python金融大数据分析笔记----第五章（数据可视化）3 ---【金融学图表】

    # -*- coding: utf-8 -*-
    """
    Created on Mon Oct  7 15:27:08 2019
    
    @author: qwy
    """
    import mpl_finance as mpf
    import matplotlib.pyplot as plt
    import tushare as ts
    import datetime
    from matplotlib.pylab import date2num
    plt.rcParams['font.sans-serif']='Lisu'
    plt.rcParams['axes.unicode_minus']=False
    
    start = '2018-05-31'
    end = '2018-06-30'
    k_d = ts.get_k_data('600118', start, end, ktype='D')
    print(k_d.head())
    k_d.date = k_d.date.map(lambda x: date2num(datetime.datetime.strptime(x, '%Y-%m-%d')))
    quotes = k_d.values
    
    #---------------------------------------
    
    fig, ax = plt.subplots(figsize=(8, 5))
    fig.subplots_adjust(bottom=0.2)
    mpf.candlestick_ochl(ax, quotes, width=0.6, colorup='r', colordown='g', alpha=0.8)
    plt.grid(True)
    ax.xaxis_date()
    # dates on the x-axis
    ax.autoscale_view()
    plt.setp(plt.gca().get_xticklabels(), rotation=30)
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

           date    open   close    high     low   volume    code
    98   2018-05-31  20.900  20.920  21.000  20.711  51514.0  600118
    99   2018-06-01  20.910  20.970  21.477  20.781  56831.0  600118
    100  2018-06-04  20.970  21.039  21.169  20.930  38909.0  600118
    101  2018-06-05  20.990  21.308  21.368  20.980  45981.0  600118
    102  2018-06-06  21.258  21.268  21.368  21.129  35788.0  600118
    Out[9]: [None, None, None, None, None, None, None, None, None, None]
    
    
      
      
      
      
      
      
      
    

    import mpl_finance as mpf
    import matplotlib.pyplot as plt
    import tushare as ts
    import datetime
    from matplotlib.pylab import date2num
    plt.rcParams['font.sans-serif']='Lisu'
    plt.rcParams['axes.unicode_minus']=False
    
    start = '2018-05-31'
    end = '2018-06-30'
    k_d = ts.get_k_data('600118', start, end, ktype='D')
    print(k_d.head())
    k_d.date = k_d.date.map(lambda x: date2num(datetime.datetime.strptime(x, '%Y-%m-%d')))
    quotes = k_d.values
    
    #---------上面这部分代码与第1点的前半部分代码同---------
    
    fig, (ax1, ax2) = plt.subplots(2, sharex=True, figsize=(8, 6))
    mpf.candlestick_ochl(ax1, quotes, width=0.6, colorup='r', colordown='g', alpha=0.8)
    ax1.set_title('中国卫星')
    ax1.set_ylabel('index level')
    ax1.grid(True)
    ax1.xaxis_date()
    plt.bar(quotes[:,0],quotes[:,5],width=0.5)
    ax2.set_ylabel('volume')
    ax2.grid(True)
    plt.setp(plt.gca().get_xticklabels(), rotation=30)
    plt.show()
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

    from mpl_toolkits.mplot3d import Axes3D
    import matplotlib.pyplot as plt
    import numpy as np
    plt.rcParams['font.sans-serif']='Lisu'
    plt.rcParams['axes.unicode_minus']=False
    
    strike = np.linspace(50, 150, 24)
    ttm = np.linspace(0.5, 2.5, 24)
    strike, ttm = np.meshgrid(strike, ttm)
    iv = (strike - 100) ** 2 / (100 * strike) / ttm
    # generate fake implied volatilities
    
    fig = plt.figure(figsize=(9, 6))
    ax = fig.gca(projection='3d')
    
    '''同上面两行代码
    fig = plt.figure(figsize=(12, 8))
    ax = Axes3D(fig)
    '''
    
    surf = ax.plot_surface(strike, ttm, iv, rstride=2, 
                       cstride=2, cmap=plt.cm.coolwarm, 
                       linewidth=0.5, antialiased=True)
    ax.set_xlabel('strike')
    ax.set_ylabel('time-to-maturity')
    ax.set_zlabel('implied volatility')
    fig.colorbar(surf, shrink=0.5, aspect=5)
    
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

    #（模拟）隐含波动率的 3D 散点图
    fig = plt.figure(figsize=(8, 5))
    ax = fig.add_subplot(111,projection='3d')
    ax.view_init(30,60)
    ax.scatter(strike, ttm, iv, zdir='z',s=25,c='b',marker='^')
    ax.set_xlabel('strike')
    ax.set_ylabel('time-to-maturity')
    ax.set_zlabel('implied volatility')