Historical Intraday Stock Price Data with Python

By popular request, this post will present how to acquire intraday stock data from google finance using python. The general structure of the code is pretty simple to understand. First a request url is created and sent. The response is then read by python to create an array or matrix of the financial data and a vector of time data. This array is created with the help of the popular Numpy package that can be downloaded from here. Then in one if-statement, the time data is then restructured into a proper unix time format and translated to a more familiar date string for each financial data point. The translated time vector is then joined with the financial array to produce a single easy to work with financial time series array. Since Numpy has been ported to Python 3, the code I wrote should be compatibile with both Python 2.X and 3.X. Here it is: import urllib2 import urllib import numpy as np from datetime import datetime urldata = {} urldata['q'] = ticker = 'JPM' # stock symbol urldata['x'] = 'NYSE' # exchange symbol urldata['i'] = '60' # interval urldata['p'] = '1d' # number of past trading days (max has been 15d) urldata['f'] = 'd,o,h,l,c,v' # requested data d is time, o is open, c is closing, h is high, l is low, v is volume url_values = urllib.urlencode(urldata) url = 'http://www.google.com/finance/getprices' full_url = url + '?' + url_values req = urllib2.Request(full_url) response = urllib2.urlopen(req).readlines() getdata = response del getdata[0:7] numberoflines = len(getdata) returnMat = np.zeros((numberoflines, 5)) timeVector = [] index = 0 for line in getdata: line = line.strip('a') listFromLine = line.split(',') returnMat[index,:] = listFromLine[1:6] timeVector.append(int(listFromLine[0])) index += 1 # convert Unix or epoch time to something more familiar for x in timeVector: if x > 500: z = x timeVector[timeVector.index(x)] = datetime.fromtimestamp(x) else: y = z+x*60 # multiply by interval timeVector[timeVector.index(x)] = datetime.fromtimestamp(y) tdata = np.array(timeVector) time = tdata.reshape((len(tdata),1)) intradata = np.concatenate((time, returnMat), axis=1) # array of all data with the properly formated times

Here is a quick example of what can be done with this data in Python 2.7 using the Numpy and matplotlib libraries. It is nothing fancy, just the usual moving average computations and some styling preferences.

The python code that was used to create the plot above can is posted below: ### Example on how to use and plot this data import matplotlib.pyplot as plt import matplotlib.font_manager as font_manager import matplotlib.dates as mdates import matplotlib.ticker as mticker import numpy as np def relative_strength(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 moving_average(p, n, type='simple'): """ compute an n period moving average. type is 'simple' | 'exponential' """ p = np.asarray(p) if type=='simple': weights = np.ones(n) else: weights = np.exp(np.linspace(-1., 0., n)) weights /= weights.sum() a = np.convolve(p, weights, mode='full')[:len(p)] a[:n] = a[n] return a def moving_average_convergence(p, 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(p) arrays """ emaslow = moving_average(p, nslow, type='exponential') emafast = moving_average(p, 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 axies 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 = intradata[:,4] rsi = relative_strength(prices) t = intradata[:,0] fillcolor = 'darkgoldenrod' ax1.plot(t, rsi, color=fillcolor) ax1.axhline(70, color=fillcolor) ax1.axhline(30, color=fillcolor) ax1.fill_between(t, rsi, 70, where=(rsi>=70), facecolor=fillcolor, edgecolor=fillcolor) ax1.fill_between(t, 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 low = intradata[:,3] high = intradata[:,2] deltas = np.zeros_like(prices) deltas[1:] = np.diff(prices) up = deltas>0 ax2.vlines(t[up], low[up], high[up], color='black', label='_nolegend_') ax2.vlines(t[~up], low[~up], high[~up], color='black', label='_nolegend_') ma5 = moving_average(prices, 5, type='simple') ma50 = moving_average(prices, 50, type='simple') linema5, = ax2.plot(t, ma5, color='blue', lw=2, label='MA (5)') linema50, = ax2.plot(t, ma50, color='red', lw=2, label='MA (50)') 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 = (intradata[:,4]*intradata[:,5])/1e6 # dollar volume in millions vmax = volume.max() poly = ax2t.fill_between(t, 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(t, macd, color='black', lw=2) ax3.plot(t, ema9, color='blue', lw=1) ax3.fill_between(t, 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) # 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(MyLocator(5, prune='both')) ax3.yaxis.set_major_locator(MyLocator(5, prune='both')) plt.show()