Initial checkin
--HG-- branch : sandbox
This commit is contained in:
Andreas
449
mpl/fibionacci.py
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449
mpl/fibionacci.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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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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ONE_MINUTE = 60. / 86400.
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LOW, NONE, HIGH = range(-1, 2)
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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 Lohi:
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"""Time series online low and high 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.low0 = None
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self.high0 = None
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self.prev_lohi = NONE
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self.lohis = [ ]
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self.lows = [ ]
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self.highs = [ ]
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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.low0 is None:
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self.low0 = tick
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self.lows.append((n, cdt, last - 1))
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if self.high0 is None:
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self.high0 = tick
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self.highs.append((n, cdt, last + 1))
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if last > self.high0[2]:
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self.high0 = tick
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if self.prev_lohi == NONE:
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if self.high0[2] > self.low0[2] + self.bias:
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res = self.high0
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self.low0 = self.high0
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self.lows.append(self.high0)
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self.lohis.append(self.high0)
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self.prev_lohi = HIGH
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if last < self.low0[2]:
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self.low0 = tick
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if self.prev_lohi == NONE:
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if self.low0[2] < self.high0[2] - self.bias:
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res = self.low0
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self.high0 = self.low0
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self.lows.append(self.low0)
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self.lohis.append(self.low0)
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self.prev_lohi = LOW
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if self.high0[1] < cdt - ONE_MINUTE and \
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((self.prev_lohi == LOW and \
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self.high0[2] > self.lows[-1][2] + self.bias) or
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(self.prev_lohi == HIGH and \
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self.high0[2] > self.highs[-1][2])):
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res = self.high0
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self.low0 = self.high0
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self.highs.append(self.high0)
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self.lohis.append(self.high0)
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self.prev_lohi = HIGH
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if self.low0[1] < cdt - ONE_MINUTE and \
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((self.prev_lohi == LOW and \
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self.low0[2] < self.lows[-1][2]) or
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(self.prev_lohi == HIGH and \
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self.low0[2] < self.highs[-1][2] - self.bias)):
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res = self.low0
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self.high0 = self.low0
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self.lows.append(self.low0)
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self.lohis.append(self.low0)
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self.prev_lohi = LOW
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if res:
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return (self.prev_lohi, res)
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else:
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return None
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class Acp:
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"""Always correct predictor"""
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def __init__(self, lows, highs):
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self.lows = lows
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self.highs = highs
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def __call__(self, tick):
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"""Always correct predictor.
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Requires previous run of DelayedAcp to determine lows and highs.
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@param tick: The value of the current tick.
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@type tick: tuple(n, 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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return res
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def find_lows_highs(xs, ys):
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dacp = DelayedAcp(10)
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for tick in zip(range(len(xs)), xs, ys):
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dacp(tick)
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return dacp.lows, dacp.highs
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class DelayedAcp:
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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.lohis = [ ]
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self.lows = [ ]
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self.highs = [ ]
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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(n, 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.lows = [(n, cdt, last - 1)]
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self.highs = [(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.lohis.append(self.mm0)
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self.highs.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.lohis.append(self.mm0)
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self.lows.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 (cmp(self.trend, 0), res)
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class Main:
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def __init__(self):
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warnings.simplefilter("default", np.RankWarning)
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self.ylow = None
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self.yhigh = None
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self.advance_count = 1
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self.root = Tk.Tk()
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self.root.wm_title("Embedding in TK")
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# create plot
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fig = plt.figure()
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self.ax1 = fig.add_subplot(211) # ticks
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self.ax2 = fig.add_subplot(212) # diff from polyfit
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# ax3 = fig.add_subplot(313) # cash
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self.ax1.set_ylabel("ticks")
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self.ax2.set_ylabel("polyfit diff")
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# ax3.set_ylabel("cash")
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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 = major_fmt
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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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self.ax2.xaxis.set_major_formatter(major_fmt)
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self.ax2.format_xdata = major_fmt
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self.ax2.format_ydata = lambda x: '%1.2f' % x
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self.ax2.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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# create artists
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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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lows, highs = find_lows_highs(self.x, self.y)
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self.mmh = Lohi(10)
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self.w0 = 0
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self.wd = 1000
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self.low_high_crs = 0
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xr, yr = self.tick_window(self.w0, self.wd)
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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,) * len(xr), 'k--')
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self.mh, = self.ax1.plot_date(xr, (yr[0],) * len(xr), 'g:')
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self.ml, = self.ax1.plot_date(xr, (yr[0],) * len(xr), 'r:')
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self.him, = self.ax1.plot_date([x for n, x, y in lows], [y for n, x, y in lows], 'go')
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self.lom, = self.ax1.plot_date([x for n, x, y in highs], [y for n, x, y in highs], 'ro')
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self.dl, = self.ax2.plot_date(xr, (0,) * len(xr), '-')
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self.set_axis(xr, yr)
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# embed canvas in Tk
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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=Tk.TRUE)
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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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bu4 = Tk.Button(master=fr1, text='1x', command=self.times_one)
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bu5 = Tk.Button(master=fr1, text='5x', command=self.times_five)
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bu6 = Tk.Button(master=fr1, text='10x', command=self.times_ten)
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bu1.pack(side=Tk.RIGHT, padx=5, pady=5)
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bu6.pack(side=Tk.RIGHT, padx=5, pady=5)
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bu5.pack(side=Tk.RIGHT, padx=5, pady=5)
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bu4.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(self):
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self.w0 += self.advance_count
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# prepare timeline window
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xr, yr = self.tick_window(self.w0, self.wd)
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while self.low_high_crs < self.w0 + self.wd:
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self.mark_low_high(self.low_high_crs)
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self.low_high_crs += 1
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# build polynomial fit
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mms = self.mmh.lohis
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if len(mms) > 1:
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mx = [ (x - int(x)) * 86400 for x in xr ]
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my = yr
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polyval = np.polyfit(mx, my, 10)
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fit = np.polyval(polyval, mx)
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self.fl.set_data(xr, fit)
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# calc diff
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self.dl.set_data(xr, yr - fit)
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maxs = self.mmh.highs
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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.lows
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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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# update tick line
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self.tl.set_data(xr, yr)
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# update axis
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self.set_axis(xr, yr)
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self.canvas.draw()
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if self.w0 < len(self.x) - self.wd - 1:
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self.after_id = self.root.after(10, self.animate)
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def set_axis(self, xr, yr, bias=50):
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if self.ylow is None:
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self.ylow = yr[0] - bias / 2
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self.yhigh = yr[0] + bias / 2
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for y in yr:
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if y < self.ylow:
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self.ylow = y
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self.yhigh = self.ylow + bias
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if y > self.yhigh:
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self.yhigh = y
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self.ylow = self.yhigh - bias
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self.ax1.axis([xr[0], xr[-1], self.ylow, self.yhigh])
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self.ax2.axis([xr[0], xr[-1], -25, +25])
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def tick_window(self, w0, wd=1000):
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return (self.x[w0:w0 + wd], self.y[w0:w0 + wd])
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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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lohi, tick = rc
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nlh, xlh, ylh = tick
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if lohi < 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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frac=0.3,
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shrink=0.1))
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elif lohi > 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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||||
frac=0.3,
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||||
shrink=0.1))
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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)
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def times_one(self):
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self.advance_count = 1
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def times_five(self):
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self.advance_count = 5
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def times_ten(self):
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self.advance_count = 10
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||||
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def run(self):
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self.root.after(500, self.animate)
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self.root.mainloop()
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||||
self.root.destroy()
|
||||
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||||
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if __name__ == "__main__":
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app = Main()
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||||
app.run()
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||||
444
mpl/fibionacci2.py
Normal file
444
mpl/fibionacci2.py
Normal file
@@ -0,0 +1,444 @@
|
||||
# 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()
|
||||
174
mpl/mpl-blit.py
Normal file
174
mpl/mpl-blit.py
Normal file
@@ -0,0 +1,174 @@
|
||||
# Copyright (c) 2009 Andreas Balogh
|
||||
# See LICENSE for details.
|
||||
|
||||
""" animated drawing of ticks
|
||||
|
||||
using blit method
|
||||
"""
|
||||
|
||||
# system imports
|
||||
|
||||
import Tkinter as tk
|
||||
import datetime
|
||||
import os
|
||||
import re
|
||||
import logging
|
||||
import sys
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.dates import date2num
|
||||
import matplotlib.dates as mdates
|
||||
import numpy as np
|
||||
|
||||
# local imports
|
||||
|
||||
# constants
|
||||
|
||||
# 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("d:\\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 main():
|
||||
LOG.debug("Loading ticks...")
|
||||
x, y = tdl(datetime.datetime(2009,6,3))
|
||||
LOG.debug("Ticks loaded.")
|
||||
|
||||
fig = plt.figure()
|
||||
ax1 = fig.add_subplot(311) # ticks
|
||||
canvas = ax1.figure.canvas
|
||||
# ax2 = fig.add_subplot(312) # gearing
|
||||
# ax3 = fig.add_subplot(313) # cash
|
||||
|
||||
ax1.set_ylabel("ticks")
|
||||
# ax2.set_ylabel("gearing")
|
||||
# ax3.set_ylabel("cash")
|
||||
|
||||
xr = [x[0]] * 500
|
||||
yr = [y[0]] * 500
|
||||
|
||||
line, = ax1.plot_date(xr, yr, '-', xdate = True)
|
||||
major_fmt = mdates.DateFormatter('%H:%M:%S')
|
||||
ax1.xaxis.set_major_formatter(major_fmt)
|
||||
# ax1.axis('tight')
|
||||
|
||||
# format the coords message box
|
||||
def price(x): return '%1.2f'%x
|
||||
# ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')
|
||||
ax1.format_xdata = mdates.DateFormatter('%H:%M:%S')
|
||||
ax1.format_ydata = price
|
||||
ax1.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()
|
||||
|
||||
background = canvas.copy_from_bbox(ax1.bbox)
|
||||
|
||||
def animate():
|
||||
w = 1000
|
||||
bias = 10
|
||||
|
||||
ymin = min(y[0:100])
|
||||
ymax = max(y[0:100])
|
||||
|
||||
low = ymin - bias
|
||||
high = ymax + bias
|
||||
trend = 0
|
||||
|
||||
for i in range(0, len(x)-w):
|
||||
# restore the clean slate background
|
||||
canvas.restore_region(background)
|
||||
# prepare timeline window
|
||||
xr = x[i:i+w]
|
||||
yr = y[i:i+w]
|
||||
# update line
|
||||
line.set_xdata(xr)
|
||||
line.set_ydata(yr)
|
||||
# determine y axis
|
||||
if yr[-1] > ymax:
|
||||
ymax = yr[-1]
|
||||
if ymax - 50 < min(yr):
|
||||
ymin = ymax - 50
|
||||
if yr[-1] < ymin:
|
||||
ymin = yr[-1]
|
||||
if ymin + 50 > max(yr):
|
||||
ymax = ymin + 50
|
||||
ax1.axis([xr[0], xr[-1], ymin, ymax])
|
||||
# check low high and annotate
|
||||
if i > 0 and i % 150 == 0:
|
||||
ax1.annotate('low/high',
|
||||
xy = (xr[-10], yr[-10]),
|
||||
xytext = (xr[-5], yr[-10] + 10),
|
||||
arrowprops = dict(facecolor = 'black',
|
||||
frac = 0.2,
|
||||
headwidth = 10,
|
||||
linewidth = 0.1,
|
||||
shrink = 0.05))
|
||||
|
||||
# just draw the animated artist
|
||||
# FIXME: redraw all items in graph
|
||||
ax1.draw_artist(line)
|
||||
# just redraw the axes rectangle
|
||||
canvas.blit(ax1.bbox)
|
||||
|
||||
|
||||
|
||||
root = fig.canvas.manager.window
|
||||
root.after(100, animate)
|
||||
plt.show()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
163
mpl/mpl-draw.py
Normal file
163
mpl/mpl-draw.py
Normal file
@@ -0,0 +1,163 @@
|
||||
# Copyright (c) 2008 Andreas Balogh
|
||||
# See LICENSE for details.
|
||||
|
||||
""" animated drawing of ticks
|
||||
|
||||
using draw method
|
||||
"""
|
||||
|
||||
# system imports
|
||||
|
||||
import Tkinter as tk
|
||||
import datetime
|
||||
import os
|
||||
import re
|
||||
import logging
|
||||
import sys
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
from matplotlib.dates import date2num
|
||||
import matplotlib.dates as mdates
|
||||
import numpy as np
|
||||
|
||||
# local imports
|
||||
|
||||
# constants
|
||||
|
||||
# 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("d:\\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 main():
|
||||
LOG.debug("Loading ticks...")
|
||||
x, y = tdl(datetime.datetime(2009,6,3))
|
||||
LOG.debug("Ticks loaded.")
|
||||
|
||||
fig = plt.figure()
|
||||
ax1 = fig.add_subplot(311) # ticks
|
||||
# ax2 = fig.add_subplot(312) # gearing
|
||||
# ax3 = fig.add_subplot(313) # cash
|
||||
|
||||
ax1.set_ylabel("ticks")
|
||||
# ax2.set_ylabel("gearing")
|
||||
# ax3.set_ylabel("cash")
|
||||
|
||||
xr = [x[0]] * 500
|
||||
yr = [y[0]] * 500
|
||||
|
||||
line, = ax1.plot_date(xr, yr, '-', xdate = True)
|
||||
major_fmt = mdates.DateFormatter('%H:%M:%S')
|
||||
ax1.xaxis.set_major_formatter(major_fmt)
|
||||
# ax1.axis('tight')
|
||||
|
||||
# format the coords message box
|
||||
def price(x): return '%1.2f'%x
|
||||
# ax.format_xdata = mdates.DateFormatter('%Y-%m-%d')
|
||||
ax1.format_xdata = mdates.DateFormatter('%H:%M:%S')
|
||||
ax1.format_ydata = price
|
||||
ax1.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()
|
||||
|
||||
def animate():
|
||||
w = 1000
|
||||
bias = 10
|
||||
|
||||
ymin = min(y[0:100])
|
||||
ymax = max(y[0:100])
|
||||
|
||||
low = ymin - bias
|
||||
high = ymax + bias
|
||||
trend = 0
|
||||
|
||||
for i in range(0, len(x)-w):
|
||||
# prepare timeline window
|
||||
xr = x[i:i+w]
|
||||
yr = y[i:i+w]
|
||||
# update line
|
||||
line.set_xdata(xr)
|
||||
line.set_ydata(yr)
|
||||
# determine y axis
|
||||
if yr[-1] > ymax:
|
||||
ymax = yr[-1]
|
||||
if ymax - 50 < min(yr):
|
||||
ymin = ymax - 50
|
||||
if yr[-1] < ymin:
|
||||
ymin = yr[-1]
|
||||
if ymin + 50 > max(yr):
|
||||
ymax = ymin + 50
|
||||
ax1.axis([xr[0], xr[-1], ymin, ymax])
|
||||
# check low high and annotate
|
||||
if i > 0 and i % 150 == 0:
|
||||
ax1.annotate('low/high',
|
||||
xy = (xr[-10], yr[-10]),
|
||||
xytext = (xr[-5], yr[-10] + 10),
|
||||
arrowprops = dict(facecolor = 'black',
|
||||
frac = 0.2,
|
||||
headwidth = 10,
|
||||
linewidth = 0.1,
|
||||
shrink = 0.05))
|
||||
|
||||
fig.canvas.draw()
|
||||
|
||||
root = fig.canvas.manager.window
|
||||
root.after(100, animate)
|
||||
plt.show()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
330
mpl/mpl-embedded.py
Normal file
330
mpl/mpl-embedded.py
Normal file
@@ -0,0 +1,330 @@
|
||||
# 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 sys
|
||||
import warnings
|
||||
|
||||
import Tkinter as Tk
|
||||
import numpy as np
|
||||
|
||||
import matplotlib
|
||||
matplotlib.use('TkAgg')
|
||||
|
||||
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg, NavigationToolbar2TkAgg
|
||||
from matplotlib.figure import Figure
|
||||
import matplotlib.pyplot as plt
|
||||
import matplotlib.dates as mdates
|
||||
from matplotlib.dates import date2num
|
||||
|
||||
# local imports
|
||||
|
||||
# constants
|
||||
|
||||
# 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("d:\\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)
|
||||
|
||||
|
||||
class Mmh:
|
||||
"""Time series max & min detector."""
|
||||
def __init__(self, bias):
|
||||
assert(bias > 0)
|
||||
self.bias = bias
|
||||
self.trend = None
|
||||
self.mm0 = None
|
||||
self.mms = [ ]
|
||||
self.mins = [ ]
|
||||
self.maxs = [ ]
|
||||
|
||||
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.mm0 is None:
|
||||
# initalise water mark
|
||||
self.mm0 = tick
|
||||
res = self.mm0
|
||||
self.mins = [(n, cdt, last - 1)]
|
||||
self.maxs = [(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.mms.insert(0, self.mm0)
|
||||
self.maxs.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.mms.insert(0, self.mm0)
|
||||
self.mins.append(self.mm0)
|
||||
res = self.mm0
|
||||
# revert trend & water mark
|
||||
self.mm0 = tick
|
||||
self.trend = +1
|
||||
return res
|
||||
|
||||
|
||||
class Main:
|
||||
def __init__(self):
|
||||
LOG.debug("Loading ticks...")
|
||||
self.x, self.y = tdl(datetime.datetime(2009,6,3))
|
||||
LOG.debug("Ticks loaded.")
|
||||
self.mmh = Mmh(10)
|
||||
|
||||
self.root = Tk.Tk()
|
||||
self.root.wm_title("Embedding in TK")
|
||||
|
||||
fig = plt.figure()
|
||||
self.ax1 = fig.add_subplot(111) # ticks
|
||||
# ax2 = fig.add_subplot(312) # gearing
|
||||
# ax3 = fig.add_subplot(313) # cash
|
||||
|
||||
self.ax1.set_ylabel("ticks")
|
||||
# ax2.set_ylabel("gearing")
|
||||
# ax3.set_ylabel("cash")
|
||||
|
||||
self.w = 1000
|
||||
self.bias = 10
|
||||
|
||||
xr = self.x[0:self.w]
|
||||
yr = self.y[0:self.w]
|
||||
|
||||
fit = np.average(yr)
|
||||
|
||||
self.tl, = self.ax1.plot_date(xr, yr, '-')
|
||||
self.fl, = self.ax1.plot_date(xr, (fit, ) * self.w, 'k--')
|
||||
self.mh, = self.ax1.plot_date(xr, (yr[0], ) * self.w, 'g:')
|
||||
self.ml, = self.ax1.plot_date(xr, (yr[0], ) * self.w, 'r:')
|
||||
major_fmt = mdates.DateFormatter('%H:%M:%S')
|
||||
self.ax1.xaxis.set_major_formatter(major_fmt)
|
||||
|
||||
self.ax1.format_xdata = mdates.DateFormatter('%H:%M:%S')
|
||||
self.ax1.format_ydata = lambda x: '%1.2f'%x
|
||||
self.ax1.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()
|
||||
self.ax1.axis([xr[0], xr[-1]+180./86400., min(yr), max(yr)])
|
||||
|
||||
self.canvas = FigureCanvasTkAgg(fig, master=self.root)
|
||||
self.canvas.draw()
|
||||
self.canvas.get_tk_widget().pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
|
||||
|
||||
# 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)
|
||||
bu1.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_start(self):
|
||||
warnings.simplefilter("default", np.RankWarning)
|
||||
|
||||
self.ymin = min(self.y[0:self.w])
|
||||
self.ymax = max(self.y[0:self.w])
|
||||
|
||||
self.low = self.ymin - self.bias
|
||||
self.high = self.ymax + self.bias
|
||||
self.trend = 0
|
||||
self.i = 0
|
||||
for n in range(0, self.w):
|
||||
self.mark_low_high(n)
|
||||
self.root.after(500, self.animate_step)
|
||||
|
||||
def animate_step(self):
|
||||
# prepare timeline window
|
||||
xr = np.array( self.x[self.i:self.i+self.w] )
|
||||
yr = np.array( self.y[self.i:self.i+self.w] )
|
||||
# update line
|
||||
self.tl.set_xdata(xr)
|
||||
self.tl.set_ydata(yr)
|
||||
# determine y axis
|
||||
if yr[-1] > self.ymax:
|
||||
self.ymax = yr[-1]
|
||||
if self.ymax - 50 < min(yr):
|
||||
self.ymin = self.ymax - 50
|
||||
if yr[-1] < self.ymin:
|
||||
self.ymin = yr[-1]
|
||||
if self.ymin + 50 > max(yr):
|
||||
self.ymax = self.ymin + 50
|
||||
# check self.low self.high and annotate
|
||||
fwd = 2
|
||||
for n in range(self.i+self.w, self.i+self.w+fwd):
|
||||
self.mark_low_high(n)
|
||||
self.i += fwd
|
||||
# build polynomial fit
|
||||
mms = self.mmh.mms
|
||||
if len(mms) > 1:
|
||||
# mx = [ (x - int(x)) * 86400 for n, x, y in mms[:4] ]
|
||||
# my = [ y for n, x, y in mms[:4] ]
|
||||
mx = [ (x - int(x)) * 86400 for x in xr ]
|
||||
my = yr
|
||||
xre = [ xr[-1] + x/86400. for x in range(1, 181)]
|
||||
xr2 = np.append(xr, xre)
|
||||
# print "mx: ", mx
|
||||
# print "my: ", my
|
||||
polyval = np.polyfit(mx, my, 30)
|
||||
# print "poly1d: ", polyval
|
||||
intx = np.array(xr, dtype = int)
|
||||
sodx = xr - intx
|
||||
sodx *= 86400
|
||||
s2x = np.append(sodx, range(sodx[-1], sodx[-1]+180))
|
||||
fit = np.polyval(polyval, s2x)
|
||||
self.fl.set_xdata(xr2)
|
||||
self.fl.set_ydata(fit)
|
||||
maxs = self.mmh.maxs
|
||||
if len(maxs) > 1:
|
||||
n, x1, y1 = maxs[-2]
|
||||
n, x2, y2 = maxs[-1]
|
||||
x3 = xr[-1]
|
||||
polyfit = np.polyfit((x1, x2), (y1, y2), 1)
|
||||
y3 = np.polyval(polyfit, x3)
|
||||
self.mh.set_data((x1, x2, x3), (y1, y2, y3))
|
||||
mins = self.mmh.mins
|
||||
if len(mins) > 1:
|
||||
n, x1, y1 = mins[-2]
|
||||
n, x2, y2 = mins[-1]
|
||||
x3 = xr[-1]
|
||||
polyfit = np.polyfit((x1, x2), (y1, y2), 1)
|
||||
y3 = np.polyval(polyfit, x3)
|
||||
self.ml.set_data((x1, x2, x3), (y1, y2, y3))
|
||||
# draw
|
||||
self.ax1.axis([xr[0], xr[-1]+180./86400., self.ymin, self.ymax])
|
||||
self.canvas.draw()
|
||||
if self.i < len(self.x)-self.w-1:
|
||||
self.after_id = self.root.after(10, self.animate_step)
|
||||
|
||||
def build_fit(self):
|
||||
pass
|
||||
|
||||
def mark_low_high(self, n):
|
||||
x = self.x
|
||||
y = self.y
|
||||
rc = self.mmh((n, x[n], y[n]))
|
||||
if rc:
|
||||
nlh, xlh, ylh = rc
|
||||
if self.mmh.trend > 0:
|
||||
# low
|
||||
self.ax1.annotate('low',
|
||||
xy = (x[nlh], y[nlh]),
|
||||
xytext = (x[n], y[nlh]),
|
||||
arrowprops = dict(facecolor = 'red',
|
||||
shrink = 0.05))
|
||||
# an.set_annotation_clip(True)
|
||||
elif self.mmh.trend < 0:
|
||||
# high
|
||||
self.ax1.annotate('high',
|
||||
xy = (x[nlh], y[nlh]),
|
||||
xytext = (x[n], y[nlh]),
|
||||
arrowprops = dict(facecolor = 'green',
|
||||
shrink = 0.05))
|
||||
# an.set_annotation_clip(True)
|
||||
|
||||
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_step)
|
||||
|
||||
def run(self):
|
||||
self.root.after(500, self.animate_start)
|
||||
self.root.mainloop()
|
||||
self.root.destroy()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
app = Main()
|
||||
app.run()
|
||||
Reference in New Issue
Block a user