带有JSON文件的Matplotlib

Question

我试图合并两个代码源，但没有效果。我使用默认finance2.py matplotlib示例（如下所示）和json文件（如下所示）。代码的“finance.fetch_historical_yahoo”部分通过yahoo从.csv中提取数据，并将其放入numpy数组中。问题是，我不需要翻译yahoo的数据，我需要以matplotlib库可读的方式翻译JSON数据。

财务2.py：

import datetime
import numpy as np
import matplotlib.colors as colors
import matplotlib.finance as finance
import matplotlib.dates as mdates
import matplotlib.ticker as mticker
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import matplotlib.font_manager as font_manager
import json #to load json libraries for json data

startdate = datetime.date(2006,1,1)
today = enddate = datetime.date.today()
ticker = 'SPY'


fh = finance.fetch_historical_yahoo(ticker, startdate, enddate) #would like this call to be json
# a numpy record array with fields: date, open, high, low, close, volume, adj_close)

r = mlab.csv2rec(fh); fh.close()
r.sort()


def moving_average(x, n, type='simple'):
    """
    compute an n period moving average.

    type is 'simple' | 'exponential'

    """
    x = np.asarray(x)
    if type=='simple':
        weights = np.ones(n)
    else:
        weights = np.exp(np.linspace(-1., 0., n))

    weights /= weights.sum()


    a =  np.convolve(x, weights, mode='full')[:len(x)]
    a[:n] = a[n]
    return a

def relative_strength(prices, n=14):
    """
    compute the n period relative strength indicator
    http://stockcharts.com/school/doku.php?id=chart_school:glossary_r#relativestrengthindex
    http://www.investopedia.com/terms/r/rsi.asp
    """

    deltas = np.diff(prices)
    seed = deltas[:n+1]
    up = seed[seed>=0].sum()/n
    down = -seed[seed<0].sum()/n
    rs = up/down
    rsi = np.zeros_like(prices)
    rsi[:n] = 100. - 100./(1.+rs)

    for i in range(n, len(prices)):
        delta = deltas[i-1] # cause the diff is 1 shorter

        if delta>0:
            upval = delta
            downval = 0.
        else:
            upval = 0.
            downval = -delta

        up = (up*(n-1) + upval)/n
        down = (down*(n-1) + downval)/n

        rs = up/down
        rsi[i] = 100. - 100./(1.+rs)

    return rsi

def moving_average_convergence(x, nslow=26, nfast=12):
    """
    compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg'
    return value is emaslow, emafast, macd which are len(x) arrays
    """
    emaslow = moving_average(x, nslow, type='exponential')
    emafast = moving_average(x, nfast, type='exponential')
    return emaslow, emafast, emafast - emaslow


plt.rc('axes', grid=True)
plt.rc('grid', color='0.75', linestyle='-', linewidth=0.5)

textsize = 9
left, width = 0.1, 0.8
rect1 = [left, 0.7, width, 0.2]
rect2 = [left, 0.3, width, 0.4]
rect3 = [left, 0.1, width, 0.2]


fig = plt.figure(facecolor='white')
axescolor  = '#f6f6f6'  # the axes background color

ax1 = fig.add_axes(rect1, axisbg=axescolor)  #left, bottom, width, height
ax2 = fig.add_axes(rect2, axisbg=axescolor, sharex=ax1)
ax2t = ax2.twinx()
ax3  = fig.add_axes(rect3, axisbg=axescolor, sharex=ax1)



### plot the relative strength indicator
prices = r.adj_close
rsi = relative_strength(prices)
fillcolor = 'darkgoldenrod'

ax1.plot(r.date, rsi, color=fillcolor)
ax1.axhline(70, color=fillcolor)
ax1.axhline(30, color=fillcolor)
ax1.fill_between(r.date, rsi, 70, where=(rsi>=70), facecolor=fillcolor, edgecolor=fillcolor)
ax1.fill_between(r.date, rsi, 30, where=(rsi<=30), facecolor=fillcolor, edgecolor=fillcolor)
ax1.text(0.6, 0.9, '>70 = overbought', va='top', transform=ax1.transAxes, fontsize=textsize)
ax1.text(0.6, 0.1, '<30 = oversold', transform=ax1.transAxes, fontsize=textsize)
ax1.set_ylim(0, 100)
ax1.set_yticks([30,70])
ax1.text(0.025, 0.95, 'RSI (14)', va='top', transform=ax1.transAxes, fontsize=textsize)
ax1.set_title('%s daily'%ticker)

### plot the price and volume data
dx = r.adj_close - r.close
low = r.low + dx
high = r.high + dx

deltas = np.zeros_like(prices)
deltas[1:] = np.diff(prices)
up = deltas>0
ax2.vlines(r.date[up], low[up], high[up], color='black', label='_nolegend_')
ax2.vlines(r.date[~up], low[~up], high[~up], color='black', label='_nolegend_')
ma20 = moving_average(prices, 20, type='simple')
ma200 = moving_average(prices, 200, type='simple')

linema20, = ax2.plot(r.date, ma20, color='blue', lw=2, label='MA (20)')
linema200, = ax2.plot(r.date, ma200, color='red', lw=2, label='MA (200)')


last = r[-1]
s = '%s O:%1.2f H:%1.2f L:%1.2f C:%1.2f, V:%1.1fM Chg:%+1.2f' % (
    today.strftime('%d-%b-%Y'),
    last.open, last.high,
    last.low, last.close,
    last.volume*1e-6,
    last.close-last.open )
t4 = ax2.text(0.3, 0.9, s, transform=ax2.transAxes, fontsize=textsize)

props = font_manager.FontProperties(size=10)
leg = ax2.legend(loc='center left', shadow=True, fancybox=True, prop=props)
leg.get_frame().set_alpha(0.5)


volume = (r.close*r.volume)/1e6  # dollar volume in millions
vmax = volume.max()
poly = ax2t.fill_between(r.date, volume, 0, label='Volume', facecolor=fillcolor, edgecolor=fillcolor)
ax2t.set_ylim(0, 5*vmax)
ax2t.set_yticks([])


### compute the MACD indicator
fillcolor = 'darkslategrey'
nslow = 26
nfast = 12
nema = 9
emaslow, emafast, macd = moving_average_convergence(prices, nslow=nslow, nfast=nfast)
ema9 = moving_average(macd, nema, type='exponential')
ax3.plot(r.date, macd, color='black', lw=2)
ax3.plot(r.date, ema9, color='blue', lw=1)
ax3.fill_between(r.date, macd-ema9, 0, alpha=0.5, facecolor=fillcolor, edgecolor=fillcolor)


ax3.text(0.025, 0.95, 'MACD (%d, %d, %d)'%(nfast, nslow, nema), va='top',
         transform=ax3.transAxes, fontsize=textsize)

#ax3.set_yticks([])
# turn off upper axis tick labels, rotate the lower ones, etc
for ax in ax1, ax2, ax2t, ax3:
    if ax!=ax3:
        for label in ax.get_xticklabels():
            label.set_visible(False)
    else:
        for label in ax.get_xticklabels():
            label.set_rotation(30)
            label.set_horizontalalignment('right')

    ax.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')



class MyLocator(mticker.MaxNLocator):
    def __init__(self, *args, **kwargs):
        mticker.MaxNLocator.__init__(self, *args, **kwargs)

    def __call__(self, *args, **kwargs):
        return mticker.MaxNLocator.__call__(self, *args, **kwargs)

# at most 5 ticks, pruning the upper and lower so they don't overlap
# with other ticks
#ax2.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))
#ax3.yaxis.set_major_locator(mticker.MaxNLocator(5, prune='both'))

ax2.yaxis.set_major_locator(MyLocator(5, prune='both'))
ax3.yaxis.set_major_locator(MyLocator(5, prune='both'))

plt.show()

.json文件：

{
        "instrument" : "EUR_USD",
        "granularity" : "D",
        "candles" : [
                {
                        "time" : "2014-02-17T22:00:00Z",
                        "openMid" : 1.259445,
                        "highMid" : 1.259955,
                        "lowMid" : 1.251825,
                        "closeMid" : 1.257955,
                        "volume" : 61184,
                        "complete" : true
                },
                {
                        "time" : "2014-02-18T22:00:00Z",
                        "openMid" : 1.257975,
                        "highMid" : 1.259955,
                        "lowMid" : 1.251825,
                        "closeMid" : 1.252945,
                        "volume" : 67528,
                        "complete" : false
                }
        ]
}

我甚至没有确切地寻找一个明确的答案，即使是在正确的方向上的一个点在这个时间点将是非常有帮助的。

提前谢谢

Answer 1

这是一个部分答案-它展示了如何将json解压成行（按行）或列（按列）的列表。

import json
import numpy as np
# json embedded here, but could be read in from text file
json_string = """{
        "instrument" : "EUR_USD",
        "granularity" : "D",
        "candles" : [
                {
                        "time" : "2014-02-17T22:00:00Z",
                        "openMid" : 1.259445,
                        "highMid" : 1.259955,
                        "lowMid" : 1.251825,
                        "closeMid" : 1.257955,
                        "volume" : 61184,
                        "complete" : true
                },
                {
                        "time" : "2014-02-18T22:00:00Z",
                        "openMid" : 1.257975,
                        "highMid" : 1.259955,
                        "lowMid" : 1.251825,
                        "closeMid" : 1.252945,
                        "volume" : 67528,
                        "complete" : false
                }
        ]
       }"""

candles = json.loads(json_string)['candles']
col_heads = ['time', 'openMid', 'highMid', 'lowMid', 'closeMid', 'volume']
f = lambda c: [c[col] for col in col_heads]
row_wise = [col_heads[:]]
row_wise.extend([f(candle) for candle in candles])
for row in row_wise: print row
print
col_wise = zip(*row_wise)
for col in col_wise: print col

剩下的是构造一个numpy重新排列：这就是mlab.csv2rec（）产生的结果。这是留给努皮大师的练习（我不是！；）

Answer 2

你可以用熊猫来更简洁地解决这个问题。这是解决你问题的相关信息。抱歉，我没有时间根据您的特定用例自定义所有代码。如果你想再看一次，请告诉我，我很乐意。

来自the docs

>>> data = [{'state': 'Florida',
...          'shortname': 'FL',
...          'info': {
...               'governor': 'Rick Scott'
...          },
...          'counties': [{'name': 'Dade', 'population': 12345},
...                      {'name': 'Broward', 'population': 40000},
...                      {'name': 'Palm Beach', 'population': 60000}]},
...         {'state': 'Ohio',
...          'shortname': 'OH',
...          'info': {
...               'governor': 'John Kasich'
...          },
...          'counties': [{'name': 'Summit', 'population': 1234},
...                       {'name': 'Cuyahoga', 'population': 1337}]}]
>>> from pandas.io.json import json_normalize
>>> result = json_normalize(data, 'counties', ['state', 'shortname',
...                                           ['info', 'governor']])
>>> result
         name  population info.governor    state shortname
0        Dade       12345    Rick Scott  Florida        FL
1     Broward       40000    Rick Scott  Florida        FL
2  Palm Beach       60000    Rick Scott  Florida        FL
3      Summit        1234   John Kasich     Ohio        OH
4    Cuyahoga        1337   John Kasich     Ohio        OH

现在可以使用pandas dataframe to visualize the data。

In [1]: import matplotlib.pyplot as plt

In [2]: ts = pandas.Series(np.random.randn(1000), index=pd.date_range('1/1/2000', periods=1000))

In [3]: ts = ts.cumsum()

In [4]: ts.plot()
Out[4]: <matplotlib.axes._subplots.AxesSubplot at 0x1120939d0>