Come ottenere un grafico per l'analisi del mercato azionario?

Question

Ho aggiornato il codice e ora fornisce il grafico, tuttavia dopo avermi dato il grafico produce i seguenti messaggi di errore.

Warning (da avvertimenti modulo): file "C: \ Python27 \ lib \ site-packages \ matplotlib \ collections.py", la linea 590 se self._edgecolors == str ('faccia'): FutureWarning: confronto elementwise fallito; tornando scalari, invece, ma in futuro si esibiranno elementwise confronto

import urllib2 
import time 
import datetime 
import numpy as np 
import matplotlib.pyplot as plt 
import matplotlib.ticker as mticker 
import matplotlib.dates as mdates 
from matplotlib.finance import candlestick_ochl 
import matplotlib 
import pylab 
matplotlib.rcParams.update({'font.size': 9}) 



def rsiFunc(prices, n=14): 
    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 movingaverage(values,window): 
    weigths = np.repeat(1.0, window)/window 
    smas = np.convolve(values, weigths, 'valid') 
    return smas # as a numpy array 

def ExpMovingAverage(values, window): 
    weights = np.exp(np.linspace(-1., 0., window)) 
    weights /= weights.sum() 
    a = np.convolve(values, weights, mode='full')[:len(values)] 
    a[:window] = a[window] 
    return a 


def computeMACD(x, slow=26, fast=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 = ExpMovingAverage(x, slow) 
    emafast = ExpMovingAverage(x, fast) 
    return emaslow, emafast, emafast - emaslow 


def graphData(stock,MA1,MA2): 
    ''' 
     Use this to dynamically pull a stock: 
    ''' 
    try: 
     print 'Currently Pulling',stock 
     print str(datetime.datetime.fromtimestamp(int(time.time())).strftime('%Y-%m-%d %H:%M:%S')) 
     #Keep in mind this is close high low open data from Yahoo 
     urlToVisit = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=10y/csv' 
     stockFile =[] 
     try: 
      sourceCode = urllib2.urlopen(urlToVisit).read() 
      splitSource = sourceCode.split('\n') 
      for eachLine in splitSource: 
       splitLine = eachLine.split(',') 
       if len(splitLine)==6: 
        if 'values' not in eachLine: 
         stockFile.append(eachLine) 
     except Exception, e: 
      print str(e), 'failed to organize pulled data.' 
    except Exception,e: 
     print str(e), 'failed to pull pricing data' 
    try: 
     date, closep, highp, lowp, openp, volume = np.loadtxt(stockFile,delimiter=',', unpack=True, 
                   converters={ 0: mdates.strpdate2num('%Y%m%d')}) 
     x = 0 
     y = len(date) 
     newAr = [] 
     while x < y: 
      appendLine = date[x],openp[x],closep[x],highp[x],lowp[x],volume[x] 
      newAr.append(appendLine) 
      x+=1 

     Av1 = movingaverage(closep, MA1) 
     Av2 = movingaverage(closep, MA2) 

     SP = len(date[MA2-1:]) 

     fig = plt.figure(facecolor='#07000d') 

     ax1 = plt.subplot2grid((6,4), (1,0), rowspan=4, colspan=4, axisbg='#07000d') 
     candlestick_ochl(ax1, newAr[-SP:], width=.6, colorup='#53c156', colordown='#ff1717')#width=.6, plot_day_summary_ohlc 

     Label1 = str(MA1)+' SMA' 
     Label2 = str(MA2)+' SMA' 

     ax1.plot(date[-SP:],Av1[-SP:],'#e1edf9',label=Label1, linewidth=1.5) 
     ax1.plot(date[-SP:],Av2[-SP:],'#4ee6fd',label=Label2, linewidth=1.5) 

     ax1.grid(True, color='w') 
     ax1.xaxis.set_major_locator(mticker.MaxNLocator(10)) 
     ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d')) 
     ax1.yaxis.label.set_color("w") 
     ax1.spines['bottom'].set_color("#5998ff") 
     ax1.spines['top'].set_color("#5998ff") 
     ax1.spines['left'].set_color("#5998ff") 
     ax1.spines['right'].set_color("#5998ff") 
     ax1.tick_params(axis='y', colors='w') 
     plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper')) #gca() 
     ax1.tick_params(axis='x', colors='w') 
     plt.ylabel('Stock price and Volume') 

     maLeg = plt.legend(loc=9, ncol=2, prop={'size':7}, 
        fancybox=True, borderaxespad=0.) 
     maLeg.get_frame().set_alpha(0.4) 
     textEd = plt.gca().get_legend().get_texts()#pylab.gca() changed to plt.gca() 
     plt.setp(textEd[0:5], color = 'w')#changed pylab.setp to plt.setp 

     volumeMin = 0 

     ax0 = plt.subplot2grid((6,4), (0,0), sharex=ax1, rowspan=1, colspan=4, axisbg='#07000d') 
     rsi = rsiFunc(closep) 
     rsiCol = '#c1f9f7' 
     posCol = '#386d13' 
     negCol = '#8f2020' 

     ax0.plot(date[-SP:], rsi[-SP:], rsiCol, linewidth=1.5) 
     ax0.axhline(70, color=negCol) 
     ax0.axhline(30, color=posCol) 
     ax0.fill_between(date[-SP:], rsi[-SP:], 70, where=(rsi[-SP:]>=70), facecolor=negCol, edgecolor=negCol, alpha=0.5) 
     ax0.fill_between(date[-SP:], rsi[-SP:], 30, where=(rsi[-SP:]<=30), facecolor=posCol, edgecolor=posCol, alpha=0.5) 
     ax0.set_yticks([30,70]) 
     ax0.yaxis.label.set_color("w") 
     ax0.spines['bottom'].set_color("#5998ff") 
     ax0.spines['top'].set_color("#5998ff") 
     ax0.spines['left'].set_color("#5998ff") 
     ax0.spines['right'].set_color("#5998ff") 
     ax0.tick_params(axis='y', colors='w') 
     ax0.tick_params(axis='x', colors='w') 
     plt.ylabel('RSI') 

     ax1v = ax1.twinx() 
     ax1v.fill_between(date[-SP:],volumeMin, volume[-SP:], facecolor='#00ffe8', alpha=.4) 
     ax1v.axes.yaxis.set_ticklabels([]) 
     ax1v.grid(False) 
     ax1v.set_ylim(0, 3*volume.max()) 
     ax1v.spines['bottom'].set_color("#5998ff") 
     ax1v.spines['top'].set_color("#5998ff") 
     ax1v.spines['left'].set_color("#5998ff") 
     ax1v.spines['right'].set_color("#5998ff") 
     ax1v.tick_params(axis='x', colors='w') 
     ax1v.tick_params(axis='y', colors='w') 


     ax2 = plt.subplot2grid((6,4), (5,0), sharex=ax1, rowspan=1, colspan=4, axisbg='#07000d') 



     # START NEW INDICATOR CODE # 



     # END NEW INDICATOR CODE # 




     plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper')) 
     ax2.spines['bottom'].set_color("#5998ff") 
     ax2.spines['top'].set_color("#5998ff") 
     ax2.spines['left'].set_color("#5998ff") 
     ax2.spines['right'].set_color("#5998ff") 
     ax2.tick_params(axis='x', colors='w') 
     ax2.tick_params(axis='y', colors='w') 
     ax2.yaxis.set_major_locator(mticker.MaxNLocator(nbins=5, prune='upper')) 





     for label in ax2.xaxis.get_ticklabels(): 
      label.set_rotation(45) 

     plt.suptitle(stock.upper(),color='w') 

     plt.setp(ax0.get_xticklabels(), visible=False) 
     plt.setp(ax1.get_xticklabels(), visible=False) 

     '''ax1.annotate('Big news!',(date[510],Av1[510]), 
      xytext=(0.8, 0.9), textcoords='axes fraction', 
      arrowprops=dict(facecolor='white', shrink=0.05), 
      fontsize=14, color = 'w', 
      horizontalalignment='right', verticalalignment='bottom')''' 

     plt.subplots_adjust(left=.09, bottom=.14, right=.94, top=.95, wspace=.20, hspace=0) 
     plt.show() 
     fig.savefig('example.png',facecolor=fig.get_facecolor()) 

    except Exception,e: 
     print 'main loop',str(e) 

while True: 
    stock = raw_input('Stock to plot: ') 
    graphData(stock,10,50) 

Answer 1

Si prega di guardare il filo Violin plot: warning with matplotlib 1.4.3 e pyplot fill_between warning since upgrade of numpy to 1.10.10

Sembra che ci sia un bug in matplotlib 1.4.3 (che ha solo iniziato causando che l'errore dal l'aggiornamento a numpy 1.10). Questo problema è stato corretto in 1.5.0 (che dovrebbe essere rilasciato a breve). Spero che questo ti aiuti.