import matplotlib.pyplot as pltfrom 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'] = Falseticker = '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 SystemExitprint(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()

import matplotlib.pyplot as pltfrom matplotlib.dates import DateFormatter, WeekdayLocator, DayLocator, MONDAY,YEARLYfrom matplotlib.finance import quotes_historical_yahoo_ohlc, candlestick_ohlcplt.rcParams['font.sans-serif'] = ['SimHei']plt.rcParams['axes.unicode_minus'] = Falseticker = '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 12mondayFormatter = DateFormatter('%m-%d-%Y') # 如：2-29-2015dayFormatter = DateFormatter('%d')          # 如：12quotes = quotes_historical_yahoo_ohlc(ticker, date1, date2)if len(quotes) == 0:    raise SystemExitfig, 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()

import datetimeimport numpy as npimport matplotlib.pyplot as pltfrom matplotlib.finance import quotes_historical_yahoo_ochlfrom matplotlib.collections import LineCollectionfrom sklearn import cluster, covariance, manifoldplt.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())).Tquotes = [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 correlationsedge_model = covariance.GraphLassoCV()# standardize the time series: using correlations rather than covariance# is more efficient for structure recoveryX = variation.copy().TX /= 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################################################################################ Visualizationplt.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 correlationspartial_correlations = edge_model.precision_.copy()d = 1 / np.sqrt(np.diag(partial_correlations))partial_correlations *= dpartial_correlations *= d[:, np.newaxis]non_zero = (np.abs(np.triu(partial_correlations, k=1)) > 0.02)# Plot the nodes using the coordinates of our embeddingplt.scatter(embedding[0], embedding[1], s=100 * d ** 2, c=labels,            cmap=plt.cm.spectral)# Plot the edgesstart_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 labelsfor 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()

import osimport 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。  <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<