""" A collection of modules for collecting, analyzing and plotting financial data. User contributions welcome! """ #from __future__ import division import os, time, warnings from urllib2 import urlopen try: from hashlib import md5 except ImportError: from md5 import md5 #Deprecated in 2.5 try: import datetime except ImportError: raise ImportError('The finance module requires datetime support (python2.3)') import numpy as np from matplotlib import verbose, get_configdir from dates import date2num from matplotlib.cbook import Bunch from matplotlib.collections import LineCollection, PolyCollection from matplotlib.colors import colorConverter from lines import Line2D, TICKLEFT, TICKRIGHT from patches import Rectangle from matplotlib.transforms import Affine2D configdir = get_configdir() cachedir = os.path.join(configdir, 'finance.cache') def parse_yahoo_historical(fh, asobject=False, adjusted=True): """ Parse the historical data in file handle fh from yahoo finance and return results as a list of d, open, close, high, low, volume where d is a floating poing representation of date, as returned by date2num if adjust=True, use adjusted prices """ results = [] lines = fh.readlines() datefmt = None for line in lines[1:]: vals = line.split(',') if len(vals)!=7: continue datestr = vals[0] if datefmt is None: try: datefmt = '%Y-%m-%d' dt = datetime.date(*time.strptime(datestr, datefmt)[:3]) except ValueError: datefmt = '%d-%b-%y' # Old Yahoo--cached file? dt = datetime.date(*time.strptime(datestr, datefmt)[:3]) d = date2num(dt) open, high, low, close = [float(val) for val in vals[1:5]] volume = int(vals[5]) if adjusted: aclose = float(vals[6]) m = aclose/close open *= m high *= m low *= m close = aclose results.append((d, open, close, high, low, volume)) results.reverse() if asobject: if len(results)==0: return None else: date, open, close, high, low, volume = map(np.asarray, zip(*results)) return Bunch(date=date, open=open, close=close, high=high, low=low, volume=volume) else: return results def fetch_historical_yahoo(ticker, date1, date2, cachename=None): """ Fetch historical data for ticker between date1 and date2. date1 and date2 are datetime instances Ex: fh = fetch_historical_yahoo('^GSPC', d1, d2) cachename is the name of the local file cache. If None, will default to the md5 hash or the url (which incorporates the ticker and date range) a file handle is returned """ ticker = ticker.upper() d1 = (date1.month-1, date1.day, date1.year) d2 = (date2.month-1, date2.day, date2.year) 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=d&ignore=.csv' url = urlFmt % (d1[0], d1[1], d1[2], d2[0], d2[1], d2[2], ticker) if cachename is None: cachename = os.path.join(cachedir, md5(url).hexdigest()) if os.path.exists(cachename): fh = file(cachename) verbose.report('Using cachefile %s for %s'%(cachename, ticker)) else: if not os.path.isdir(cachedir): os.mkdir(cachedir) urlfh = urlopen(url) fh = file(cachename, 'w') fh.write(urlfh.read()) fh.close() verbose.report('Saved %s data to cache file %s'%(ticker, cachename)) fh = file(cachename, 'r') return fh def quotes_historical_yahoo(ticker, date1, date2, asobject=False, adjusted=True, cachename=None): """ Get historical data for ticker between date1 and date2. date1 and date2 are datetime instances results are a list of tuples (d, open, close, high, low, volume) where d is a floating poing representation of date, as returned by date2num if asobject is True, the return val is an object with attrs date, open, close, high, low, volume, which are equal length arrays if adjust=True, use adjusted prices Ex: sp = f.quotes_historical_yahoo('^GSPC', d1, d2, asobject=True, adjusted=True) returns = (sp.open[1:] - sp.open[:-1])/sp.open[1:] [n,bins,patches] = hist(returns, 100) mu = mean(returns) sigma = std(returns) x = normpdf(bins, mu, sigma) plot(bins, x, color='red', lw=2) cachename is the name of the local file cache. If None, will default to the md5 hash or the url (which incorporates the ticker and date range) """ fh = fetch_historical_yahoo(ticker, date1, date2, cachename) try: ret = parse_yahoo_historical(fh, asobject, adjusted) except IOError, exc: warnings.warn('urlopen() failure\n' + url + '\n' + exc.strerror[1]) return None return ret def plot_day_summary(ax, quotes, ticksize=3, colorup='k', colordown='r', ): """ quotes is a list of (time, open, close, high, low, ...) tuples Represent the time, open, close, high, low as a vertical line ranging from low to high. The left tick is the open and the right tick is the close. time must be in float date format - see date2num ax : an Axes instance to plot to ticksize : open/close tick marker in points colorup : the color of the lines where close >= open colordown : the color of the lines where close < open return value is a list of lines added """ lines = [] for q in quotes: t, open, close, high, low = q[:5] if close>=open : color = colorup else : color = colordown vline = Line2D( xdata=(t, t), ydata=(low, high), color=color, antialiased=False, # no need to antialias vert lines ) oline = Line2D( xdata=(t, t), ydata=(open, open), color=color, antialiased=False, marker=TICKLEFT, markersize=ticksize, ) cline = Line2D( xdata=(t, t), ydata=(close, close), color=color, antialiased=False, markersize=ticksize, marker=TICKRIGHT) lines.extend((vline, oline, cline)) ax.add_line(vline) ax.add_line(oline) ax.add_line(cline) ax.autoscale_view() return lines def candlestick(ax, quotes, width=0.2, colorup='k', colordown='r', alpha=1.0): """ quotes is a list of (time, open, close, high, low, ...) tuples. As long as the first 5 elements of the tuples are these values, the tuple can be as long as you want (eg it may store volume). time must be in float days format - see date2num Plot the time, open, close, high, low as a vertical line ranging from low to high. Use a rectangular bar to represent the open-close span. If close >= open, use colorup to color the bar, otherwise use colordown ax : an Axes instance to plot to width : fraction of a day for the rectangle width colorup : the color of the rectangle where close >= open colordown : the color of the rectangle where close < open alpha : the rectangle alpha level return value is lines, patches where lines is a list of lines added and patches is a list of the rectangle patches added """ OFFSET = width/2.0 lines = [] patches = [] for q in quotes: t, open, close, high, low = q[:5] if close>=open : color = colorup lower = open height = close-open else : color = colordown lower = close height = open-close vline = Line2D( xdata=(t, t), ydata=(low, high), color='k', linewidth=0.5, antialiased=True, ) rect = Rectangle( xy = (t-OFFSET, lower), width = width, height = height, facecolor = color, edgecolor = color, ) rect.set_alpha(alpha) lines.append(vline) patches.append(rect) ax.add_line(vline) ax.add_patch(rect) ax.autoscale_view() return lines, patches def plot_day_summary2(ax, opens, closes, highs, lows, ticksize=4, colorup='k', colordown='r', ): """ Represent the time, open, close, high, low as a vertical line ranging from low to high. The left tick is the open and the right tick is the close. ax : an Axes instance to plot to ticksize : size of open and close ticks in points colorup : the color of the lines where close >= open colordown : the color of the lines where close < open return value is a list of lines added """ # note this code assumes if any value open, close, low, high is # missing they all are missing rangeSegments = [ ((i, low), (i, high)) for i, low, high in zip(xrange(len(lows)), lows, highs) if low != -1 ] # the ticks will be from ticksize to 0 in points at the origin and # we'll translate these to the i, close location openSegments = [ ((-ticksize, 0), (0, 0)) ] # the ticks will be from 0 to ticksize in points at the origin and # we'll translate these to the i, close location closeSegments = [ ((0, 0), (ticksize, 0)) ] offsetsOpen = [ (i, open) for i, open in zip(xrange(len(opens)), opens) if open != -1 ] offsetsClose = [ (i, close) for i, close in zip(xrange(len(closes)), closes) if close != -1 ] scale = ax.figure.dpi * (1.0/72.0) tickTransform = Affine2D().scale(scale, 0.0) r,g,b = colorConverter.to_rgb(colorup) colorup = r,g,b,1 r,g,b = colorConverter.to_rgb(colordown) colordown = r,g,b,1 colord = { True : colorup, False : colordown, } colors = [colord[open= open colordown : the color of the lines where close < open alpha : bar transparency return value is lineCollection, barCollection """ # note this code assumes if any value open, close, low, high is # missing they all are missing delta = width/2. barVerts = [ ( (i-delta, open), (i-delta, close), (i+delta, close), (i+delta, open) ) for i, open, close in zip(xrange(len(opens)), opens, closes) if open != -1 and close!=-1 ] rangeSegments = [ ((i, low), (i, high)) for i, low, high in zip(xrange(len(lows)), lows, highs) if low != -1 ] r,g,b = colorConverter.to_rgb(colorup) colorup = r,g,b,alpha r,g,b = colorConverter.to_rgb(colordown) colordown = r,g,b,alpha colord = { True : colorup, False : colordown, } colors = [colord[open= open colordown : the color of the lines where close < open alpha : bar transparency """ r,g,b = colorConverter.to_rgb(colorup) colorup = r,g,b,alpha r,g,b = colorConverter.to_rgb(colordown) colordown = r,g,b,alpha colord = { True : colorup, False : colordown, } colors = [colord[open= open colordown : the color of the lines where close < open alpha : bar transparency nb: first point is not displayed - it is used only for choosing the right color """ return volume_overlay(ax,closes[:-1],closes[1:],volumes[1:],colorup,colordown,width,alpha) def volume_overlay3(ax, quotes, colorup='k', colordown='r', width=4, alpha=1.0): """ Add a volume overlay to the current axes. quotes is a list of (d, open, close, high, low, volume) and close-open is used to determine the color of the bar kwarg width : the bar width in points colorup : the color of the lines where close1 >= close0 colordown : the color of the lines where close1 < close0 alpha : bar transparency """ r,g,b = colorConverter.to_rgb(colorup) colorup = r,g,b,alpha r,g,b = colorConverter.to_rgb(colordown) colordown = r,g,b,alpha colord = { True : colorup, False : colordown, } dates, opens, closes, highs, lows, volumes = zip(*quotes) colors = [colord[close1>=close0] for close0, close1 in zip(closes[:-1], closes[1:]) if close0!=-1 and close1 !=-1] colors.insert(0,colord[closes[0]>=opens[0]]) right = width/2.0 left = -width/2.0 bars = [ ( (left, 0), (left, volume), (right, volume), (right, 0)) for d, open, close, high, low, volume in quotes] sx = ax.figure.dpi * (1.0/72.0) # scale for points sy = ax.bbox.height / ax.viewLim.height barTransform = Affine2D().scale(sx,sy) dates = [d for d, open, close, high, low, volume in quotes] offsetsBars = [(d, 0) for d in dates] useAA = 0, # use tuple here lw = 0.5, # and here barCollection = PolyCollection(bars, facecolors = colors, edgecolors = ( (0,0,0,1), ), antialiaseds = useAA, linewidths = lw, offsets = offsetsBars, transOffset = ax.transData, ) barCollection.set_transform(barTransform) minpy, maxx = (min(dates), max(dates)) miny = 0 maxy = max([volume for d, open, close, high, low, volume in quotes]) corners = (minpy, miny), (maxx, maxy) ax.update_datalim(corners) #print 'datalim', ax.dataLim.get_bounds() #print 'viewlim', ax.viewLim.get_bounds() ax.add_collection(barCollection) ax.autoscale_view() return barCollection def index_bar(ax, vals, facecolor='b', edgecolor='l', width=4, alpha=1.0, ): """ Add a bar collection graph with height vals (-1 is missing). ax : an Axes instance to plot to width : the bar width in points alpha : bar transparency """ facecolors = (colorConverter.to_rgba(facecolor, alpha),) edgecolors = (colorConverter.to_rgba(edgecolor, alpha),) right = width/2.0 left = -width/2.0 bars = [ ( (left, 0), (left, v), (right, v), (right, 0)) for v in vals if v != -1 ] sx = ax.figure.dpi * (1.0/72.0) # scale for points sy = ax.bbox.height / ax.viewLim.height barTransform = Affine2D().scale(sx,sy) offsetsBars = [ (i, 0) for i,v in enumerate(vals) if v != -1 ] barCollection = PolyCollection(bars, facecolors = facecolors, edgecolors = edgecolors, antialiaseds = (0,), linewidths = (0.5,), offsets = offsetsBars, transOffset = ax.transData, ) barCollection.set_transform(barTransform) minpy, maxx = (0, len(offsetsBars)) miny = 0 maxy = max([v for v in vals if v!=-1]) corners = (minpy, miny), (maxx, maxy) ax.update_datalim(corners) ax.autoscale_view() # add these last ax.add_collection(barCollection) return barCollection