checkin after svn update

--HG--
branch : sandbox

mpl/namedtuple.py

@@ -0,0 +1,40 @@
+# http://code.activestate.com/recipes/500261/
+
+from operator import itemgetter 
+import sys 
+
+def __from_iterable__(cls,arg):
+    return cls.__new__(cls,*arg)
+
+
+def NamedTuple(typename, field_names):
+    if isinstance(field_names, str):
+        field_names = field_names.split()
+    nargs = len(field_names)
+
+    def __new__(cls, *args, **kwds):
+        if (len(args) == 1) and (getattr(args[0], '__iter__', False)):
+            args = tuple(name for name in args[0])
+        if kwds:
+            try:
+                args += tuple(kwds[name] for name in field_names[len(args):])
+            except KeyError, name:
+                raise TypeError('%s missing required argument: %s' % (typename, name))
+        if len(args) != nargs:
+            raise TypeError('%s takes exactly %d arguments (%d given)' % (typename, nargs, len(args)))
+        return tuple.__new__(cls, args)
+
+    repr_template = '%s(%s)' % (typename, ', '.join('%s=%%r' % name for name in field_names))
+
+    m = dict(vars(tuple))       # pre-lookup superclass methods (for faster lookup)
+    m.update(__doc__= '%s(%s)' % (typename, ', '.join(field_names)),
+             __slots__ = (),    # no per-instance dict (so instances are same size as tuples)
+             __new__ = __new__,
+             __repr__ = lambda self, _format=repr_template.__mod__: _format(self),
+             __module__ = sys._getframe(1).f_globals['__name__'],
+             __field_names__ = tuple(field_names),
+             __from_iterable__=classmethod(__from_iterable__),
+             )
+    m.update((name, property(itemgetter(index))) for index, name in enumerate(field_names))
+
+    return type(typename, (tuple,), m)

mpl/sw-trend1.py

@@ -362,12 +362,12 @@ class Main:
         
         # create plot
         fig = plt.figure()
-        self.ax1 = fig.add_subplot(311) # ticks
+        self.ax1 = fig.add_subplot(211) # ticks
-        self.ax2 = fig.add_subplot(312) # slope of line segement
+        # self.ax2 = fig.add_subplot(312) # slope of line segement
-        self.ax3 = fig.add_subplot(313) # moving average (10min)
+        self.ax3 = fig.add_subplot(212) # moving average (10min)
     
         self.ax1.set_ylabel("ticks")
-        self.ax2.set_ylabel("slope")
+        # self.ax2.set_ylabel("slope")
         self.ax3.set_ylabel("gearing")
     
         major_fmt = mdates.DateFormatter('%H:%M:%S')
@@ -378,12 +378,14 @@ class Main:
         self.ax1.format_ydata = lambda x: '%1.2f' % x
         self.ax1.grid(True)
 
+        """
         self.ax2.xaxis.set_major_formatter(major_fmt)
         self.ax2.xaxis.set_major_locator(mdates.MinuteLocator(byminute = range(0, 60, 10)))
         self.ax2.xaxis.set_minor_locator(mdates.MinuteLocator())
         self.ax2.format_xdata = major_fmt
         self.ax2.format_ydata = lambda x: '%1.2f' % x
         self.ax2.grid(True)
+        """
 
         self.ax3.xaxis.set_major_formatter(major_fmt)
         self.ax3.xaxis.set_major_locator(mdates.MinuteLocator(byminute = range(0, 60, 10)))
@@ -398,7 +400,7 @@ class Main:
 
         # create artists
         LOG.debug("Loading ticks...")
-        self.xs, self.ys, self.vs = tdl(datetime.datetime(2009, 7, 2))
+        self.xs, self.ys, self.vs = tdl(datetime.datetime(2009, 7, 1))
         LOG.debug("Ticks loaded.")
         lows, highs = find_lows_highs(self.xs, self.ys)
         self.mas = self.ys[:]
@@ -406,7 +408,7 @@ class Main:
         self.gs = [ 0 ] * len(self.xs)
         
         self.mmh = TimedLohi(5)
-        self.osw = SlidingWindow(5)
+        self.osw = SlidingWindow(2)
     
         self.w0 = 0
         self.wd = 2000
@@ -426,7 +428,7 @@ class Main:
         self.dl, = self.ax1.plot_date(xr, vr, 'g-')
 
         # slope subplot
-        self.sl, = self.ax2.plot_date(xr, sr, '-')
+        # self.sl, = self.ax2.plot_date(xr, sr, '-')
 
         # gearing subplot
         self.gl, = self.ax3.plot_date(xr, gr, '-')
@@ -468,7 +470,7 @@ class Main:
         # update tick line
         self.tl.set_data(xr, yr)
         # update segment slope
-        self.sl.set_data(xr, sr)
+        # self.sl.set_data(xr, sr)
         # update volume line
         self.dl.set_data(xr, vr)
         # gearing line
@@ -491,7 +493,7 @@ class Main:
                 self.yhigh = y
                 self.ylow = self.yhigh - bias
         self.ax1.axis([xr[0], xr[-1], self.ylow, self.yhigh])
-        self.ax2.axis([xr[0], xr[-1], -5, +5])
+        # self.ax2.axis([xr[0], xr[-1], -5, +5])
         self.ax3.axis([xr[0], xr[-1], -50, +50])
     
     def tick_window(self, w0, wd = 1000):

mpl/sw-trend2.py

@@ -0,0 +1,604 @@
+# Copyright (c) 2009 Andreas Balogh
+# See LICENSE for details.
+
+"""
+Online sliding window with trend analysis
+
+1. segment tick data with a sliding window alogrithm
+2. recognise low/high points by comparing slope information
+3. recognise trend by observing low/high point difference 
+"""
+
+# system imports
+
+import datetime
+import os
+import re
+import logging
+import warnings
+import math
+
+import Tkinter as Tk
+import numpy as np
+
+import matplotlib as mpl
+mpl.use('TkAgg')
+
+from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
+import matplotlib.pyplot as plt
+import matplotlib.dates as mdates
+from matplotlib.dates import date2num
+
+# local imports
+
+from namedtuple import NamedTuple
+from globals import *
+
+# constants
+
+ONE_MINUTE = 60. / 86400.
+LOW, NONE, HIGH = range(-1, 2)
+
+Trend = NamedTuple('Trend', 'n x y')
+
+# globals
+
+LOG = logging.getLogger()
+
+logging.basicConfig(level=logging.DEBUG,
+                    format='%(asctime)s.%(msecs)03i %(levelname).4s %(process)d:%(thread)d %(message)s',
+                    datefmt='%H:%M:%S')
+
+MDF_REO = re.compile("(..):(..):(..)\.*(\d+)*")
+
+
+def tdl(tick_date):
+    """ returns a list of tick tuples (cdt, last) for specified day """
+    fiid = "846900"
+    year = tick_date.strftime("%Y")
+    yyyymmdd = tick_date.strftime("%Y%m%d")
+    filename = "%s.csv" % (fiid)
+    filepath = os.path.join(RTTRD_VAR, "consors-mdf\\data", year, yyyymmdd, filename)
+    x = [ ]
+    y = [ ]
+    v = [ ]
+    fh = open(filepath, "r")
+    try:
+        prev_last = ""
+        for line in fh:
+            flds = line.split(",")
+            # determine file version
+            if flds[2] == "LAST":
+                last = float(flds[3])
+                vol = float(flds[4])
+            else:
+                last = float(flds[4])
+                vol = 0.0
+            # skip ticks with same last price
+            if prev_last == last:
+                continue
+            else:
+                prev_last = last 
+            # parse time
+            mobj = MDF_REO.match(flds[0])
+            if mobj is None:
+                raise ValueError("no match for [%s]" % (flds[0],))
+            (hh, mm, ss, ms) = mobj.groups()
+            if ms:
+                c_time = datetime.time(int(hh), int(mm), int(ss), int(ms) * 1000)
+            else:
+                c_time = datetime.time(int(hh), int(mm), int(ss))
+            cdt = datetime.datetime.combine(tick_date, c_time)
+            x.append(date2num(cdt))
+            y.append(last)
+            v.append(vol)
+    finally:
+        fh.close()
+    # throw away first line of file (close price from previous day)
+    del x[0] 
+    del y[0] 
+    del v[0]
+    return (x, y, v) 
+
+def interpolate_line(xs, ys):
+    """Fit a straight line y = bx + a to a set of points (x, y) """
+    # from two data points only!
+    x1, x2 = xs
+    y1, y2 = ys
+    try:
+        b = ( y2 - y1 ) / ( x2 - x1 )
+    except ZeroDivisionError:
+        print "interpolate_line: division by zero, ", x1, x2, y1, y2
+        b = 0.0
+    a = y1 - b * x1 
+    return (b, a) 
+
+def num2sod(x):
+    frac, integ = math.modf(x)
+    return frac * 86400 
+
+
+class Bunch:
+    def __init__(self, **kwds):
+        self.__dict__.update(kwds)
+
+
+class TimedLohi:
+    """Time series online low and high detector.
+    
+    Confirms low/high candidates after timeout.
+    Time dependent.
+    """
+    def __init__(self, bias, timeout = ONE_MINUTE):
+        assert(bias > 0) 
+        self.bias = bias
+        self.timeout = timeout
+        self.low0 = None
+        self.high0 = None
+        self.prev_lohi = NONE
+        self.lohis = [ ]
+        self.lows = [ ]
+        self.highs = [ ]
+
+    def __call__(self, tick):
+        """Add extended tick to the max min parser.
+        
+        @param tick: The value of the current tick.
+        @type tick: tuple(cdt, last)
+
+        @return: 1. Tick if new max min has been detected,
+            2. None otherwise.
+        """
+        n, cdt, last = tick
+        res = None
+        # automatic initialisation
+        if self.low0 is None:
+            self.low0 = tick
+            self.lows.append((n, cdt, last - 1))
+        if self.high0 is None:
+            self.high0 = tick
+            self.highs.append((n, cdt, last + 1))
+        if last > self.high0[2]:
+            self.high0 = tick
+            if self.prev_lohi == NONE:
+                if self.high0[2] > self.low0[2] + self.bias:
+                    res = self.high0
+                    self.low0 = self.high0
+                    self.lows.append(self.high0)
+                    self.lohis.append(self.high0)
+                    self.prev_lohi = HIGH
+        if last < self.low0[2]:
+            self.low0 = tick
+            if self.prev_lohi == NONE:
+                if self.low0[2] < self.high0[2] - self.bias:
+                    res = self.low0
+                    self.high0 = self.low0  
+                    self.lows.append(self.low0)
+                    self.lohis.append(self.low0)
+                    self.prev_lohi = LOW
+        if self.high0[1] < cdt - self.timeout and \
+            ((self.prev_lohi == LOW and \
+              self.high0[2] > self.lows[-1][2] + self.bias) or
+             (self.prev_lohi == HIGH and \
+              self.high0[2] > self.highs[-1][2])):
+            res = self.high0
+            self.low0 = self.high0
+            self.highs.append(self.high0)
+            self.lohis.append(self.high0)
+            self.prev_lohi = HIGH
+        if self.low0[1] < cdt - self.timeout and \
+            ((self.prev_lohi == LOW and \
+              self.low0[2] < self.lows[-1][2]) or 
+             (self.prev_lohi == HIGH and \
+              self.low0[2] < self.highs[-1][2] - self.bias)):
+            res = self.low0
+            self.high0 = self.low0  
+            self.lows.append(self.low0)
+            self.lohis.append(self.low0)
+            self.prev_lohi = LOW
+        if res:
+            return (self.prev_lohi, res)
+        else:
+            return None
+
+
+def find_lows_highs(xs, ys):
+    dacp = DelayedAcp(10)
+    for tick in zip(range(len(xs)), xs, ys):
+        dacp(tick)
+    return dacp.lows, dacp.highs    
+
+
+class DelayedAcp:
+    """Time series max & min detector."""
+    def __init__(self, bias):
+        assert(bias > 0) 
+        self.bias = bias
+        self.trend = None
+        self.mm0 = None
+        self.lohis = [ ]
+        self.lows = [ ]
+        self.highs = [ ]
+
+    def __call__(self, tick):
+        """Add extended tick to the max min parser.
+        
+        @param tick: The value of the current tick.
+        @type tick: tuple(n, cdt, last)
+
+        @return: 1. Tick if new max min has been detected,
+            2. None otherwise.
+        """
+        n, cdt, last = tick
+        res = None
+        # automatic initialisation
+        if self.mm0 is None:
+            # initialise water mark
+            self.mm0 = tick
+            res = self.mm0
+            self.lows = [(n, cdt, last - 1)]
+            self.highs = [(n, cdt, last + 1)]
+        else:
+            # initialise trend until price has changed
+            if self.trend is None or self.trend == 0:
+                self.trend = cmp(last, self.mm0[2])
+        # check for max
+        if self.trend > 0:
+            if last > self.mm0[2]:
+                self.mm0 = tick
+            if last < self.mm0[2] - self.bias:
+                self.lohis.append(self.mm0)
+                self.highs.append(self.mm0)
+                res = self.mm0
+                # revert trend & water mark
+                self.mm0 = tick
+                self.trend = -1
+        # check for min
+        if self.trend < 0:
+            if last < self.mm0[2]:
+                self.mm0 = tick
+            if last > self.mm0[2] + self.bias:
+                self.lohis.append(self.mm0)
+                self.lows.append(self.mm0)
+                res = self.mm0
+                # revert trend & water mark
+                self.mm0 = tick
+                self.trend = +1
+        return (cmp(self.trend, 0), res)
+
+
+class SlidingWindow:
+    """Douglas-Peucker algorithm."""
+    def __init__(self, bias):
+        assert(bias > 0) 
+        self.bias = bias
+        self.xs = [ ]
+        self.ys = [ ]
+        self.segx = [ ]
+        self.segy = [ ]
+        self.types = [ ]
+        self.bs = [ ]
+
+    def __call__(self, tick):
+        """Add extended tick to the max min parser.
+        
+        @param tick: The value of the current tick.
+        @type tick: tuple(n, cdt, last)
+
+        @return: 1. Tick if new max min has been detected,
+            2. None otherwise.
+        """
+        n, cdt, last = tick
+        max_distance = self.bias
+        rc = None
+        self.xs.append(cdt)
+        self.ys.append(last)
+        x0, y0 = (self.xs[0], self.ys[0])
+        x1, y1 = (self.xs[-1], self.ys[-1])
+        if n == 0:
+            self.segx.append(x0)
+            self.segy.append(y0)
+        if len(self.xs) < 2:
+            return None
+        # check distance
+        coefs = interpolate_line((x0, x1), (y0, y1))
+        ip_ys = np.polyval(coefs, self.xs)
+        d_ys = np.absolute(self.ys - ip_ys)
+        d_max = np.amax(d_ys) 
+        if d_max > max_distance:
+            n = np.argmax(d_ys)
+            x2, y2 = (self.xs[n], self.ys[n])
+            self.segx.append(x2)
+            self.segy.append(y2)
+            segment_added = True
+            # store slope of segment
+            b0, a0 = interpolate_line((x0, x2), (y0, y2))
+            self.bs.append(b0)
+            # remove ticks of previous segment
+            del self.xs[0:n]
+            del self.ys[0:n]
+            # slope of current segment 
+            x0, y0 = (self.xs[0], self.ys[0])
+            b1, a1 = interpolate_line((x0, x1), (y0, y1))
+            lohi = self.get_type(b0, b1)
+            rc = (x2, y2, lohi)
+        return (self.segx + [x1], self.segy + [y1], rc)
+
+    def get_type(self, b0, b1):
+        """ calculate gearing
+        y: previous slope, x: current slope
+            <0 ~0 >0
+        <0  L  L  L
+        ~0  H  0  L
+        >0  H  H  H
+        """
+        if b0 < -SMALL and b1 < -SMALL and b0 > b1:
+            lohi = "d+" 
+        elif b0 < -SMALL and b1 < SMALL and b0 < b1:
+            lohi = "d-" 
+        elif b0 < -SMALL and b1 > SMALL:
+            lohi = "L"
+        elif abs(b0) < SMALL and b1 < -SMALL:
+            lohi = "d+"
+        elif abs(b0) < SMALL and abs(b1) < SMALL:
+            lohi = "0"
+        elif abs(b0) < SMALL and b1 > SMALL:
+            lohi = "u+"
+        elif b0 > SMALL and b1 < -SMALL:
+            lohi = "H"
+        elif b0 > SMALL and b1 > -SMALL and b0 > b1:
+            lohi = "u-"
+        elif b0 > SMALL and b1 > SMALL and b0 < b1:
+            lohi = "u+"
+        else:
+            lohi = "?"
+        return lohi
+
+
+SMALL = 1E-10
+
+
+class Main:
+    def __init__(self):
+        warnings.simplefilter("default", np.RankWarning)
+        self.advance_count = 10
+        self.ylow = None
+        self.yhigh = None
+        self.trend_starts = None
+        self.segs = [ ]
+
+        self.root = Tk.Tk()
+        self.root.wm_title("Embedding in TK")
+        
+        # create plot
+        fig = plt.figure()
+        self.ax1 = fig.add_subplot(211) # ticks
+        self.ax2 = fig.add_subplot(212) # moving average (10min)
+    
+        self.ax1.set_ylabel("ticks")
+        self.ax2.set_ylabel("gearing")
+    
+        major_fmt = mdates.DateFormatter('%H:%M:%S')
+        self.ax1.xaxis.set_major_formatter(major_fmt)
+        self.ax1.xaxis.set_major_locator(mdates.MinuteLocator(byminute = range(0, 60, 10)))
+        self.ax1.xaxis.set_minor_locator(mdates.MinuteLocator())
+        self.ax1.format_xdata = major_fmt
+        self.ax1.format_ydata = lambda x: '%1.2f' % x
+        self.ax1.grid(True)
+
+        self.ax2.xaxis.set_major_formatter(major_fmt)
+        self.ax2.xaxis.set_major_locator(mdates.MinuteLocator(byminute = range(0, 60, 10)))
+        self.ax2.xaxis.set_minor_locator(mdates.MinuteLocator())
+        self.ax2.format_xdata = major_fmt
+        self.ax2.format_ydata = lambda x: '%1.2f' % x
+        self.ax2.grid(True)
+        
+        # rotates and right aligns the x labels, and moves the bottom of the
+        # axes up to make room for them
+        fig.autofmt_xdate()
+
+        # create artists
+        LOG.debug("Loading ticks...")
+        self.xs, self.ys, self.vs = tdl(datetime.datetime(2009, 7, 1))
+        LOG.debug("Ticks loaded.")
+        lows, highs = find_lows_highs(self.xs, self.ys)
+        self.mas = self.ys[:]
+        
+        self.mmh = TimedLohi(5)
+        self.osw = SlidingWindow(2)
+    
+        self.w0 = 0
+        self.wd = 2000
+        self.w_crs = 0 
+        xr, yr, mar = self.tick_window(self.w0, self.wd)
+        self.gr = [0.0] * self.wd
+        
+        # add artists to top subplot
+        # tick line and segments
+        self.tl, = self.ax1.plot_date(xr, yr, '-')
+        self.seg, = self.ax1.plot_date((xr[0], xr[1]), (yr[0], yr[1]), 'k-')
+        # Acp markers
+        self.him, = self.ax1.plot_date([x for n, x, y in lows], [y for n, x, y in lows], 'go')
+        self.lom, = self.ax1.plot_date([x for n, x, y in highs], [y for n, x, y in highs], 'ro')
+        # trend lines
+        self.trd, = self.ax1.plot_date(xr[0:1], yr[0:1], 'k--')
+        self.trh, = self.ax1.plot_date(xr[0:1], yr[0:1], 'k-')
+        self.trl, = self.ax1.plot_date(xr[0:1], yr[0:1], 'k-')
+
+        # add artists to bottom subplot
+        self.gl, = self.ax2.plot_date(xr, self.gr, '-')
+
+        self.set_axis(xr, yr)
+
+        # embed canvas in Tk 
+        self.canvas = FigureCanvasTkAgg(fig, master=self.root)
+        self.canvas.draw()
+        self.canvas.get_tk_widget().pack(side=Tk.TOP, fill=Tk.BOTH, expand=Tk.TRUE)
+        
+        # toolbar = NavigationToolbar2TkAgg( self.canvas, self.root )
+        # toolbar.update()
+        # self.canvas._tkself.canvas.pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
+        
+        fr1 = Tk.Frame(master=self.root)
+        bu1 = Tk.Button(master=fr1, text='Quit', command=self.root.quit)
+        bu2 = Tk.Button(master=fr1, text='Stop', command=self.stop)
+        bu4 = Tk.Button(master=fr1, text='1x', command=self.times_one)
+        bu5 = Tk.Button(master=fr1, text='5x', command=self.times_five)
+        bu6 = Tk.Button(master=fr1, text='10x', command=self.times_ten)
+        bu1.pack(side=Tk.RIGHT, padx=5, pady=5)
+        bu6.pack(side=Tk.RIGHT, padx=5, pady=5)
+        bu5.pack(side=Tk.RIGHT, padx=5, pady=5)
+        bu4.pack(side=Tk.RIGHT, padx=5, pady=5)
+        bu2.pack(side=Tk.RIGHT, padx=5, pady=5)
+        fr1.pack(side=Tk.BOTTOM)
+
+
+    def animate(self):
+        self.w0 += self.advance_count
+        # prepare timeline window
+        while self.w_crs < self.w0 + self.wd:
+            self.ma(self.w_crs, 10)
+            self.fitter(self.w_crs)
+            self.w_crs += 1
+        xr, yr, mar = self.tick_window(self.w0, self.wd)
+        # update tick line
+        self.tl.set_data(xr, yr)
+        # gearing line
+        # self.gl.set_data(xr, gr)
+        # update axis
+        self.set_axis(xr, yr)
+        self.canvas.draw()
+        if self.w0 < len(self.xs) - self.wd - 1:
+            self.after_id = self.root.after(10, self.animate)
+            
+    def set_axis(self, xr, yr, bias=50):
+        if self.ylow is None: 
+            self.ylow = yr[0] - bias / 2
+            self.yhigh = yr[0] + bias / 2
+        for y in yr:
+            if y < self.ylow:
+                self.ylow = y
+                self.yhigh = self.ylow + bias
+            if y > self.yhigh:
+                self.yhigh = y
+                self.ylow = self.yhigh - bias
+        self.ax1.axis([xr[0], xr[-1], self.ylow, self.yhigh])
+        self.ax2.axis([xr[0], xr[-1], -50, +50])
+    
+    def tick_window(self, w0, wd = 1000):
+        return (self.xs[w0:w0 + wd], 
+                self.ys[w0:w0 + wd], 
+                self.mas[w0:w0 + wd],
+                )
+        
+    def ma(self, n0, min):
+        self.mas[n0] = np.average(self.ys[n0-min*60:n0])
+    
+    def fitter(self, n0):
+        # find last low/high within t-1
+        # linear regression from t-5 to t-1
+        # linear regression within t-1
+        # visual inspection
+
+        # determine run-on low and highs
+        if self.trend_starts is None:
+            self.trend_starts = [Trend(n=n0, x=self.xs[n0], y=self.ys[n0])]
+        trend_start = self.trend_starts[-1]
+        # wait for 30 secs to stabilise
+        if trend_start.n + 30 > n0:
+            return
+        # fit trend
+        xr = self.xs[trend_start.n:n0]
+        yr = self.ys[trend_start.n:n0]
+        ps = np.polyfit(xr, yr, 1)
+        trend_xs = [xr[0], xr[-1]]
+        trend_ys = np.polyval(ps, trend_xs)
+        self.trd.set_data(trend_xs, trend_ys)
+        # fit counter trend
+        
+    
+    def mark_segments(self, n):
+        x = self.xs
+        y = self.ys
+        rc = self.osw((n, x[n], y[n]))
+        if rc is not None:
+            segx, segy, lohi = rc 
+            self.seg.set_data(segx, segy)
+            if lohi is not None:
+                text = lohi[2]
+                if text == "u+":
+                    fc = "blue"
+                    dy = -15
+                elif text == "d+":
+                    fc = "blue"
+                    dy = +15
+                elif text == "H":
+                    fc = "green"
+                    dy = +15
+                elif text == "L":
+                    fc = "red"
+                    dy = -15
+                else:
+                    fc = None
+                if fc:
+                    self.ax1.annotate(text,
+                                xy=(lohi[0], lohi[1]),
+                                xytext=(segx[-1], segy[-2]+dy),
+                                arrowprops=dict(facecolor=fc,
+                                                  frac=0.3,
+                                                  shrink=0.1))
+        
+    def mark_low_high(self, n):
+        x = self.xs
+        y = self.ys
+        rc = self.mmh((n, x[n], y[n]))
+        if rc:
+            lohi, tick = rc
+            nlh, xlh, ylh = tick
+            if lohi < 0:
+                # low
+                self.ax1.annotate('low',
+                            xy=(x[nlh], y[nlh]),
+                            xytext=(x[n], y[nlh]),
+                            arrowprops=dict(facecolor='red',
+                                              frac=0.3,
+                                              shrink=0.1))
+            elif lohi > 0:
+                # high
+                self.ax1.annotate('high',
+                            xy=(x[nlh], y[nlh]),
+                            xytext=(x[n], y[nlh]),
+                            arrowprops=dict(facecolor='green',
+                                              frac=0.3,
+                                              shrink=0.1))
+
+    def stop(self):
+        if self.after_id:
+            self.root.after_cancel(self.after_id)
+            self.after_id = None
+    
+    def resume(self):
+        if self.after_id is None:
+            self.after_id = self.root.after(10, self.animate)
+
+    def times_one(self):
+        self.advance_count = 1
+        self.resume()
+
+    def times_five(self):
+        self.advance_count = 5
+        self.resume()
+    
+    def times_ten(self):
+        self.advance_count = 10
+        self.resume()
+    
+    def run(self):
+        self.root.after(500, self.animate)
+        self.root.mainloop()
+        self.root.destroy()
+
+
+if __name__ == "__main__":
+    app = Main()
+    app.run()