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checkin after svn update

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branch : sandbox
This commit is contained in:
Andreas
2009-10-03 09:51:09 +00:00
parent d4d45b00c7
commit bb3de1e8c3
3 changed files with 655 additions and 9 deletions
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40
mpl/namedtuple.py Normal file
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@@ -0,0 +1,40 @@
# http://code.activestate.com/recipes/500261/
from operator import itemgetter
import sys
def __from_iterable__(cls,arg):
return cls.__new__(cls,*arg)
def NamedTuple(typename, field_names):
if isinstance(field_names, str):
field_names = field_names.split()
nargs = len(field_names)
def __new__(cls, *args, **kwds):
if (len(args) == 1) and (getattr(args[0], '__iter__', False)):
args = tuple(name for name in args[0])
if kwds:
try:
args += tuple(kwds[name] for name in field_names[len(args):])
except KeyError, name:
raise TypeError('%s missing required argument: %s' % (typename, name))
if len(args) != nargs:
raise TypeError('%s takes exactly %d arguments (%d given)' % (typename, nargs, len(args)))
return tuple.__new__(cls, args)
repr_template = '%s(%s)' % (typename, ', '.join('%s=%%r' % name for name in field_names))
m = dict(vars(tuple)) # pre-lookup superclass methods (for faster lookup)
m.update(__doc__= '%s(%s)' % (typename, ', '.join(field_names)),
__slots__ = (), # no per-instance dict (so instances are same size as tuples)
__new__ = __new__,
__repr__ = lambda self, _format=repr_template.__mod__: _format(self),
__module__ = sys._getframe(1).f_globals['__name__'],
__field_names__ = tuple(field_names),
__from_iterable__=classmethod(__from_iterable__),
)
m.update((name, property(itemgetter(index))) for index, name in enumerate(field_names))
return type(typename, (tuple,), m)

20
mpl/sw-trend1.py
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@@ -362,12 +362,12 @@ class Main:
# create plot
fig = plt.figure()
self.ax1 = fig.add_subplot(311) # ticks
self.ax2 = fig.add_subplot(312) # slope of line segement
self.ax3 = fig.add_subplot(313) # moving average (10min)
self.ax1 = fig.add_subplot(211) # ticks
# self.ax2 = fig.add_subplot(312) # slope of line segement
self.ax3 = fig.add_subplot(212) # moving average (10min)
self.ax1.set_ylabel("ticks")
self.ax2.set_ylabel("slope")
# self.ax2.set_ylabel("slope")
self.ax3.set_ylabel("gearing")
major_fmt = mdates.DateFormatter('%H:%M:%S')
@@ -378,12 +378,14 @@ class Main:
self.ax1.format_ydata = lambda x: '%1.2f' % x
self.ax1.grid(True)
"""
self.ax2.xaxis.set_major_formatter(major_fmt)
self.ax2.xaxis.set_major_locator(mdates.MinuteLocator(byminute = range(0, 60, 10)))
self.ax2.xaxis.set_minor_locator(mdates.MinuteLocator())
self.ax2.format_xdata = major_fmt
self.ax2.format_ydata = lambda x: '%1.2f' % x
self.ax2.grid(True)
"""
self.ax3.xaxis.set_major_formatter(major_fmt)
self.ax3.xaxis.set_major_locator(mdates.MinuteLocator(byminute = range(0, 60, 10)))
@@ -398,7 +400,7 @@ class Main:
# create artists
LOG.debug("Loading ticks...")
self.xs, self.ys, self.vs = tdl(datetime.datetime(2009, 7, 2))
self.xs, self.ys, self.vs = tdl(datetime.datetime(2009, 7, 1))
LOG.debug("Ticks loaded.")
lows, highs = find_lows_highs(self.xs, self.ys)
self.mas = self.ys[:]
@@ -406,7 +408,7 @@ class Main:
self.gs = [ 0 ] * len(self.xs)
self.mmh = TimedLohi(5)
self.osw = SlidingWindow(5)
self.osw = SlidingWindow(2)
self.w0 = 0
self.wd = 2000
@@ -426,7 +428,7 @@ class Main:
self.dl, = self.ax1.plot_date(xr, vr, 'g-')
# slope subplot
self.sl, = self.ax2.plot_date(xr, sr, '-')
# self.sl, = self.ax2.plot_date(xr, sr, '-')
# gearing subplot
self.gl, = self.ax3.plot_date(xr, gr, '-')
@@ -468,7 +470,7 @@ class Main:
# update tick line
self.tl.set_data(xr, yr)
# update segment slope
self.sl.set_data(xr, sr)
# self.sl.set_data(xr, sr)
# update volume line
self.dl.set_data(xr, vr)
# gearing line
@@ -491,7 +493,7 @@ class Main:
self.yhigh = y
self.ylow = self.yhigh - bias
self.ax1.axis([xr[0], xr[-1], self.ylow, self.yhigh])
self.ax2.axis([xr[0], xr[-1], -5, +5])
# self.ax2.axis([xr[0], xr[-1], -5, +5])
self.ax3.axis([xr[0], xr[-1], -50, +50])
def tick_window(self, w0, wd = 1000):

604
mpl/sw-trend2.py Normal file
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@@ -0,0 +1,604 @@
# Copyright (c) 2009 Andreas Balogh
# See LICENSE for details.
"""
Online sliding window with trend analysis
1. segment tick data with a sliding window alogrithm
2. recognise low/high points by comparing slope information
3. recognise trend by observing low/high point difference
"""
# system imports
import datetime
import os
import re
import logging
import warnings
import math
import Tkinter as Tk
import numpy as np
import matplotlib as mpl
mpl.use('TkAgg')
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
from matplotlib.dates import date2num
# local imports
from namedtuple import NamedTuple
from globals import *
# constants
ONE_MINUTE = 60. / 86400.
LOW, NONE, HIGH = range(-1, 2)
Trend = NamedTuple('Trend', 'n x y')
# globals
LOG = logging.getLogger()
logging.basicConfig(level=logging.DEBUG,
format='%(asctime)s.%(msecs)03i %(levelname).4s %(process)d:%(thread)d %(message)s',
datefmt='%H:%M:%S')
MDF_REO = re.compile("(..):(..):(..)\.*(\d+)*")
def tdl(tick_date):
""" returns a list of tick tuples (cdt, last) for specified day """
fiid = "846900"
year = tick_date.strftime("%Y")
yyyymmdd = tick_date.strftime("%Y%m%d")
filename = "%s.csv" % (fiid)
filepath = os.path.join(RTTRD_VAR, "consors-mdf\\data", year, yyyymmdd, filename)
x = [ ]
y = [ ]
v = [ ]
fh = open(filepath, "r")
try:
prev_last = ""
for line in fh:
flds = line.split(",")
# determine file version
if flds[2] == "LAST":
last = float(flds[3])
vol = float(flds[4])
else:
last = float(flds[4])
vol = 0.0
# skip ticks with same last price
if prev_last == last:
continue
else:
prev_last = last
# parse time
mobj = MDF_REO.match(flds[0])
if mobj is None:
raise ValueError("no match for [%s]" % (flds[0],))
(hh, mm, ss, ms) = mobj.groups()
if ms:
c_time = datetime.time(int(hh), int(mm), int(ss), int(ms) * 1000)
else:
c_time = datetime.time(int(hh), int(mm), int(ss))
cdt = datetime.datetime.combine(tick_date, c_time)
x.append(date2num(cdt))
y.append(last)
v.append(vol)
finally:
fh.close()
# throw away first line of file (close price from previous day)
del x[0]
del y[0]
del v[0]
return (x, y, v)
def interpolate_line(xs, ys):
"""Fit a straight line y = bx + a to a set of points (x, y) """
# from two data points only!
x1, x2 = xs
y1, y2 = ys
try:
b = ( y2 - y1 ) / ( x2 - x1 )
except ZeroDivisionError:
print "interpolate_line: division by zero, ", x1, x2, y1, y2
b = 0.0
a = y1 - b * x1
return (b, a)
def num2sod(x):
frac, integ = math.modf(x)
return frac * 86400
class Bunch:
def __init__(self, **kwds):
self.__dict__.update(kwds)
class TimedLohi:
"""Time series online low and high detector.
Confirms low/high candidates after timeout.
Time dependent.
"""
def __init__(self, bias, timeout = ONE_MINUTE):
assert(bias > 0)
self.bias = bias
self.timeout = timeout
self.low0 = None
self.high0 = None
self.prev_lohi = NONE
self.lohis = [ ]
self.lows = [ ]
self.highs = [ ]
def __call__(self, tick):
"""Add extended tick to the max min parser.
@param tick: The value of the current tick.
@type tick: tuple(cdt, last)
@return: 1. Tick if new max min has been detected,
2. None otherwise.
"""
n, cdt, last = tick
res = None
# automatic initialisation
if self.low0 is None:
self.low0 = tick
self.lows.append((n, cdt, last - 1))
if self.high0 is None:
self.high0 = tick
self.highs.append((n, cdt, last + 1))
if last > self.high0[2]:
self.high0 = tick
if self.prev_lohi == NONE:
if self.high0[2] > self.low0[2] + self.bias:
res = self.high0
self.low0 = self.high0
self.lows.append(self.high0)
self.lohis.append(self.high0)
self.prev_lohi = HIGH
if last < self.low0[2]:
self.low0 = tick
if self.prev_lohi == NONE:
if self.low0[2] < self.high0[2] - self.bias:
res = self.low0
self.high0 = self.low0
self.lows.append(self.low0)
self.lohis.append(self.low0)
self.prev_lohi = LOW
if self.high0[1] < cdt - self.timeout and \
((self.prev_lohi == LOW and \
self.high0[2] > self.lows[-1][2] + self.bias) or
(self.prev_lohi == HIGH and \
self.high0[2] > self.highs[-1][2])):
res = self.high0
self.low0 = self.high0
self.highs.append(self.high0)
self.lohis.append(self.high0)
self.prev_lohi = HIGH
if self.low0[1] < cdt - self.timeout and \
((self.prev_lohi == LOW and \
self.low0[2] < self.lows[-1][2]) or
(self.prev_lohi == HIGH and \
self.low0[2] < self.highs[-1][2] - self.bias)):
res = self.low0
self.high0 = self.low0
self.lows.append(self.low0)
self.lohis.append(self.low0)
self.prev_lohi = LOW
if res:
return (self.prev_lohi, res)
else:
return None
def find_lows_highs(xs, ys):
dacp = DelayedAcp(10)
for tick in zip(range(len(xs)), xs, ys):
dacp(tick)
return dacp.lows, dacp.highs
class DelayedAcp:
"""Time series max & min detector."""
def __init__(self, bias):
assert(bias > 0)
self.bias = bias
self.trend = None
self.mm0 = None
self.lohis = [ ]
self.lows = [ ]
self.highs = [ ]
def __call__(self, tick):
"""Add extended tick to the max min parser.
@param tick: The value of the current tick.
@type tick: tuple(n, cdt, last)
@return: 1. Tick if new max min has been detected,
2. None otherwise.
"""
n, cdt, last = tick
res = None
# automatic initialisation
if self.mm0 is None:
# initialise water mark
self.mm0 = tick
res = self.mm0
self.lows = [(n, cdt, last - 1)]
self.highs = [(n, cdt, last + 1)]
else:
# initialise trend until price has changed
if self.trend is None or self.trend == 0:
self.trend = cmp(last, self.mm0[2])
# check for max
if self.trend > 0:
if last > self.mm0[2]:
self.mm0 = tick
if last < self.mm0[2] - self.bias:
self.lohis.append(self.mm0)
self.highs.append(self.mm0)
res = self.mm0
# revert trend & water mark
self.mm0 = tick
self.trend = -1
# check for min
if self.trend < 0:
if last < self.mm0[2]:
self.mm0 = tick
if last > self.mm0[2] + self.bias:
self.lohis.append(self.mm0)
self.lows.append(self.mm0)
res = self.mm0
# revert trend & water mark
self.mm0 = tick
self.trend = +1
return (cmp(self.trend, 0), res)
class SlidingWindow:
"""Douglas-Peucker algorithm."""
def __init__(self, bias):
assert(bias > 0)
self.bias = bias
self.xs = [ ]
self.ys = [ ]
self.segx = [ ]
self.segy = [ ]
self.types = [ ]
self.bs = [ ]
def __call__(self, tick):
"""Add extended tick to the max min parser.
@param tick: The value of the current tick.
@type tick: tuple(n, cdt, last)
@return: 1. Tick if new max min has been detected,
2. None otherwise.
"""
n, cdt, last = tick
max_distance = self.bias
rc = None
self.xs.append(cdt)
self.ys.append(last)
x0, y0 = (self.xs[0], self.ys[0])
x1, y1 = (self.xs[-1], self.ys[-1])
if n == 0:
self.segx.append(x0)
self.segy.append(y0)
if len(self.xs) < 2:
return None
# check distance
coefs = interpolate_line((x0, x1), (y0, y1))
ip_ys = np.polyval(coefs, self.xs)
d_ys = np.absolute(self.ys - ip_ys)
d_max = np.amax(d_ys)
if d_max > max_distance:
n = np.argmax(d_ys)
x2, y2 = (self.xs[n], self.ys[n])
self.segx.append(x2)
self.segy.append(y2)
segment_added = True
# store slope of segment
b0, a0 = interpolate_line((x0, x2), (y0, y2))
self.bs.append(b0)
# remove ticks of previous segment
del self.xs[0:n]
del self.ys[0:n]
# slope of current segment
x0, y0 = (self.xs[0], self.ys[0])
b1, a1 = interpolate_line((x0, x1), (y0, y1))
lohi = self.get_type(b0, b1)
rc = (x2, y2, lohi)
return (self.segx + [x1], self.segy + [y1], rc)
def get_type(self, b0, b1):
""" calculate gearing
y: previous slope, x: current slope
<0 ~0 >0
<0 L L L
~0 H 0 L
>0 H H H
"""
if b0 < -SMALL and b1 < -SMALL and b0 > b1:
lohi = "d+"
elif b0 < -SMALL and b1 < SMALL and b0 < b1:
lohi = "d-"
elif b0 < -SMALL and b1 > SMALL:
lohi = "L"
elif abs(b0) < SMALL and b1 < -SMALL:
lohi = "d+"
elif abs(b0) < SMALL and abs(b1) < SMALL:
lohi = "0"
elif abs(b0) < SMALL and b1 > SMALL:
lohi = "u+"
elif b0 > SMALL and b1 < -SMALL:
lohi = "H"
elif b0 > SMALL and b1 > -SMALL and b0 > b1:
lohi = "u-"
elif b0 > SMALL and b1 > SMALL and b0 < b1:
lohi = "u+"
else:
lohi = "?"
return lohi
SMALL = 1E-10
class Main:
def __init__(self):
warnings.simplefilter("default", np.RankWarning)
self.advance_count = 10
self.ylow = None
self.yhigh = None
self.trend_starts = None
self.segs = [ ]
self.root = Tk.Tk()
self.root.wm_title("Embedding in TK")
# create plot
fig = plt.figure()
self.ax1 = fig.add_subplot(211) # ticks
self.ax2 = fig.add_subplot(212) # moving average (10min)
self.ax1.set_ylabel("ticks")
self.ax2.set_ylabel("gearing")
major_fmt = mdates.DateFormatter('%H:%M:%S')
self.ax1.xaxis.set_major_formatter(major_fmt)
self.ax1.xaxis.set_major_locator(mdates.MinuteLocator(byminute = range(0, 60, 10)))
self.ax1.xaxis.set_minor_locator(mdates.MinuteLocator())
self.ax1.format_xdata = major_fmt
self.ax1.format_ydata = lambda x: '%1.2f' % x
self.ax1.grid(True)
self.ax2.xaxis.set_major_formatter(major_fmt)
self.ax2.xaxis.set_major_locator(mdates.MinuteLocator(byminute = range(0, 60, 10)))
self.ax2.xaxis.set_minor_locator(mdates.MinuteLocator())
self.ax2.format_xdata = major_fmt
self.ax2.format_ydata = lambda x: '%1.2f' % x
self.ax2.grid(True)
# rotates and right aligns the x labels, and moves the bottom of the
# axes up to make room for them
fig.autofmt_xdate()
# create artists
LOG.debug("Loading ticks...")
self.xs, self.ys, self.vs = tdl(datetime.datetime(2009, 7, 1))
LOG.debug("Ticks loaded.")
lows, highs = find_lows_highs(self.xs, self.ys)
self.mas = self.ys[:]
self.mmh = TimedLohi(5)
self.osw = SlidingWindow(2)
self.w0 = 0
self.wd = 2000
self.w_crs = 0
xr, yr, mar = self.tick_window(self.w0, self.wd)
self.gr = [0.0] * self.wd
# add artists to top subplot
# tick line and segments
self.tl, = self.ax1.plot_date(xr, yr, '-')
self.seg, = self.ax1.plot_date((xr[0], xr[1]), (yr[0], yr[1]), 'k-')
# Acp markers
self.him, = self.ax1.plot_date([x for n, x, y in lows], [y for n, x, y in lows], 'go')
self.lom, = self.ax1.plot_date([x for n, x, y in highs], [y for n, x, y in highs], 'ro')
# trend lines
self.trd, = self.ax1.plot_date(xr[0:1], yr[0:1], 'k--')
self.trh, = self.ax1.plot_date(xr[0:1], yr[0:1], 'k-')
self.trl, = self.ax1.plot_date(xr[0:1], yr[0:1], 'k-')
# add artists to bottom subplot
self.gl, = self.ax2.plot_date(xr, self.gr, '-')
self.set_axis(xr, yr)
# embed canvas in Tk
self.canvas = FigureCanvasTkAgg(fig, master=self.root)
self.canvas.draw()
self.canvas.get_tk_widget().pack(side=Tk.TOP, fill=Tk.BOTH, expand=Tk.TRUE)
# toolbar = NavigationToolbar2TkAgg( self.canvas, self.root )
# toolbar.update()
# self.canvas._tkself.canvas.pack(side=Tk.TOP, fill=Tk.BOTH, expand=1)
fr1 = Tk.Frame(master=self.root)
bu1 = Tk.Button(master=fr1, text='Quit', command=self.root.quit)
bu2 = Tk.Button(master=fr1, text='Stop', command=self.stop)
bu4 = Tk.Button(master=fr1, text='1x', command=self.times_one)
bu5 = Tk.Button(master=fr1, text='5x', command=self.times_five)
bu6 = Tk.Button(master=fr1, text='10x', command=self.times_ten)
bu1.pack(side=Tk.RIGHT, padx=5, pady=5)
bu6.pack(side=Tk.RIGHT, padx=5, pady=5)
bu5.pack(side=Tk.RIGHT, padx=5, pady=5)
bu4.pack(side=Tk.RIGHT, padx=5, pady=5)
bu2.pack(side=Tk.RIGHT, padx=5, pady=5)
fr1.pack(side=Tk.BOTTOM)
def animate(self):
self.w0 += self.advance_count
# prepare timeline window
while self.w_crs < self.w0 + self.wd:
self.ma(self.w_crs, 10)
self.fitter(self.w_crs)
self.w_crs += 1
xr, yr, mar = self.tick_window(self.w0, self.wd)
# update tick line
self.tl.set_data(xr, yr)
# gearing line
# self.gl.set_data(xr, gr)
# update axis
self.set_axis(xr, yr)
self.canvas.draw()
if self.w0 < len(self.xs) - self.wd - 1:
self.after_id = self.root.after(10, self.animate)
def set_axis(self, xr, yr, bias=50):
if self.ylow is None:
self.ylow = yr[0] - bias / 2
self.yhigh = yr[0] + bias / 2
for y in yr:
if y < self.ylow:
self.ylow = y
self.yhigh = self.ylow + bias
if y > self.yhigh:
self.yhigh = y
self.ylow = self.yhigh - bias
self.ax1.axis([xr[0], xr[-1], self.ylow, self.yhigh])
self.ax2.axis([xr[0], xr[-1], -50, +50])
def tick_window(self, w0, wd = 1000):
return (self.xs[w0:w0 + wd],
self.ys[w0:w0 + wd],
self.mas[w0:w0 + wd],
)
def ma(self, n0, min):
self.mas[n0] = np.average(self.ys[n0-min*60:n0])
def fitter(self, n0):
# find last low/high within t-1
# linear regression from t-5 to t-1
# linear regression within t-1
# visual inspection
# determine run-on low and highs
if self.trend_starts is None:
self.trend_starts = [Trend(n=n0, x=self.xs[n0], y=self.ys[n0])]
trend_start = self.trend_starts[-1]
# wait for 30 secs to stabilise
if trend_start.n + 30 > n0:
return
# fit trend
xr = self.xs[trend_start.n:n0]
yr = self.ys[trend_start.n:n0]
ps = np.polyfit(xr, yr, 1)
trend_xs = [xr[0], xr[-1]]
trend_ys = np.polyval(ps, trend_xs)
self.trd.set_data(trend_xs, trend_ys)
# fit counter trend
def mark_segments(self, n):
x = self.xs
y = self.ys
rc = self.osw((n, x[n], y[n]))
if rc is not None:
segx, segy, lohi = rc
self.seg.set_data(segx, segy)
if lohi is not None:
text = lohi[2]
if text == "u+":
fc = "blue"
dy = -15
elif text == "d+":
fc = "blue"
dy = +15
elif text == "H":
fc = "green"
dy = +15
elif text == "L":
fc = "red"
dy = -15
else:
fc = None
if fc:
self.ax1.annotate(text,
xy=(lohi[0], lohi[1]),
xytext=(segx[-1], segy[-2]+dy),
arrowprops=dict(facecolor=fc,
frac=0.3,
shrink=0.1))
def mark_low_high(self, n):
x = self.xs
y = self.ys
rc = self.mmh((n, x[n], y[n]))
if rc:
lohi, tick = rc
nlh, xlh, ylh = tick
if lohi < 0:
# low
self.ax1.annotate('low',
xy=(x[nlh], y[nlh]),
xytext=(x[n], y[nlh]),
arrowprops=dict(facecolor='red',
frac=0.3,
shrink=0.1))
elif lohi > 0:
# high
self.ax1.annotate('high',
xy=(x[nlh], y[nlh]),
xytext=(x[n], y[nlh]),
arrowprops=dict(facecolor='green',
frac=0.3,
shrink=0.1))
def stop(self):
if self.after_id:
self.root.after_cancel(self.after_id)
self.after_id = None
def resume(self):
if self.after_id is None:
self.after_id = self.root.after(10, self.animate)
def times_one(self):
self.advance_count = 1
self.resume()
def times_five(self):
self.advance_count = 5
self.resume()
def times_ten(self):
self.advance_count = 10
self.resume()
def run(self):
self.root.after(500, self.animate)
self.root.mainloop()
self.root.destroy()
if __name__ == "__main__":
app = Main()
app.run()