trend channels to find fibionacci reference points
--HG-- branch : sandbox
This commit is contained in:
Andreas
@@ -169,28 +169,6 @@ class Lohi:
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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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@@ -222,13 +200,13 @@ class DelayedAcp:
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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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# initialise 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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# initialise 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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420
mpl/fibionacci3.py
Normal file
420
mpl/fibionacci3.py
Normal file
@@ -0,0 +1,420 @@
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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 warnings
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import math
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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
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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("c:\\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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def num2sod(x):
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frac, integ = math.modf(x)
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return frac * 86400
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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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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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# initialise 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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# initialise 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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# Acp markers
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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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lohis = self.mmh.lohis
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if len(lohis) >= 4:
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n0, x0, y0 = lohis[-4]
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n1, x1, y1 = lohis[-1]
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x2 = xr[-1]
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coefs = np.polyfit([num2sod(x) for n, x, y in lohis[-4:]], [y for n, x, y in lohis[-4:]], 1)
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self.fl.set_data((x0, x2), [np.polyval(coefs, num2sod(x)) for x in (x0, x2)])
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# width of trend channel
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mx = 0
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for n in range(n0, n1):
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mx = max(mx, math.fabs(np.polyval(coefs, num2sod(self.x[n])) - self.y[n]))
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a, b = coefs
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self.mh.set_data((x0, x2), [np.polyval((a, b+mx), num2sod(x)) for x in (x0, x2)])
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self.ml.set_data((x0, x2), [np.polyval((a, b-mx), num2sod(x)) for x in (x0, x2)])
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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)
|
||||
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()
|
||||
Reference in New Issue
Block a user