(函数内部声明但没有使用global的变量)，
# 之后是上一层结构def或者lambda的本地作用域(E-enclosure),
# 之后是全局作用域(G-global)（函数中使用global声明的变量或在模块层声明的变量），
# 最后是内置作用域(B)（即python的内置类和函数等）］
# 并且在第一处能够找到这个变量名的地方停下来。
# 如果变量名在整个的搜索过程中都没有找到，Python就会报错。
#
# 补：上面的变量规则只适用于简单对象，当出现引用对象的属性时，则有另一套搜索规则:
# 属性引用搜索一个或多个对象，而不是作用域，并且有可能涉及到所谓的"继承"
# 补2:global修饰符在python里的一个独特现象:
# 在模块层面定义的变量（无需global修饰），
# 如果在函数中没有再定义同名变量，可以在函数中当做全局变量使用.
# 如果在函数中要对它重新赋值的话, 则必须在本函数中事先声明为全局变量, 否则会抛出异常.
#
# #先声明全局本函数里用到的全局变量: 图表, 上下文, 股票对象
# #使用global语句可以清楚地表明变量是在外面的块定义的, 而且在本函数内
# #可以使用或者修改这些变量(前提是必须先声明为全局变量, 以便告诉python
# #解释器这些变量是全局的(主块和函数块共有的)已经是在外部--主代码块里--定义好了的,
# # 或者是本代码块要传递到主代码块里的变量).
#==============================================================================class Plotter(object):u'''Plotter class to make picture of stock's ohlcv data'''# define class varptype_dict={'lday':u'日','lc5':u'五分钟'} # 这里声明的变量, 不用加global修饰符, 也是全局变量def __init__(self, context, stk, subset, quanxi=None):self.context = contextself.stk = stkself.subset = subsetself.quanxi = quanxiself.fig = Noneself.ax1 = self.ax2 = self.ax3 = Noneself.candle_colors = Noneself.length = Noneself.x = Nonedef plot_candle_only(self, savefig=False):u'''仅绘制主图 '''self.layout(volume_bars=False)self.candles()self.primary_curves()self.savfig(savefig)#fig #在ipython console里显示整个图表def plot_candle_vol(self, savefig=False):u'''主图+成交量图'''self.layout(volume_bars=True)self.candles() self.primary_curves() self.vol_bars()self.savfig(savefig)passdef plot_timing(self, timing=None, savefig=False):u'''画图: timing之K线性形态candles + (MA20, MA120) + 形态标注volume barpara: timing: Series, note: str, {'csyx', 'szx', etc}, 长上影线, 十字星等'''self.layout(volume_bars=True)self.candles() self.primary_curves() self.vol_bars()self.annotate(timing)self.savfig(savefig)def layout(self, volume_bars=True):u''''''if volume_bars:self.fig, (self.ax1, self.ax2) = plt.subplots(2, 1, sharex=True, gridspec_kw={'height_ratios': [3,1]} )else:self.fig,self.ax1 = plt.subplots(1,1)#res = fig, ax1#return resdef candles(self,col_func=None):u'''subset: slice object, slice(start,stop,step)that is:slice(100)slice(-100,None)slice(100,200)slice(-200,-100,2)'2011-09''2017''''def default_col_func(index, open1, close, low, high):return 'black' if open1[index] > close[index] else 'white' # r g b cyan black whitesubset=self.subsetcol_func= col_func or default_col_funcohlc = self.stk.ohlc[subset] if self.subset else self.stk.ohlcopen1,high,low,close = ohlc.open, ohlc.high, ohlc.low, ohlc.closeself.length = length = len(close)self.x = x = np.arange(length)candle_colors = [col_func(i, open1, close, low, high) for i in x]self.candle_colors = candle_colors# 计算出 每日的开盘价/收盘价里的最大值和最小值oc_min = pd.concat([open1, close], axis=1).min(axis=1)oc_max = pd.concat([open1, close], axis=1).max(axis=1)#candles = ax1.bar(x, oc_max-oc_min, bottom=oc_min, color=candle_colors, linewidth=0)#lines = ax1.vlines(x + 0.4, low, high, color=candle_colors, linewidth=1)candles = self.ax1.bar(x-0.4, oc_max-oc_min, bottom=oc_min, color=candle_colors, linewidth=0.2, edgecolor='black')shadlines_up = self.ax1.vlines(x, oc_max, high, color=['black']* length, linewidth=0.3)shadlines_dn = self.ax1.vlines(x, low, oc_min, color=['black']* length, linewidth=0.3)#print candles.__class__, shadlines_up.__class__, shadlines_dn.__class__isinstance(candles, matplotlib.container.BarContainer) == Trueisinstance(shadlines_dn, matplotlib.collections.LineCollection)isinstance(shadlines_up, matplotlib.collections.LineCollection)self.custom_figure()self.custom_yaxis()passdef primary_curves(self): #subset=None):#ohlc = stk.ohlc[subset] if subset else stk.ohlc#close = ohlc.closesubset = self.subsetif (isinstance(self.stk.ma20, pd.Series) and isinstance(self.stk.cyc61, pd.Series)):ma20 = self.stk.ma20[subset] if subset else self.stk.ma20cyc61 = self.stk.cyc61[subset] if subset else self.stk.cyc61indicators = [ma20, cyc61]x=self.xfor ind in indicators:self.ax1.plot(x, ind, 'o-', lw=0.1, markersize=0.7, markeredgewidth=0.1, label=ind.name) #带圆圈标记的实线self.ax1.legend()self.custom_xaxis(ax=self.ax1)def secondary_curves(self, ax):# ohlc = stk.ohlc[subset] if subset else stk.ohlcpassdef vol_bars(self):u''''''subset = self.subsetohlc = self.stk.ohlc[subset] if subset else self.stk.ohlcvolume = ohlc['volume']#open1,high,low,close = ohlc.open, ohlc.high, ohlc.low, ohlc.closex = self.xvolume_scale = Nonescaled_volume = volumeif volume.max() > 1000*1000:volume_scale = u'百万股' #'M'scaled_volume = volume / 1000.0/1000.0elif volume.max() > 1000:volume_scale = u'千股'scaled_volume = volume / 1000.0self.ax2.bar(x-0.4, scaled_volume, color=self.candle_colors, linewidth=0.2, edgecolor='black')volume_title = 'Volume'if volume_scale:volume_title = 'Volume (%s)' % volume_scale#ax2.set_title(volume_title) # 太难看了self.ax2.set_ylabel(volume_title, fontdict=None)self.ax2.xaxis.grid(False)#plt.setp(ax.get_xticklabels(minor=False), fontsize=6)self.custom_xaxis(self.ax2)passdef annotate(self, timing):u'''在主图上标注给定的K线形态:param:timing: event of Series of k-patternnote: str, 对应于事件的标注文本example:>>> plotter.annotate(csyx) #长上影线'''#ax=plt.gca()#xx = self.action.p_DJR.indexc = self.stk.ohlc.close[self.subset] if self.subset else self.stk.ohlc.closeself.timing = timing[self.subset] if self.subset else timingptn_dt = c[self.timing].index # True 逻辑选择 选出长上影线的时机(日期索引)note = self.note = self.timing.name[:3]ax = self.ax1xx = map(lambda dt: c.index.get_loc(dt), ptn_dt) yy = c * 1.1#strings = self.action['value'].values.astype(str)#strings = self.action['bonus'].values.astype(str)#strings = map(lambda x: u'派'+str(x), strings)for i,x in enumerate(xx):#ax.text(x, yy[i], strings[i])print i, c.index[x], x, yy[x], c[x]ax.annotate(note, xy=(x, yy[x]*1.05/1.1), xytext=(x, yy[x]+0.0),arrowprops=dict(facecolor='black', color='red',#shrink=0.05,arrowstyle='->',),)def custom_yaxis(self):u'''# 设定 Y 轴上的刻度#==================================================================================================================================================python - Matplotlib log scale tick label number formatting - Stack Overflow https://stackoverflow.com/questions/21920233/matplotlib-log-scale-tick-label-number-formatting每个坐标轴都有7大属性:ax1.set_yscale, ylim, ylabel, yticks, yticklabels, ybound, ymargin'''#use_expo=True; expbase=1.1 # 2 e 10yaxis= self.ax1.get_yaxis()isinstance(yaxis, matplotlib.axis.YAxis)self.ax1.set_yscale(value='log', basey=expbase)passdef custom_figure(self):u''' '''# 依据绘图数据的长度和时间轴的比例尺(比如1:16)确定图表的长度: #fig = plt.gcf()#fig.set_size_inches(18.5, 10.5)self.fig.set_size_inches(self.length/16.0, 6) # /18 /20 /16 diff time-scalestitle = u'%s(%s)%s周期蜡烛图'%(self.context.name, self.context.symbol, self.ptype_dict[self.context.ptype])self.ax1.set_title(title)passdef custom_xaxis(self, ax):u''''''subset = self.subsetohlc = self.stk.ohlc[subset] if subset else self.stk.ohlcclose = ohlc.closelength = self.length # len(close)ax.set_xlim(-2, length+10)xaxis= ax.get_xaxis()yaxis= ax.get_yaxis()# 设定 X 轴上的主刻度/次刻度位置#==================================================================================================================================================mdindex, wdindex, sdindex= self.ohlc_find_idx_fdim(close) xMajorLocator= FixedLocator(np.array(mdindex)) # 针对主刻度,实例化一个"固定式刻度定位"xMinorLocator= FixedLocator(np.array(wdindex)) # 确定 X 轴的 MinorLocator# 确定 X 轴的 MajorFormatter 和 MinorFormatter # 自定义的刻度格式(应该是一个function)datelist = close.index.date.tolist() def x_major_formatter_1(idx, pos=None): u'''格式函数的功能: idx 是位置location, 依据位置, 返回对应的日期刻度标签'''#return datelist[idx].strftime('%Y-%m-%d')return datelist[idx].strftime('%m\n%Y')def x_major_formatter_2(idx, pos=None):return datelist[idx].strftime('\n\n%m\n%Y')def x_minor_formatter_1(idx, pos=None):#return datelist[idx].strftime(u'一\n%d') # 周一return datelist[idx].strftime(u'M\n%d') # 周一def x_minor_formatter_2(idx, pos=None):return datelist[idx].strftime('%m-%d')xMajorFormatter_1 = FuncFormatter(x_major_formatter_1)xMajorFormatter_2 = FuncFormatter(x_major_formatter_2)xMinorFormatter_1 = FuncFormatter(x_minor_formatter_1)# 设定 X 轴的 Locator 和 Formatterxaxis.set_major_locator(xMajorLocator)xaxis.set_minor_locator(xMinorLocator)xaxis.set_major_formatter(xMajorFormatter_1)if self.ax2 is None:xaxis.set_major_formatter(xMajorFormatter_2)xaxis.set_minor_formatter(xMinorFormatter_1)if self.ax2 is None: # 仅绘制主图# 设定不显示的刻度标签:if ax==self.ax1:plt.setp(ax.get_xticklabels(minor=False), visible=True) #主刻度标签 可见plt.setp(ax.get_xticklabels(minor=True), visible=True) #次刻度标签 可见elif ((self.ax1 != None) and (self.ax2 != None)): # case of 主图+成交量图if ax==self.ax2:plt.setp(ax.get_xticklabels(minor=True), visible=False) #次刻度标签 隐藏elif ax==self.ax1:plt.setp(ax.get_xticklabels(minor=False), visible=False) #主刻度标签 隐藏# 设定 X 轴主刻度和次刻度标签的样式(字体大小)for malabel in ax.get_xticklabels(minor=False):malabel.set_fontsize(12) # 6号也太小了#malabel.set_horizontalalignment('right')#malabel.set_rotation('45')# if ax == ax1 or ax2:for milabel in ax.get_xticklabels(minor=True):milabel.set_fontsize(12) # 5 太小了#milabel.set_horizontalalignment('right')#milabel.set_rotation('45')#milabel.set_fontdict=myfont#milabel.set_fontproperties=myfont#milabel.set_prop=myfont# 设置两个坐标轴上的 grid#==================================================================================================================================================#xaxis_2.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.2)xaxis.grid(True, 'major', color='0.3', linestyle='dotted', linewidth=0.3)xaxis.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1)#yaxis_2.grid(True, 'major', color='0.3', linestyle='dashed', linewidth=0.2)yaxis.grid(True, 'major', color='0.3', linestyle='dotted', linewidth=0.1)yaxis.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1)yaxis.get_major_ticks()[2].label = \Text(0,28.1024,u'28.10 $\\mathdefault{1.1^{35}}$')def ohlc_find_idx_fdim(self, ohlc):u'''功能: index of first trading-day in month ------- 获取每个月的第一个交易日的下标(又称0轴索引). 从数据框的时间索引里提取对应的日期, 然后检索出下标.- 另外, 也获取每个交易周的第一个交易日的下标输入:- ohlc: pandas数据框返回:- list例子:------->>> mdindex, wdindex, sdindex= ohlc_find_idx_fdim(ohlc_last60)'''#datelist= [ datetime.date(int(ys), int(ms), int(ds)) for ys, ms, ds in [ dstr.split('-') for dstr in pdata[u'日期'] ] ]#last60 = ohlc[-250:]last60 = ohlcdatelist = last60.index.date.tolist()# 确定 X 轴的 MajorLocatormdindex= [] # 每个月第一个交易日在所有日期列表中的 index, 月日期索引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))wdindex =[] # weekday index, 每周的第一个交易日的索引for y in sorted(years):weeknum= set([int(d.strftime('%U')) for d in datelist if d.year==y])for w in sorted(weeknum):wd= min([dt for dt in datelist if dt.year==y and int(dt.strftime('%U'))==w])wdindex.append(datelist.index(wd))#==============================================================================# wdindex= [] # 每周第一个交易日在所有日期列表中的 index, 每周的第一个交易日的索引# for d in datelist:# if d.weekday() == 0: wdindex.append(datelist.index(d))# #==============================================================================# === 检索每个季末交易日的下标: sdindex: end of season day index ===# 对ndarray or list 进行逻辑运输时, 需要用np.logical_or()方法才是正确的方法:#filter1= (months==3) or (months==6)#filter1= (months==3).tolist() or (months==6).tolist() #ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()dt= last60.index.date # 得到ndarray of date, # dti= last60.index # 得到pd.ts.index.DtetimeIndex of date, months= last60.index.month #得到ndarray of month, 取值范围为: 1~12# nextbar_m= last60.index.shift(1, freq='D').month # 当移动时间下标时, 数据的频率不能为空# 这样做还是有问题的, pd的做法是: 引用未来1 Day的日期, 也就是当前的日期+1day的日期# 比如: 当前的日期是 2016-12-30, 2017-01-03# .shift(1)的日期是: 2016-12-31, 2017-01-04# ==> 误判了4季末的日期变更线坐标位置# 解决办法: 应该让freq= 'per index bar', 查询一下pd的doc吧... # 变通办法: .drop first element value or .delete(0) the first location# and then .insert one value at end, to make the same length# 变通办法之: 用 freq='BQ', 来生成一个dtindex:# pd.date_range(start=mi[0], end=mi[-1], freq='BQ') # BQ business quarter endfrequency# Time Series / Date functionality — pandas 0.19.2 documentation # http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases # # === 还有更简洁的办法: 就是dti.quarter属性直接提供了第几个季节 ===i_index= last60.index.delete(0) i_index= i_index.insert(-1, last60.index[-1]) # -1 表示最后一个下标位置nextbar_m= i_index.month # endMar= np.logical_and(months==3, nextbar_m==4)endJun= np.logical_and(months==6, nextbar_m==7)endSep= np.logical_and(months==9, nextbar_m==10)endDec= np.logical_and(months==12, nextbar_m==1)tmp1= np.logical_or(endMar, endJun)tmp2= np.logical_or(endSep, endDec)mask= np.logical_or(tmp1, tmp2)sdindex= [dt.tolist().index(i) for i in dt[mask] ]#print u'\n==> 季节变更坐标线:'#print u' 每个季末的x轴的位置下标: %r' % sdindex#print u' 每个季末的x轴的位置时间: %r' % last60.index[sdindex]return mdindex, wdindex, sdindexdef savfig(self, savefig=False):if savefig:now = datetime.datetime.now()now_s = now.strftime('%Y%m%d_%H%M%S_')microsec = str(now.microsecond)#fn= '%s_%s_%s.pdf' %(context.name, now_s, microsec )#fig.savefig(fn, dpi=300)#print u'\n==> 该pdf文件被创建: %s' %fnfn= '%s_%s_%s.png' %(self.context.name, now_s, microsec )self.fig.savefig(fn, dpi=300)print u'\n==> 该png文件被创建: %s' %fnpassif __name__ == '__main__':pass代码(2017.11)
- 主块代码
- 绘图模块的代码
- 结果展示
结果展示1:
结果展示2:
主块代码: test1_load.py
# -*- coding: utf-8 -*-import pandas as pdimport amipy as ami
reload(ami)
import do_plot as dp
reload(dp)#context = ami.Context('600699.SH')
context = ami.Context('000911.SZ')
stk = ami.Stock(context) #None,None)
stk.grab_data_tdxlday(context, num_days=None)
stk.ohlc = stk.ohlc_rawstk.ma20 = ami.TTR.sma(stk.ohlc.close, 20)
stk.cyc61 = ami.TTR.sma(stk.ohlc.close, 120)
subset = slice(-120,None) # '2017-07' '2017'
subset = '2017' #slice(-120,None) # '2017-07' '2017'datas = (context, stk, subset)# 仅绘制主图
#dp.plot_candle_only(datas)# 主图+成交量图
dp.plot_candle_vol(datas)
绘图模块代码 do_plot.py
# -*- coding: utf-8 -*-#import sysimport numpy as np
import pandas as pdimport matplotlib
import matplotlib.pyplot as plt
from matplotlib.ticker import (FixedLocator, #MultipleLocator, #LogLocator, #NullFormatter, FuncFormatter, #LogFormatter )
from matplotlib.font_manager import FontProperties
myfont = FontProperties(fname=r"c:\windows\fonts\msyh.ttf") #size可不用指定
matplotlib.rcParams['axes.unicode_minus'] = False#import amipy as ami#==============================================================================
# Python中的作用域及global用法 - Summer_cool - 博客园
# https://www.cnblogs.com/summer-cool/p/3884595.html
#
# 函数定义了本地作用域，而模块定义的是全局作用域。
# 如果想要在函数内定义全局作用域，需要加上global修饰符。
#
# 变量名解析：LEGB原则
# 当在函数中使用未认证的变量名时，Python搜索４个作用域:
# [本地作用域(L-local)(函数内部声明但没有使用global的变量)，
# 之后是上一层结构def或者lambda的本地作用域(E-enclosure),
# 之后是全局作用域(G-global)（函数中使用global声明的变量或在模块层声明的变量），
# 最后是内置作用域(B)（即python的内置类和函数等）］
# 并且在第一处能够找到这个变量名的地方停下来。
# 如果变量名在整个的搜索过程中都没有找到，Python就会报错。
#
# 补：上面的变量规则只适用于简单对象，当出现引用对象的属性时，则有另一套搜索规则:
# 属性引用搜索一个或多个对象，而不是作用域，并且有可能涉及到所谓的"继承"
# 补2:global修饰符在python里的一个独特现象:
# 在模块层面定义的变量（无需global修饰），
# 如果在函数中没有再定义同名变量，可以在函数中当做全局变量使用.
# 如果在函数中要对它重新赋值的话, 则必须在本函数中事先声明为全局变量, 否则会抛出异常.
#
# #先声明全局本函数里用到的全局变量: 图表, 上下文, 股票对象
# #使用global语句可以清楚地表明变量是在外面的块定义的, 而且在本函数内
# #可以使用或者修改这些变量(前提是必须先声明为全局变量, 以便告诉python
# #解释器这些变量是全局的(主块和函数块共有的)已经是在外部--主代码块里--定义好了的,
# # 或者是本代码块要传递到主代码块里的变量).
#==============================================================================
global fig, ax1, ax2, ax3 # 模块级变量名, 分别代表: 整个图表, 子图1/2/3
global context, stk, subset # 模块级变量名
global candle_colors, length
ax2=ax3=None #初始化 ax2/ax3 子图实例为None, #fig和ax1可以不用初始化, 因为调用layout()后总是要返回fig和ax1的
ptype_dict={'lday':u'日','lc5':u'五分钟'} # 这里声明的变量, 不用加global修饰符, 也是全局变量def layout(volume_bars=True):u''''''global fig, ax1, ax2, ax3if volume_bars:fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, gridspec_kw={'height_ratios': [3,1]} )res = fig, (ax1,ax2)else:fig,ax1 = plt.subplots(1,1)res = fig, ax1return resdef candles(#subset=None,col_func=None):u'''subset: slice object, slice(start,stop,step)that is:slice(100)slice(-100,None)slice(100,200)slice(-200,-100,2)'2011-09''2017''''global context, stk, subsetglobal candle_colors # 可能会被以后的函数所用到(比如画成交量柱子)global lengthdef default_col_func(index, open1, close, low, high):return 'black' if open1[index] > close[index] else 'white' # r g b cyan black whitecol_func= col_func or default_col_funcohlc = stk.ohlc[subset] if subset else stk.ohlcopen1,high,low,close = ohlc.open, ohlc.high, ohlc.low, ohlc.closelength = len(close)x = np.arange(length)candle_colors = [col_func(i, open1, close, low, high) for i in x]# 计算出 每日的开盘价/收盘价里的最大值和最小值oc_min = pd.concat([open1, close], axis=1).min(axis=1)oc_max = pd.concat([open1, close], axis=1).max(axis=1)#candles = ax1.bar(x, oc_max-oc_min, bottom=oc_min, color=candle_colors, linewidth=0)#lines = ax1.vlines(x + 0.4, low, high, color=candle_colors, linewidth=1)candles = ax1.bar(x-0.4, oc_max-oc_min, bottom=oc_min, color=candle_colors, linewidth=0.2, edgecolor='black')shadlines_up = ax1.vlines(x, oc_max, high, color=['black']* length, linewidth=0.3)shadlines_dn = ax1.vlines(x, low, oc_min, color=['black']* length, linewidth=0.3)#print candles.__class__, shadlines_up.__class__, shadlines_dn.__class__isinstance(candles, matplotlib.container.BarContainer) == Trueisinstance(shadlines_dn, matplotlib.collections.LineCollection)isinstance(shadlines_up, matplotlib.collections.LineCollection)custom_figure()custom_yaxis()passdef primary_curves(): #subset=None):#ohlc = stk.ohlc[subset] if subset else stk.ohlc#close = ohlc.closeif (isinstance(stk.ma20, pd.Series) and isinstance(stk.cyc61, pd.Series)):ma20 = stk.ma20[subset] if subset else stk.ma20cyc61 = stk.cyc61[subset] if subset else stk.cyc61length = len(ma20)x = np.arange(length)indicators = [ma20, cyc61]for ind in indicators:ax1.plot(x, ind, 'o-', lw=0.1, markersize=0.7, markeredgewidth=0.1, label=ind.name) #带圆圈标记的实线ax1.legend()custom_xaxis(ax=ax1)def secondary_curves(ax,subset=None):
# ohlc = stk.ohlc[subset] if subset else stk.ohlcpassdef vol_bars():u''''''global stk, subsetohlc = stk.ohlc[subset] if subset else stk.ohlcvolume = ohlc['volume']#open1,high,low,close = ohlc.open, ohlc.high, ohlc.low, ohlc.closex = np.arange(length)volume_scale = Nonescaled_volume = volumeif volume.max() > 1000*1000:volume_scale = u'百万股' #'M'scaled_volume = volume / 1000.0/1000.0elif volume.max() > 1000:volume_scale = u'千股'scaled_volume = volume / 1000.0ax2.bar(x-0.4, scaled_volume, color=candle_colors, linewidth=0.2, edgecolor='black')volume_title = 'Volume'if volume_scale:volume_title = 'Volume (%s)' % volume_scaleax2.set_title(volume_title)ax2.xaxis.grid(False)#plt.setp(ax.get_xticklabels(minor=False), fontsize=6)custom_xaxis(ax2)passdef custom_yaxis():u'''# 设定 Y 轴上的刻度#==================================================================================================================================================python - Matplotlib log scale tick label number formatting - Stack Overflow https://stackoverflow.com/questions/21920233/matplotlib-log-scale-tick-label-number-formatting'''#use_expo=True; expbase=1.1 # 2 e 10yaxis= ax1.get_yaxis()isinstance(yaxis, matplotlib.axis.YAxis)ax1.set_yscale(value='log', basey=expbase)passdef custom_figure():u''' '''# 依据绘图数据的长度和时间轴的比例尺(比如1:16)确定图表的长度: #fig = plt.gcf()#fig.set_size_inches(18.5, 10.5)fig.set_size_inches(length/16.0, 6) # /18 /20 /16 diff time-scalestitle = u'%s(%s)%s周期蜡烛图'%(context.name, context.symbol, ptype_dict[context.ptype])ax1.set_title(title)passdef custom_xaxis(ax):u''''''global ax1, ax2, ax3ohlc = stk.ohlc[subset] if subset else stk.ohlcclose = ohlc.close#length = len(close)ax.set_xlim(-2, length+10)xaxis= ax.get_xaxis()yaxis= ax.get_yaxis()# 设定 X 轴上的主刻度/次刻度位置#==================================================================================================================================================mdindex, wdindex, sdindex= ohlc_find_idx_fdim(close) xMajorLocator= FixedLocator(np.array(mdindex)) # 针对主刻度,实例化一个"固定式刻度定位"xMinorLocator= FixedLocator(np.array(wdindex)) # 确定 X 轴的 MinorLocator# 确定 X 轴的 MajorFormatter 和 MinorFormatter # 自定义的刻度格式(应该是一个function)datelist = close.index.date.tolist() def x_major_formatter_1(idx, pos=None): u'''格式函数的功能: idx 是位置location, 依据位置, 返回对应的日期刻度标签'''#return datelist[idx].strftime('%Y-%m-%d')return datelist[idx].strftime('%m\n%Y')def x_major_formatter_2(idx, pos=None):return datelist[idx].strftime('\n\n%m\n%Y')def x_minor_formatter_1(idx, pos=None):#return datelist[idx].strftime(u'一\n%d') # 周一return datelist[idx].strftime(u'M\n%d') # 周一def x_minor_formatter_2(idx, pos=None):return datelist[idx].strftime('%m-%d')xMajorFormatter_1 = FuncFormatter(x_major_formatter_1)xMajorFormatter_2 = FuncFormatter(x_major_formatter_2)xMinorFormatter_1 = FuncFormatter(x_minor_formatter_1)# 设定 X 轴的 Locator 和 Formatterxaxis.set_major_locator(xMajorLocator)xaxis.set_minor_locator(xMinorLocator)xaxis.set_major_formatter(xMajorFormatter_1)if ax2 is None:xaxis.set_major_formatter(xMajorFormatter_2)xaxis.set_minor_formatter(xMinorFormatter_1)if ax2 is None: # 仅绘制主图# 设定不显示的刻度标签:if ax==ax1:plt.setp(ax.get_xticklabels(minor=False), visible=True) #主刻度标签 可见plt.setp(ax.get_xticklabels(minor=True), visible=True) #次刻度标签 可见elif ((ax1 != None) and (ax2 != None)): # case of 主图+成交量图if ax==ax2:plt.setp(ax.get_xticklabels(minor=True), visible=False) #次刻度标签 隐藏elif ax==ax1:plt.setp(ax.get_xticklabels(minor=False), visible=False) #主刻度标签 隐藏# 设定 X 轴主刻度和次刻度标签的样式(字体大小)for malabel in ax.get_xticklabels(minor=False):malabel.set_fontsize(12) # 6号也太小了#malabel.set_horizontalalignment('right')#malabel.set_rotation('45')# if ax == ax1 or ax2:for milabel in ax.get_xticklabels(minor=True):milabel.set_fontsize(12) # 5 太小了#milabel.set_horizontalalignment('right')#milabel.set_rotation('45')#milabel.set_fontdict=myfont#milabel.set_fontproperties=myfont#milabel.set_prop=myfont# 设置两个坐标轴上的 grid#==================================================================================================================================================#xaxis_2.grid(True, 'major', color='0.3', linestyle='solid', linewidth=0.2)xaxis.grid(True, 'major', color='0.3', linestyle='dotted', linewidth=0.3)xaxis.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1)#yaxis_2.grid(True, 'major', color='0.3', linestyle='dashed', linewidth=0.2)yaxis.grid(True, 'major', color='0.3', linestyle='dotted', linewidth=0.1)yaxis.grid(True, 'minor', color='0.3', linestyle='dotted', linewidth=0.1)def ohlc_find_idx_fdim(ohlc):u'''功能: index of first trading-day in month ------- 获取每个月的第一个交易日的下标(又称0轴索引). 从数据框的时间索引里提取对应的日期, 然后检索出下标.- 另外, 也获取每个交易周的第一个交易日的下标输入:- ohlc: pandas数据框返回:- list例子:------->>> mdindex, wdindex, sdindex= ohlc_find_idx_fdim(ohlc_last60)'''#datelist= [ datetime.date(int(ys), int(ms), int(ds)) for ys, ms, ds in [ dstr.split('-') for dstr in pdata[u'日期'] ] ]last60 = ohlc[-250:]datelist = last60.index.date.tolist()# 确定 X 轴的 MajorLocatormdindex= [] # 每个月第一个交易日在所有日期列表中的 index, 月日期索引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))wdindex =[] # weekday index, 每周的第一个交易日的索引for y in sorted(years):weeknum= set([int(d.strftime('%U')) for d in datelist if d.year==y])for w in sorted(weeknum):wd= min([dt for dt in datelist if dt.year==y and int(dt.strftime('%U'))==w])wdindex.append(datelist.index(wd))#==============================================================================# wdindex= [] # 每周第一个交易日在所有日期列表中的 index, 每周的第一个交易日的索引# for d in datelist:# if d.weekday() == 0: wdindex.append(datelist.index(d))# #==============================================================================# === 检索每个季末交易日的下标: sdindex: end of season day index ===# 对ndarray or list 进行逻辑运输时, 需要用np.logical_or()方法才是正确的方法:#filter1= (months==3) or (months==6)#filter1= (months==3).tolist() or (months==6).tolist() #ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()dt= last60.index.date # 得到ndarray of date, # dti= last60.index # 得到pd.ts.index.DtetimeIndex of date, months= last60.index.month #得到ndarray of month, 取值范围为: 1~12# nextbar_m= last60.index.shift(1, freq='D').month # 当移动时间下标时, 数据的频率不能为空# 这样做还是有问题的, pd的做法是: 引用未来1 Day的日期, 也就是当前的日期+1day的日期# 比如: 当前的日期是 2016-12-30, 2017-01-03# .shift(1)的日期是: 2016-12-31, 2017-01-04# ==> 误判了4季末的日期变更线坐标位置# 解决办法: 应该让freq= 'per index bar', 查询一下pd的doc吧... # 变通办法: .drop first element value or .delete(0) the first location# and then .insert one value at end, to make the same length# 变通办法之: 用 freq='BQ', 来生成一个dtindex:# pd.date_range(start=mi[0], end=mi[-1], freq='BQ') # BQ business quarter endfrequency# Time Series / Date functionality — pandas 0.19.2 documentation # http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases # # === 还有更简洁的办法: 就是dti.quarter属性直接提供了第几个季节 ===i_index= last60.index.delete(0) i_index= i_index.insert(-1, last60.index[-1]) # -1 表示最后一个下标位置nextbar_m= i_index.month # endMar= np.logical_and(months==3, nextbar_m==4)endJun= np.logical_and(months==6, nextbar_m==7)endSep= np.logical_and(months==9, nextbar_m==10)endDec= np.logical_and(months==12, nextbar_m==1)tmp1= np.logical_or(endMar, endJun)tmp2= np.logical_or(endSep, endDec)mask= np.logical_or(tmp1, tmp2)sdindex= [dt.tolist().index(i) for i in dt[mask] ]#print u'\n==> 季节变更坐标线:'#print u' 每个季末的x轴的位置下标: %r' % sdindex#print u' 每个季末的x轴的位置时间: %r' % last60.index[sdindex]return mdindex, wdindex, sdindexdef plot_candle_only(datas):u'''仅绘制主图 '''global context, stk, subsetglobal fig, ax1, ax2, ax3global candle_colors, lengthcontext, stk, subset = dataslayout(volume_bars=False)candles()primary_curves()#fig #在ipython console里显示整个图表def plot_candle_vol(datas):u'''主图+成交量图'''global context, stk, subsetglobal fig, ax1, ax2, ax3global candle_colors, lengthcontext, stk, subset = dataslayout(volume_bars=True)candles() primary_curves() vol_bars()passif __name__ == '__main__':pass
