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matplotlib画K线
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实习期间帮公司的交易平台做K线图部分，看到了peakgao大佬的高仿同花顺的代码：https://blog.csdn.net/PeakGao/article/details/105634317 大佬没有直接使用mpl_finance，看上去效果很棒。我就在后面加了键鼠交互的内容。这不是用在公司平台上的版本，只是很粗糙的添加了交互功能，代码里还有不少问题。 增加功能：鼠标移动显示数值，鼠标滚轮缩小放大，方向键左右移动。
代码部分因为不需要所以把MACD和KDJ注释掉了： # -*- coding:utf-8 -*- import tushare as ts # 获取股票数据用 import numpy as np import pandas as pd import matplotlib.pyplot as plt import matplotlib.ticker as ticker # 用于日期刻度定制 # import mpl_finance as mpf # 注释了上面一行的旧版，新版是mplfinance，少了一个下划线，运行没警告，新版绘制蜡烛图性能应该更高，但是限得比较死， # 不好加子图，后面我们把关键代码拿出来自己绘制 from mplfinance import original_flavor as mpf # 用新版库里面的旧版实现(跟mpl_finance一模一样) from matplotlib import colors as mcolors # 用于颜色转换成渲染时顶点需要的颜色格式 from matplotlib.collections import LineCollection, PolyCollection # 用于绘制直线集合和多边形集合 # import tools # 计算MACD和KDJ(没有使用talib，有缺陷) # def calc_macd(df, fastperiod=12, slowperiod=26, signalperiod=9): # ewma12 = df['close'].ewm(span=fastperiod,adjust=False).mean() # ewma26 = df['close'].ewm(span=slowperiod,adjust=False).mean() # df['dif'] = ewma12-ewma26 # df['dea'] = df['dif'].ewm(span=signalperiod,adjust=False).mean() # df['bar'] = (df['dif']-df['dea'])*2 # # df['macd'] = 0 # # series = df['dif']>0 # # df.loc[series[series == True].index, 'macd'] = 1 # return df # def calc_kdj(df): # low_list = df['low'].rolling(9, min_periods=9).min() # low_list.fillna(value=df['low'].expanding().min(), inplace=True) # high_list = df['high'].rolling(9, min_periods=9).max() # high_list.fillna(value=df['high'].expanding().max(), inplace=True) # rsv = (df['close'] - low_list) / (high_list - low_list) * 100 # df['k'] = pd.DataFrame(rsv).ewm(com=2).mean() # df['d'] = df['k'].ewm(com=2).mean() # df['j'] = 3 * df['k'] - 2 * df['d'] # # df['kdj'] = 0 # # series = df['k']>df['d'] # # df.loc[series[series == True].index, 'kdj'] = 1 # # # df.loc[series[(series == True) & (series.shift() == False)].index, 'kdjcross'] = 1 # # # df.loc[series[(series == False) & (series.shift() == True)].index, 'kdjcross'] = -1 # return df # 获取数据 df = ts.get_k_data('603986', '2019-09-01') # 获取日K线数据 df.drop('code', axis=1, inplace=True) # 剔除多余的列 # calc_macd(df) # 计算MACD值，数据存于DataFrame中 # calc_kdj(df) # 计算KDJ值，数据存于DataFrame中 y0 = df.iloc[:,0].tolist() columns = df.columns vmax = max(df['volume'].tolist()) # df = df[-150:] # 取最近的N天测试，列顺序为 date,open,close,high,low,volume,dif,dea,bar,k,d,j # 日期转换成整数序列 date_tickers=df.date.values df.date = range(0, len(df)) # 日期改变成序号 matix = df.values # 转换成绘制蜡烛图需要的数据格式(date, open, close, high, low, volume) xdates = matix[:, 0] # X轴数据(这里用的天数索引) # 设置外观效果 plt.rc('font', family='Microsoft YaHei') # 用中文字体，防止中文显示不出来 plt.rc('figure', fc='k') # 绘图对象背景图 plt.rc('text', c='#d43221') # 文本颜色 plt.rc('axes', axisbelow=True, xmargin=0, fc='k', ec='#800000', lw=1.5, labelcolor='#800000', unicode_minus=False) # 坐标轴属性(置底，左边无空隙，背景色，边框色，线宽，文本颜色，中文负号修正) plt.rc('xtick', c='#d43221') # x轴刻度文字颜色 plt.rc('ytick', c='#d43221') # y轴刻度文字颜色 plt.rc('grid', c='#800000', alpha=0.9, ls=':', lw=0.8) # 网格属性(颜色，透明值，线条样式，线宽) plt.rc('lines', lw=0.8) # 全局线宽 # 创建绘图对象和4个坐标轴 fig = plt.figure(figsize=(16, 8)) left, width = 0.05, 0.9 ax1 = fig.add_axes([left, 0.35, width, 0.6]) # left, bottom, width, height ax2 = fig.add_axes([left, 0.05, width, 0.3], sharex=ax1) # 共享ax1轴 # ax3 = fig.add_axes([left, 0.25, width, 0.2], sharex=ax1) # 共享ax1轴 # ax4 = fig.add_axes([left, 0.05, width, 0.2], sharex=ax1) # 共享ax1轴 plt.setp(ax1.get_xticklabels(), visible=False) # 使x轴刻度文本不可见，因为共享，不需要显示 # plt.setp(ax2.get_xticklabels(), visible=False) # 使x轴刻度文本不可见，因为共享，不需要显示 # plt.setp(ax3.get_xticklabels(), visible=False) # 使x轴刻度文本不可见，因为共享，不需要显示 # 绘制蜡烛图 def format_date(x, pos=None): return '' if xlen(date_tickers)-1 else date_tickers[int(x)] # 日期格式化函数，根据天数索引取出日期值 ax1.xaxis.set_major_formatter(ticker.FuncFormatter(format_date)) # 设置自定义x轴格式化日期函数 ax1.xaxis.set_major_locator(ticker.MultipleLocator(max(int(len(df)/8), 5))) # 横向最多排8个左右的日期，最少5个，防止日期太拥挤 # mpf.candlestick_ochl(ax1, matix, width=0.5, colorup='#ff3232', colordown='#54fcfc') # # 下面这一段代码，替换了上面注释的这个函数，因为上面的这个函数达不到同花顺的效果 opens, closes, highs, lows = matix[:, 1], matix[:, 2], matix[:, 3], matix[:, 4] # 取出ochl值 avg_dist_between_points = (xdates[-1] - xdates[0]) / float(len(xdates)) # 计算每个日期之间的距离 delta = avg_dist_between_points / 4.0 # 用于K线实体(矩形)的偏移坐标计算 barVerts = [((date - delta, open), (date - delta, close), (date + delta, close), (date + delta, open)) for date, open, close in zip(xdates, opens, closes) ] # 生成K线实体(矩形)的4个顶点坐标 rangeSegLow = [ ((date, low), (date, min(open, close))) for date, low, open, close in zip(xdates, lows, opens, closes) ] # 生成下影线顶点列表 rangeSegHigh = [ ((date, high), (date, max(open, close))) for date, high, open, close in zip(xdates, highs, opens, closes) ] # 生成上影线顶点列表 rangeSegments = rangeSegLow + rangeSegHigh # 上下影线顶点列表 cmap = {True: mcolors.to_rgba('#000000', 1.0), False: mcolors.to_rgba('#54fcfc', 1.0)} # K线实体(矩形)中间的背景色(True是上涨颜色，False是下跌颜色) inner_colors = [ cmap[opn < cls] for opn, cls in zip(opens, closes) ] # K线实体(矩形)中间的背景色列表 cmap = {True: mcolors.to_rgba('#ff3232', 1.0), False: mcolors.to_rgba('#54fcfc', 1.0)} # K线实体(矩形)边框线颜色(上下影线和后面的成交量颜色也共用) updown_colors = [ cmap[opn < cls] for opn, cls in zip(opens, closes) ] # K线实体(矩形)边框线颜色(上下影线和后面的成交量颜色也共用)列表 ax1.add_collection(LineCollection(rangeSegments, colors=updown_colors, linewidths=0.5, antialiaseds=False)) # 生成上下影线的顶点数据(颜色，线宽，反锯齿，反锯齿关闭好像没效果) ax1.add_collection(PolyCollection(barVerts, facecolors=inner_colors, edgecolors=updown_colors, antialiaseds=False, linewidths=0.5)) # 生成多边形(矩形)顶点数据(背景填充色，边框色，反锯齿，线宽) # 绘制均线 mav_colors = ['#ffffff', '#d4ff07', '#ff80ff', '#00e600', '#02e2f4', '#ffffb9', '#2a6848'] # 均线循环颜色 mav_period = [5, 10, 20, 30, 60, 120, 180] # 定义要绘制的均线周期，可增减 n = len(df) for i in range(len(mav_period)): if n >= mav_period[i]: mav_vals = df['close'].rolling(mav_period[i]).mean().values ax1.plot(xdates, mav_vals, c=mav_colors[i%len(mav_colors)], label='MA'+str(mav_period[i])) ax1.set_title('K线图') # 标题 ax1.grid(True) # 画网格 ax1.legend(loc='upper right') # 图例放置于右上角 ax1.xaxis_date() # 好像要不要效果一样？ # 绘制成交量和成交量均线（5日，10日） # ax2.bar(xdates, matix[:, 5], width= 0.5, color=updown_colors) # 绘制成交量柱状图 barVerts = [((date - delta, 0), (date - delta, vol), (date + delta, vol), (date + delta, 0)) for date, vol in zip(xdates, matix[:,5]) ] # 生成K线实体(矩形)的4个顶点坐标 ax2.add_collection(PolyCollection(barVerts, facecolors=inner_colors, edgecolors=updown_colors, antialiaseds=False, linewidths=0.5)) # 生成多边形(矩形)顶点数据(背景填充色，边框色，反锯齿，线宽) if n>=5: # 5日均线，作法类似前面的均线 vol5 = df['volume'].rolling(5).mean().values ax2.plot(xdates, vol5, c='y', label='VOL5') if n>=10: # 10日均线，作法类似前面的均线 vol10 = df['volume'].rolling(10).mean().values ax2.plot(xdates, vol10, c='w', label='VOL10') ax2.yaxis.set_ticks_position('right') # y轴显示在右边 ax2.legend(loc='upper right') # 图例放置于右上角 ax2.grid(True) # 画网格 # ax2.set_ylabel('成交量') # y轴名称 # # 绘制MACD # difs, deas, bars = matix[:, 6], matix[:, 7], matix[:, 8] # 取出MACD值 # ax3.axhline(0, ls='-', c='g', lw=0.5) # 水平线 # ax3.plot(xdates, difs, c='w', label='DIFF') # 绘制DIFF线 # ax3.plot(xdates, deas, c='y', label='DEA') # 绘制DEA线 # # ax3.bar(xdates, df['bar'], width= 0.05, color=bar_colors) # 绘制成交量柱状图(发现用bar绘制，线的粗细不一致，故使用下面的直线列表) # cmap = {True: mcolors.to_rgba('r', 1.0), False: mcolors.to_rgba('g', 1.0)} # MACD线颜色，大于0为红色，小于0为绿色 # bar_colors = [ cmap[bar > 0] for bar in bars ] # MACD线颜色列表 # vlines = [ ((date, 0), (date, bars[date])) for date in range(len(bars)) ] # 生成MACD线顶点列表 # ax3.add_collection(LineCollection(vlines, colors=bar_colors, linewidths=0.5, antialiaseds=False)) # 生成MACD线的顶点数据(颜色，线宽，反锯齿) # ax3.legend(loc='upper right') # 图例放置于右上角 # ax3.grid(True) # 画网格 # # 绘制KDJ # K, D, J = matix[:, 9], matix[:, 10], matix[:, 11] # 取出KDJ值 # ax4.axhline(0, ls='-', c='g', lw=0.5) # 水平线 # ax4.yaxis.set_ticks_position('right') # y轴显示在右边 # ax4.plot(xdates, K, c='y', label='K') # 绘制K线 # ax4.plot(xdates, D, c='c', label='D') # 绘制D线 # ax4.plot(xdates, J, c='m', label='J') # 绘制J线 # ax4.legend(loc='upper right') # 图例放置于右上角 # ax4.grid(True) # 画网格 # set useblit = True on gtkagg for enhanced performance from matplotlib.widgets import Cursor # 处理鼠标 cursor = Cursor(ax1, useblit=True, color='w', linewidth=0.5, linestyle='--') # 鼠标交互 print(df) y1 = df.iloc[:,1].tolist() y2 = df.iloc[:,2].tolist() y3 = df.iloc[:,3].tolist() y4 = df.iloc[:,4].tolist() y5 = df.iloc[:,5].tolist() len_y = len(y0) x = range(len_y) y = [y0,y1,y2,y3,y4,y5] text = [] for yi in y: # _y = [y[-1]]*len_y # text0 = plt.text(len_y-1,y[-1],str(y[-1]),fontsize = 10) _y = [yi[-1]]*len_y texti = plt.text(len_y-1,yi[-1],str(yi[-1]),fontsize = 10) text += [texti] def motion(event): axtemp=event.inaxes x_min, x_max = axtemp.get_xlim() try: for i in range(len(y)): yi = y[i] texti = text[i] temp = yi[int(np.round(event.xdata))] for j in range(len_y): _y[j] = temp ###### # text0.set_position((event.xdata, temp+250)) texti.set_position((x_min, vmax*3-(vmax)/10*i)) texti.set_text(columns[i] + ':' + str(temp)) # print(texti) fig.canvas.draw_idle() # 绘图动作实时反映在图像上 except: pass # 键盘方向键左右拖动 def on_key_press(event): axtemp=event.inaxes x_min, x_max = axtemp.get_xlim() step = (x_max - x_min) // 20 + 1 if event.key == 'right': axtemp.set(xlim=(x_min + step, x_max + step)) elif event.key == 'left': axtemp.set(xlim=(x_min - step, x_max - step)) x_min, x_max = axtemp.get_xlim() try: for i in range(len(y)): yi = y[i] texti = text[i] temp = yi[int(np.round(event.xdata))] for j in range(len_y): _y[j] = temp ###### # text0.set_position((event.xdata, temp+250)) texti.set_position((x_min, vmax*3-(vmax)/10*i)) texti.set_text(columns[i] + ':' + str(temp)) except: pass fig.canvas.draw_idle() # 滚轮缩放 def scroll(event): axtemp=event.inaxes x_min, x_max = axtemp.get_xlim() fanwei_x = (x_max - x_min) / 10 if event.button == 'up': axtemp.set(xlim=(x_min + fanwei_x, x_max - fanwei_x)) ax1.xaxis.set_major_locator(ticker.MultipleLocator(max(int((x_max-x_min)/8), 5))) # 横向最多排8个左右的日期，最少5个，防止日期太拥挤 elif event.button == 'down': axtemp.set(xlim=(x_min - fanwei_x, x_max + fanwei_x)) ax1.xaxis.set_major_locator(ticker.MultipleLocator(max(int((x_max-x_min)/8), 5))) # 横向最多排8个左右的日期，最少5个，防止日期太拥挤 x_min, x_max = axtemp.get_xlim() try: for i in range(len(y)): yi = y[i] texti = text[i] temp = yi[int(np.round(event.xdata))] for j in range(len_y): _y[j] = temp ###### # text0.set_position((event.xdata, temp+250)) texti.set_position((x_min, vmax*3-(vmax)/10*i)) texti.set_text(columns[i] + ':' + str(temp)) except: pass fig.canvas.draw_idle() fig.canvas.mpl_connect('motion_notify_event', motion) fig.canvas.mpl_connect('key_press_event', on_key_press) fig.canvas.mpl_connect('scroll_event', scroll) plt.show()
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