iframe-proxy | Sunbelt Computer Software

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A collection of modules for collecting, analyzing and plotting
financial data.   User contributions welcome!
#from __future__ import division
import os, time, warnings, md5
from urllib import urlopen
try: import datetime
except ImportError:
    raise SystemExit('The finance module requires datetime support (python2.3)')
import numpy as npy
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(npy.asarray, zip(*results))
        return Bunch(date=date, open=open, close=close, high=high, low=low, volume=volume)
        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
    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.md5(url).hexdigest())
    if os.path.exists(cachename):
        fh = file(cachename)
        verbose.report('Using cachefile %s for %s'%(cachename, ticker))
        if not os.path.isdir(cachedir): os.mkdir(cachedir)
        fh = file(cachename, 'w')
        fh.write(urlopen(url).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
    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().scaled(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<close] for open, close in zip(opens, closes) if open!=-1 and close !=-1]
    assert(len(rangeSegments)==len(offsetsOpen))
    assert(len(offsetsOpen)==len(offsetsClose))
    assert(len(offsetsClose)==len(colors))
    useAA = 0,   # use tuple here
    lw = 1,      # and here
    rangeCollection = LineCollection(rangeSegments,
                                     colors       = colors,
                                     linewidths   = lw,
                                     antialiaseds = useAA,
    openCollection = LineCollection(openSegments,
                                    colors       = colors,
                                    antialiaseds = useAA,
                                    linewidths   = lw,
                                    offsets      = offsetsOpen,
                                    transOffset  = ax.transData,
    openCollection.set_transform(tickTransform)
    closeCollection = LineCollection(closeSegments,
                                     colors       = colors,
                                     antialiaseds = useAA,
                                     linewidths   = lw,
                                     offsets      = offsetsClose,
                                     transOffset  = ax.transData,
    closeCollection.set_transform(tickTransform)
    minpy, maxx = (0, len(rangeSegments))
    miny = min([low for low in lows if low !=-1])
    maxy = max([high for high in highs if high != -1])
    corners = (minpy, miny), (maxx, maxy)
    ax.update_datalim(corners)
    ax.autoscale_view()
    # add these last
    ax.add_collection(rangeCollection)
    ax.add_collection(openCollection)
    ax.add_collection(closeCollection)
    return rangeCollection, openCollection, closeCollection
def candlestick2(ax, opens, closes, highs, lows, width=4,
                 colorup='k', colordown='r',
                 alpha=0.75,
    Represent the open, close as a bar line and high low range as a
    vertical line.
    ax          : an Axes instance to plot to
    width       : the bar width in points
    colorup     : the color of the lines where close >= 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
    right = width/2.0
    left = -width/2.0
    barVerts = [ ( (left, 0), (left, close-open), (right, close-open), (right, 0) ) for open, close in zip(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 ]
    offsetsBars = [ (i, open) for i,open in zip(xrange(len(opens)), opens) if open != -1 ]
    sx = ax.figure.dpi * (1.0/72.0)  # scale for points
    sy = (ax.bbox.ur().y() - ax.bbox.ll().y()) / (ax.viewLim.ur().y() - ax.viewLim.ll().y())
    barTransform = Affine2D().scaled(sx,sy)
    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<close] for open, close in zip(opens, closes) if open!=-1 and close !=-1]
    assert(len(barVerts)==len(rangeSegments))
    assert(len(rangeSegments)==len(offsetsBars))
    assert(len(offsetsBars)==len(colors))
    useAA = 0,  # use tuple here
    lw = 0.5,   # and here
    rangeCollection = LineCollection(rangeSegments,
                                     colors       = ( (0,0,0,1), ),
                                     linewidths   = lw,
                                     antialiaseds = useAA,
    barCollection = PolyCollection(barVerts,
                                   facecolors   = colors,
                                   edgecolors   = ( (0,0,0,1), ),
                                   antialiaseds = useAA,
                                   linewidths   = lw,
                                   offsets      = offsetsBars,
                                   transOffset  = ax.transData,
    barCollection.set_transform(barTransform)
    minpy, maxx = (0, len(rangeSegments))
    miny = min([low for low in lows if low !=-1])
    maxy = max([high for high in highs if high != -1])
    corners = (minpy, miny), (maxx, maxy)
    ax.update_datalim(corners)
    ax.autoscale_view()
    # add these last
    ax.add_collection(barCollection)
    ax.add_collection(rangeCollection)
    return rangeCollection, barCollection
def volume_overlay(ax, opens, closes, volumes,
                   colorup='k', colordown='r',
                   width=4, alpha=1.0):
    Add a volume overlay to the current axes.  The opens and closes
    are used to determine the color of the bar.  -1 is missing.  If a
    value is missing on one it must be missing on all
    ax          : an Axes instance to plot to
    width       : the bar width in points
    colorup     : the color of the lines where close >= 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<close] for open, close in zip(opens, closes) if open!=-1 and close !=-1]
    right = width/2.0
    left = -width/2.0
    bars = [ ( (left, 0), (left, v), (right, v), (right, 0)) for v in volumes if v != -1 ]
    sx = ax.figure.dpi * (1.0/72.0)  # scale for points
    sy = (ax.bbox.ur().y() - ax.bbox.ll().y()) / (ax.viewLim.ur().y() - ax.viewLim.ll().y())
    barTransform = Affine2D().scaled(sx,sy)
    offsetsBars = [ (i, 0) for i,v in enumerate(volumes) if v != -1 ]
    barCollection = PolyCollection(bars,
                                   facecolors   = colors,
                                   edgecolors   = ( (0,0,0,1), ),
                                   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 volumes if v!=-1])
    corners = (minpy, miny), (maxx, maxy)
    ax.update_datalim(corners)
    ax.autoscale_view()
    # add these last
    return barCollection
def volume_overlay2(ax, closes, volumes,
                   colorup='k', colordown='r',
                   width=4, alpha=1.0):
    Add a volume overlay to the current axes.  The closes are used to
    determine the color of the bar.  -1 is missing.  If a value is
    missing on one it must be missing on all
    ax          : an Axes instance to plot to
    width       : the bar width in points
    colorup     : the color of the lines where close >= 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
    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.ur().y() - ax.bbox.ll().y()) / (ax.viewLim.ur().y() - ax.viewLim.ll().y())
    barTransform = Affine2D().scaled(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.ur().y() - ax.bbox.ll().y()) / (ax.viewLim.ur().y() - ax.viewLim.ll().y())
    barTransform = Affine2D().scaled(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
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