# Copyright (c) 2008 Andreas Balogh
# See LICENSE for details.

""" animated drawing of ticks

    using self.canvas embedded in Tk application
    
    Fibionacci retracements of 61.8, 50.0, 38.2, 23.6 % of min and max
"""

# system imports

import datetime
import os
import re
import logging
import warnings
import math

import Tkinter as Tk
import numpy as np

import matplotlib
matplotlib.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

# constants

ONE_MINUTE = 60. / 86400.
LOW, NONE, HIGH = range(-1, 2)

# 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("c:\\rttrd-prd-var\\consors-mdf\\data", year, yyyymmdd, filename)
    x = [ ]
    y = [ ]
    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)
    finally:
        fh.close()
    # throw away first line of file (close price from previous day)
    del x[0] 
    del y[0] 
    return (x, y) 

def num2sod(x):
    frac, integ = math.modf(x)
    return frac * 86400 

class Lohi:
    """Time series online low and high detector."""
    def __init__(self, bias):
        assert(bias > 0) 
        self.bias = bias
        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 - ONE_MINUTE 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 - ONE_MINUTE 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


class Acp:
    """Always correct predictor"""
    def __init__(self, lows, highs):
        self.lows = lows
        self.highs = highs

    def __call__(self, tick):
        """Always correct predictor.
        
        Requires previous run of DelayedAcp to determine lows and highs.
        
        @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
        return res


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:
            # initalise water mark
            self.mm0 = tick
            res = self.mm0
            self.lows = [(n, cdt, last - 1)]
            self.highs = [(n, cdt, last + 1)]
        else:
            # initalise 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 Main:
    def __init__(self):
        warnings.simplefilter("default", np.RankWarning)
        self.ylow = None
        self.yhigh = None
        self.advance_count = 1

        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) # diff from polyfit
        # ax3 = fig.add_subplot(313) # cash
    
        self.ax1.set_ylabel("ticks")
        self.ax2.set_ylabel("polyfit diff")
        # ax3.set_ylabel("cash")
    
        major_fmt = mdates.DateFormatter('%H:%M:%S')
        self.ax1.xaxis.set_major_formatter(major_fmt)
        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.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.x, self.y = tdl(datetime.datetime(2009, 6, 3))
        LOG.debug("Ticks loaded.")
        lows, highs = find_lows_highs(self.x, self.y)
        
        self.mmh = Lohi(10)
    
        self.w0 = 0
        self.wd = 1000
        self.low_high_crs = 0
        xr, yr = self.tick_window(self.w0, self.wd)
        fit = np.average(yr)
        
        self.tl, = self.ax1.plot_date(xr, yr, '-')
        self.fl, = self.ax1.plot_date(xr, (fit,) * len(xr), 'k--')
        self.mh, = self.ax1.plot_date(xr, (yr[0],) * len(xr), 'g:')
        self.ml, = self.ax1.plot_date(xr, (yr[0],) * len(xr), 'r:')
        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')

        self.dl, = self.ax2.plot_date(xr, (0,) * len(xr), '-')

        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)
        bu3 = Tk.Button(master=fr1, text='Resume', command=self.resume)
        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)
        bu3.pack(side=Tk.RIGHT, padx=5, pady=5)
        fr1.pack(side=Tk.BOTTOM)


    def animate(self):
        self.w0 += self.advance_count
        # prepare timeline window
        xr, yr = self.tick_window(self.w0, self.wd)
        while self.low_high_crs < self.w0 + self.wd:
            self.mark_low_high(self.low_high_crs)
            self.low_high_crs += 1
        # build polynomial fit
        mms = self.mmh.lohis
        if len(mms) > 1:
            mx = [ num2sod(x) for x in xr ]
            my = yr
            polyval = np.polyfit(mx, my, 10)
            fit = np.polyval(polyval, mx)
            self.fl.set_data(xr, fit)
            # calc diff
            self.dl.set_data(xr, yr - fit)
        highs = self.mmh.highs
        if len(highs) >= 4:
            coefs = np.polyfit([num2sod(x) for n, x, y in highs[-4:]], [y for n, x, y in highs[-4:]], 3)
            self.mh.set_data(xr, [np.polyval(coefs, num2sod(x)) for x in xr])
        lows = self.mmh.lows
        if len(lows) >= 4:
            coefs = np.polyfit([num2sod(x) for n, x, y in lows[-4:]], [y for n, x, y in lows[-4:]], 3)
            self.ml.set_data(xr, [np.polyval(coefs, num2sod(x)) for x in xr])
        # update tick line
        self.tl.set_data(xr, yr)
        # update axis
        self.set_axis(xr, yr)
        self.canvas.draw()
        if self.w0 < len(self.x) - 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], -25, +25])
    
    def tick_window(self, w0, wd = 1000):
        return (self.x[w0:w0 + wd], self.y[w0:w0 + wd])
        
    def mark_low_high(self, n):
        x = self.x
        y = self.y
        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

    def times_five(self):
        self.advance_count = 5
    
    def times_ten(self):
        self.advance_count = 10
    
    def run(self):
        self.root.after(500, self.animate)
        self.root.mainloop()
        self.root.destroy()


if __name__ == "__main__":
    app = Main()
    app.run()