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--HG-- branch : sandbox
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Andreas
330
mpl/mpl-embedded.py
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330
mpl/mpl-embedded.py
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# Copyright (c) 2008 Andreas Balogh
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# See LICENSE for details.
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""" animated drawing of ticks
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using self.canvas embedded in Tk application
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Fibionacci retracements of 61.8, 50.0, 38.2, 23.6 % of min and max
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"""
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# system imports
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import datetime
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import os
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import re
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import logging
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import sys
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import warnings
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import Tkinter as Tk
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import numpy as np
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import matplotlib
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matplotlib.use('TkAgg')
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from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
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from matplotlib.figure import Figure
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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from matplotlib.dates import date2num
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# local imports
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# constants
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# globals
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LOG = logging.getLogger()
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logging.basicConfig(level=logging.DEBUG,
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format='%(asctime)s.%(msecs)03i %(levelname).4s %(process)d:%(thread)d %(message)s',
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datefmt='%H:%M:%S')
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MDF_REO = re.compile("(..):(..):(..)\.*(\d+)*")
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def tdl(tick_date):
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""" returns a list of tick tuples (cdt, last) for specified day """
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fiid = "846900"
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year = tick_date.strftime("%Y")
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yyyymmdd = tick_date.strftime("%Y%m%d")
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filename = "%s.csv" % (fiid)
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filepath = os.path.join("d:\\rttrd-prd-var\\consors-mdf\\data", year, yyyymmdd, filename)
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x = [ ]
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y = [ ]
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fh = open(filepath, "r")
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try:
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prev_last = ""
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for line in fh:
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flds = line.split(",")
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# determine file version
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if flds[2] == "LAST":
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last = float(flds[3])
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vol = float(flds[4])
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else:
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last = float(flds[4])
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vol = 0.0
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# skip ticks with same last price
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if prev_last == last:
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continue
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else:
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prev_last = last
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# parse time
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mobj = MDF_REO.match(flds[0])
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if mobj is None:
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raise ValueError("no match for [%s]" % (flds[0],))
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(hh, mm, ss, ms) = mobj.groups()
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if ms:
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c_time = datetime.time(int(hh), int(mm), int(ss), int(ms) * 1000)
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else:
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c_time = datetime.time(int(hh), int(mm), int(ss))
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cdt = datetime.datetime.combine(tick_date, c_time)
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x.append(date2num(cdt))
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y.append(last)
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finally:
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fh.close()
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# throw away first line of file (close price from previous day)
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del x[0]
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del y[0]
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return (x, y)
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class Mmh:
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"""Time series max & min detector."""
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def __init__(self, bias):
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assert(bias > 0)
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self.bias = bias
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self.trend = None
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self.mm0 = None
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self.mms = [ ]
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self.mins = [ ]
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self.maxs = [ ]
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def __call__(self, tick):
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"""Add extended tick to the max min parser.
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@param tick: The value of the current tick.
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@type tick: tuple(cdt, last)
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@return: 1. Tick if new max min has been detected,
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2. None otherwise.
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"""
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n, cdt, last = tick
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res = None
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# automatic initialisation
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if self.mm0 is None:
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# initalise water mark
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self.mm0 = tick
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res = self.mm0
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self.mins = [(n, cdt, last - 1)]
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self.maxs = [(n, cdt, last + 1)]
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else:
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# initalise trend until price has changed
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if self.trend is None or self.trend == 0:
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self.trend = cmp(last, self.mm0[2])
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# check for max
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if self.trend > 0:
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if last > self.mm0[2]:
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self.mm0 = tick
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if last < self.mm0[2] - self.bias:
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self.mms.insert(0, self.mm0)
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self.maxs.append(self.mm0)
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res = self.mm0
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# revert trend & water mark
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self.mm0 = tick
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self.trend = -1
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# check for min
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if self.trend < 0:
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if last < self.mm0[2]:
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self.mm0 = tick
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if last > self.mm0[2] + self.bias:
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self.mms.insert(0, self.mm0)
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self.mins.append(self.mm0)
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res = self.mm0
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# revert trend & water mark
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self.mm0 = tick
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self.trend = +1
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return res
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class Main:
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def __init__(self):
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LOG.debug("Loading ticks...")
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self.x, self.y = tdl(datetime.datetime(2009,6,3))
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LOG.debug("Ticks loaded.")
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self.mmh = Mmh(10)
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self.root = Tk.Tk()
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self.root.wm_title("Embedding in TK")
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fig = plt.figure()
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self.ax1 = fig.add_subplot(111) # ticks
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# ax2 = fig.add_subplot(312) # gearing
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# ax3 = fig.add_subplot(313) # cash
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self.ax1.set_ylabel("ticks")
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# ax2.set_ylabel("gearing")
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# ax3.set_ylabel("cash")
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self.w = 1000
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self.bias = 10
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xr = self.x[0:self.w]
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yr = self.y[0:self.w]
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fit = np.average(yr)
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self.tl, = self.ax1.plot_date(xr, yr, '-')
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self.fl, = self.ax1.plot_date(xr, (fit, ) * self.w, 'k--')
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self.mh, = self.ax1.plot_date(xr, (yr[0], ) * self.w, 'g:')
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self.ml, = self.ax1.plot_date(xr, (yr[0], ) * self.w, 'r:')
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major_fmt = mdates.DateFormatter('%H:%M:%S')
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self.ax1.xaxis.set_major_formatter(major_fmt)
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self.ax1.format_xdata = mdates.DateFormatter('%H:%M:%S')
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self.ax1.format_ydata = lambda x: '%1.2f'%x
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self.ax1.grid(True)
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# rotates and right aligns the x labels, and moves the bottom of the
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# axes up to make room for them
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fig.autofmt_xdate()
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self.ax1.axis([xr[0], xr[-1]+180./86400., min(yr), max(yr)])
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self.canvas = FigureCanvasTkAgg(fig, master=self.root)
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self.canvas.draw()
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self.canvas.get_tk_widget().pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
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# toolbar = NavigationToolbar2TkAgg( self.canvas, self.root )
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# toolbar.update()
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# self.canvas._tkself.canvas.pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
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fr1 = Tk.Frame(master = self.root)
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bu1 = Tk.Button(master = fr1, text='Quit', command=self.root.quit)
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bu2 = Tk.Button(master = fr1, text='Stop', command=self.stop)
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bu3 = Tk.Button(master = fr1, text='Resume', command=self.resume)
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bu1.pack(side = Tk.RIGHT, padx = 5, pady = 5)
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bu2.pack(side = Tk.RIGHT, padx = 5, pady = 5)
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bu3.pack(side = Tk.RIGHT, padx = 5, pady = 5)
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fr1.pack(side = Tk.BOTTOM)
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def animate_start(self):
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warnings.simplefilter("default", np.RankWarning)
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self.ymin = min(self.y[0:self.w])
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self.ymax = max(self.y[0:self.w])
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self.low = self.ymin - self.bias
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self.high = self.ymax + self.bias
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self.trend = 0
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self.i = 0
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for n in range(0, self.w):
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self.mark_low_high(n)
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self.root.after(500, self.animate_step)
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def animate_step(self):
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# prepare timeline window
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xr = np.array( self.x[self.i:self.i+self.w] )
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yr = np.array( self.y[self.i:self.i+self.w] )
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# update line
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self.tl.set_xdata(xr)
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self.tl.set_ydata(yr)
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# determine y axis
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if yr[-1] > self.ymax:
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self.ymax = yr[-1]
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if self.ymax - 50 < min(yr):
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self.ymin = self.ymax - 50
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if yr[-1] < self.ymin:
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self.ymin = yr[-1]
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if self.ymin + 50 > max(yr):
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self.ymax = self.ymin + 50
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# check self.low self.high and annotate
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fwd = 2
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for n in range(self.i+self.w, self.i+self.w+fwd):
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self.mark_low_high(n)
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self.i += fwd
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# build polynomial fit
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mms = self.mmh.mms
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if len(mms) > 1:
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# mx = [ (x - int(x)) * 86400 for n, x, y in mms[:4] ]
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# my = [ y for n, x, y in mms[:4] ]
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mx = [ (x - int(x)) * 86400 for x in xr ]
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my = yr
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xre = [ xr[-1] + x/86400. for x in range(1, 181)]
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xr2 = np.append(xr, xre)
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# print "mx: ", mx
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# print "my: ", my
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polyval = np.polyfit(mx, my, 30)
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# print "poly1d: ", polyval
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intx = np.array(xr, dtype = int)
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sodx = xr - intx
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sodx *= 86400
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s2x = np.append(sodx, range(sodx[-1], sodx[-1]+180))
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fit = np.polyval(polyval, s2x)
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self.fl.set_xdata(xr2)
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self.fl.set_ydata(fit)
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maxs = self.mmh.maxs
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if len(maxs) > 1:
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n, x1, y1 = maxs[-2]
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n, x2, y2 = maxs[-1]
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x3 = xr[-1]
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polyfit = np.polyfit((x1, x2), (y1, y2), 1)
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y3 = np.polyval(polyfit, x3)
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self.mh.set_data((x1, x2, x3), (y1, y2, y3))
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mins = self.mmh.mins
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if len(mins) > 1:
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n, x1, y1 = mins[-2]
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n, x2, y2 = mins[-1]
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x3 = xr[-1]
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polyfit = np.polyfit((x1, x2), (y1, y2), 1)
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y3 = np.polyval(polyfit, x3)
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self.ml.set_data((x1, x2, x3), (y1, y2, y3))
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# draw
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self.ax1.axis([xr[0], xr[-1]+180./86400., self.ymin, self.ymax])
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self.canvas.draw()
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if self.i < len(self.x)-self.w-1:
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self.after_id = self.root.after(10, self.animate_step)
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def build_fit(self):
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pass
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def mark_low_high(self, n):
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x = self.x
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y = self.y
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rc = self.mmh((n, x[n], y[n]))
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if rc:
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nlh, xlh, ylh = rc
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if self.mmh.trend > 0:
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# low
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self.ax1.annotate('low',
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xy = (x[nlh], y[nlh]),
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xytext = (x[n], y[nlh]),
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arrowprops = dict(facecolor = 'red',
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shrink = 0.05))
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# an.set_annotation_clip(True)
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elif self.mmh.trend < 0:
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# high
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self.ax1.annotate('high',
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xy = (x[nlh], y[nlh]),
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xytext = (x[n], y[nlh]),
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arrowprops = dict(facecolor = 'green',
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shrink = 0.05))
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# an.set_annotation_clip(True)
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def stop(self):
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if self.after_id:
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self.root.after_cancel(self.after_id)
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self.after_id = None
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def resume(self):
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if self.after_id is None:
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self.after_id = self.root.after(10, self.animate_step)
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def run(self):
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self.root.after(500, self.animate_start)
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self.root.mainloop()
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self.root.destroy()
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if __name__ == "__main__":
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app = Main()
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app.run()
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