用matplotlib获取雅虎股票数据并作图

matplotlib有一个finance子模块提供了一个获取雅虎股票数据的api接口：quotes_historical_yahoo_ochl

感觉非常好用！

示例一

       获取数据并作折线图

import matplotlib.pyplot as plt
from matplotlib.finance import quotes_historical_yahoo_ochl  
from matplotlib.dates import YearLocator, MonthLocator, DateFormatter  
import datetime  

plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

ticker = '600028.ss'
date1 = datetime.date( 2015, 1, 10 )  
date2 = datetime.date( 2016, 1, 10 )  
  
daysFmt  = DateFormatter('%m-%d-%Y')  
  
quotes = quotes_historical_yahoo_ochl(ticker, date1, date2)  
if len(quotes) == 0:  
    raise SystemExit
print(quotes[1])

dates = [q[0] for q in quotes]  
opens = [q[1] for q in quotes]
closes = [q[2] for q in quotes]
  
fig = plt.figure()  
ax = fig.add_subplot(111)  
ax.plot_date(dates, opens, '-')  
  
# format the ticks  
ax.xaxis.set_major_formatter(daysFmt)  
ax.autoscale_view()  
  
# format the coords message box  
def price(x): 
    return '$%1.2f'%x 
    
ax.fmt_xdata = DateFormatter('%Y-%m-%d')  
ax.fmt_ydata = price  
ax.grid(True)

fig.autofmt_xdate()  
plt.title('中国石化 600028')
plt.show() 

效果图：

示例二

      获取数据，并作蜡烛图

import matplotlib.pyplot as plt

from matplotlib.dates import DateFormatter, WeekdayLocator, DayLocator, MONDAY,YEARLY
from matplotlib.finance import quotes_historical_yahoo_ohlc, candlestick_ohlc

plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

ticker = '600028' # 600028 是"中国石化"的股票代码
ticker += '.ss'   # .ss 表示上证 .sz表示深证

date1 = (2015, 8, 1) # 起始日期，格式：(年，月，日)元组
date2 = (2016, 1, 1)  # 结束日期，格式：(年，月，日)元组


mondays = WeekdayLocator(MONDAY)            # 主要刻度
alldays = DayLocator()                      # 次要刻度
#weekFormatter = DateFormatter('%b %d')     # 如：Jan 12
mondayFormatter = DateFormatter('%m-%d-%Y') # 如：2-29-2015
dayFormatter = DateFormatter('%d')          # 如：12

quotes = quotes_historical_yahoo_ohlc(ticker, date1, date2)
if len(quotes) == 0:
    raise SystemExit

fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)

ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(mondayFormatter)
#ax.xaxis.set_minor_formatter(dayFormatter)

#plot_day_summary(ax, quotes, ticksize=3)
candlestick_ohlc(ax, quotes, width=0.6, colorup='r', colordown='g')

ax.xaxis_date()
ax.autoscale_view()
plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')

ax.grid(True)
plt.title('中国石化 600028')
plt.show()

效果图：

示例三

      获取上证50成分股数据，进行聚类分析（看看那些股票价格关联性强），并作图

import datetime

import numpy as np
import matplotlib.pyplot as plt

from matplotlib.finance import quotes_historical_yahoo_ochl
from matplotlib.collections import LineCollection

from sklearn import cluster, covariance, manifold

plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False

###############################################################################
# Retrieve the data from Internet

# Choose a time period reasonably calm (not too long ago so that we get
# high-tech firms, and before the 2008 crash)
d1 = datetime.datetime(2015, 1, 1)
d2 = datetime.datetime(2016, 1, 1)

# 上证50成分股
symbol_dict = {
    "600000": "浦发银行",
    "600010": "包钢股份",
    "600015": "华夏银行",
    "600016": "民生银行",
    "600018": "上港集团",
    "600028": "中国石化",
    "600030": "中信证券",
    "600036": "招商银行",
    "600048": "保利地产",
    "600050": "中国联通",
    "600089": "特变电工",
    "600104": "上汽集团",
    "600109": "国金证券",
    "600111": "北方稀土",
    "600150": "中国船舶",
    "600256": "广汇能源",
    "600406": "国电南瑞",
    "600518": "康美药业",
    "600519": "贵州茅台",
    "600583": "海油工程",
    "600585": "海螺水泥",
    "600637": "东方明珠",
    "600690": "青岛海尔",
    "600837": "海通证券",
    "600887": "伊利股份",
    "600893": "中航动力",
    "600958": "东方证券",
    "600999": "招商证券",
    "601006": "大秦铁路",
    "601088": "中国神华",
    "601166": "兴业银行",
    "601169": "北京银行",
    "601186": "中国铁建",
    "601288": "农业银行",
    "601318": "中国平安",
    "601328": "交通银行",
    "601390": "中国中铁",
    "601398": "工商银行",
    "601601": "中国太保",
    "601628": "中国人寿",
    "601668": "中国建筑",
    "601688": "华泰证券",
    "601766": "中国中车",
    "601800": "中国交建",
    "601818": "光大银行",
    "601857": "中国石油",
    "601901": "方正证券",
    "601988": "中国银行",
    "601989": "中国重工",
    "601998": "中信银行"}

symbols, names = np.array(list(symbol_dict.items())).T

quotes = [quotes_historical_yahoo_ochl(symbol+".ss", d1, d2, asobject=True)
          for symbol in symbols]

open = np.array([q.open for q in quotes]).astype(np.float)
close = np.array([q.close for q in quotes]).astype(np.float)

# 每日价格浮动包含了重要信息！
variation = close - open

###############################################################################
# Learn a graphical structure from the correlations
edge_model = covariance.GraphLassoCV()

# standardize the time series: using correlations rather than covariance
# is more efficient for structure recovery
X = variation.copy().T
X /= X.std(axis=0)
edge_model.fit(X)

###############################################################################
# Cluster using affinity propagation

_, labels = cluster.affinity_propagation(edge_model.covariance_)
n_labels = labels.max()

for i in range(n_labels + 1):
    print('Cluster %i: %s' % ((i + 1), ', '.join(names[labels == i])))

###############################################################################
# Find a low-dimension embedding for visualization: find the best position of
# the nodes (the stocks) on a 2D plane

# We use a dense eigen_solver to achieve reproducibility (arpack is
# initiated with random vectors that we don't control). In addition, we
# use a large number of neighbors to capture the large-scale structure.
node_position_model = manifold.LocallyLinearEmbedding(
    n_components=2, eigen_solver='dense', n_neighbors=6)

embedding = node_position_model.fit_transform(X.T).T

###############################################################################
# Visualization
plt.figure(1, facecolor='w', figsize=(10, 8))
plt.clf()
ax = plt.axes([0., 0., 1., 1.])
plt.axis('off')

# Display a graph of the partial correlations
partial_correlations = edge_model.precision_.copy()
d = 1 / np.sqrt(np.diag(partial_correlations))
partial_correlations *= d
partial_correlations *= d[:, np.newaxis]
non_zero = (np.abs(np.triu(partial_correlations, k=1)) > 0.02)

# Plot the nodes using the coordinates of our embedding
plt.scatter(embedding[0], embedding[1], s=100 * d ** 2, c=labels,
            cmap=plt.cm.spectral)

# Plot the edges
start_idx, end_idx = np.where(non_zero)
#a sequence of (*line0*, *line1*, *line2*), where::
#            linen = (x0, y0), (x1, y1), ... (xm, ym)
segments = [[embedding[:, start], embedding[:, stop]]
            for start, stop in zip(start_idx, end_idx)]
values = np.abs(partial_correlations[non_zero])
lc = LineCollection(segments,
                    zorder=0, cmap=plt.cm.hot_r,
                    norm=plt.Normalize(0, .7 * values.max()))
lc.set_array(values)
lc.set_linewidths(15 * values)
ax.add_collection(lc)

# Add a label to each node. The challenge here is that we want to
# position the labels to avoid overlap with other labels
for index, (name, label, (x, y)) in enumerate(
        zip(names, labels, embedding.T)):

    dx = x - embedding[0]
    dx[index] = 1
    dy = y - embedding[1]
    dy[index] = 1
    this_dx = dx[np.argmin(np.abs(dy))]
    this_dy = dy[np.argmin(np.abs(dx))]
    if this_dx > 0:
        horizontalalignment = 'left'
        x = x + .002
    else:
        horizontalalignment = 'right'
        x = x - .002
    if this_dy > 0:
        verticalalignment = 'bottom'
        y = y + .002
    else:
        verticalalignment = 'top'
        y = y - .002
    plt.text(x, y, name, size=10,
             horizontalalignment=horizontalalignment,
             verticalalignment=verticalalignment,
             bbox=dict(facecolor='w',
                       edgecolor=plt.cm.spectral(label / float(n_labels)),
                       alpha=.6))

plt.xlim(embedding[0].min() - .15 * embedding[0].ptp(),
         embedding[0].max() + .10 * embedding[0].ptp(),)
plt.ylim(embedding[1].min() - .03 * embedding[1].ptp(),
         embedding[1].max() + .03 * embedding[1].ptp())

plt.title('上证50成分股')
plt.show()

效果图：

说明：

      这个图是原例子的图，统计的是美股60只股票，我用原例运行是可以的。

      但是我换成上证50成分股后，雅虎拒绝我的的连接，所以下载不了数据，因此就看不到效果！

      抱歉了各位朋友。

有图为证：

另：

示例四

      下载雅虎股票数据到本地保存

import os
import urllib.request

'''
雅虎历史数据请求

    请求地址：http://ichart.yahoo.com/table.csv?s=string&a=int&b=int&c=int&d=int&e=int&f=int&g=d&ignore=.csv
        或者：http://table.finance.yahoo.com/table.csv?a=%d&b=%d&c=%d&d=%d&e=%d&f=%d&s=%s&y=0&g=%s&ignore=.csv
        两者参数有点不一样

    说明：
        s — 股票名称
        a — 起始时间，月
        b — 起始时间，日
        c — 起始时间，年
        d — 结束时间，月
        e — 结束时间，日
        f — 结束时间，年
        g — 时间周期。

    Ø  参数g的取值范围：d->‘日’(day), w->‘周’(week)，m->‘月’(mouth)，v->‘dividends only’
    
    Ø  月份是从0开始的，如9月数据，则写为08。  <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<

示例

    查询浦发银行2010.09.25 – 2010.10.8之间日线数据

    http://ichart.yahoo.com/table.csv?s=600000.SS&a=08&b=25&c=2010&d=09&e=8&f=2010&g=d

    查看国内沪深股市的股票，规则是：沪股代码末尾加.ss，深股代码末尾加.sz。如浦发银行的代号是：600000.SS
'''


ticker = '600028' # 600028 是"中国石化"的股票代码
ticker += '.ss'   # .ss 表示上证 .sz表示深证

date1 = ( 2015, 1, 1 ) #begining time  
date2 = ( 2016, 1, 1 ) #ending time  
  
d1 = (date1[1]-1, date1[2], date1[0])  
d2 = (date2[1]-1, date2[2], date2[0])  
  
g='d'  
  
urlFmt = 'http://table.finance.yahoo.com/table.csv?a=%d&b=%d&c=%d&d=%d&e=%d&f=%d&s=%s&y=0&g=%s&ignore=.csv'  
url =  urlFmt % (d1[0], d1[1], d1[2], d2[0], d2[1], d2[2], ticker, g)  #the url of historical data  

filename = 'data.csv'                #file name  
filename = os.path.join(os.path.dirname(__file__), filename)   #located file  

urllib.request.urlretrieve(url, filename)        #下载，保存