def segtrends(x, segments=2, charts=True, window=7):
"""
Turn minitrends to iterative process more easily adaptable to
implementation in simple trading systems; allows backtesting functionality.
:param x: One-dimensional data set
:param window: How long the trendlines should be. If window < 1, then it
will be taken as a percentage of the size of the data
:param charts: Boolean value saying whether to print chart to screen
"""
import numpy as np
y = np.array(x)
n=len(y)
movy = movingaverage(y, window)
# Implement trendlines and Find the indexes of these maxima in the data
segments = int(segments)
maxima = np.ones(segments)
minima = np.ones(segments)
x_maxima = np.ones(segments)
x_minima = np.ones(segments)
segsize = int(len(y)/segments)
for i in range(1, segments+1):
ind2 = i*segsize
ind1 = ind2 - segsize
seg = y[ind1:ind2]
maxima[i-1] = max(seg)
minima[i-1] = min(seg)
x_maxima[i-1] = ind1 + (np.where(seg == maxima[i-1])[0][0])
x_minima[i-1] = ind1 + (np.where(seg == minima[i-1])[0][0])
if charts:
import matplotlib.pyplot as plt
plt.plot(y)
plt.grid(True)
for i in range(0, segments-1):
maxslope = (maxima[i+1] - maxima[i]) / (x_maxima[i+1] - x_maxima[i])
a_max = maxima[i] - (maxslope * x_maxima[i])
b_max = maxima[i] + (maxslope * (len(y) - x_maxima[i]))
maxline = np.linspace(a_max, b_max, len(y))
minslope = (minima[i+1] - minima[i]) / (x_minima[i+1] - x_minima[i])
a_min = minima[i] - (minslope * x_minima[i])
b_min = minima[i] + (minslope * (len(y) - x_minima[i]))
minline = np.linspace(a_min, b_min, len(y))
if charts:
#plt.plot(maxline, 'g')
#plt.plot(minline, 'r')
pass
if charts:
plt.plot(range(n), movy, 'b')
plt.plot(x_maxima, maxima, 'g')
plt.plot(x_minima, minima, 'r')
plt.show()
# OUTPUT
return x_maxima, maxima, x_minima, minima
def get_orders(cls, x, segments=2, window=7, charts=True, verbose=False):
''' generate orders from segtrends '''
from filter import movingaverage
from trendy import segtrends
x_maxima, maxima, x_minima, minima = segtrends(x, segments, window,
charts)
n = len(x)
y = np.array(x)
movy = movingaverage(y, window)
# generate order strategy
orders = np.zeros(n)
last_buy = y[0]
for i in range(1, n):
# get 2 latest support point y values prior to x
pmin = minima[np.where(x_minima <= i)]
pmax = maxima[np.where(x_maxima <= i)]
buy, last_buy = cls.get_order_from_trend(pmin, pmax, y[i], movy[i],
last_buy)
orders[i] = buy
if verbose:
print "orders", orders
return orders
def segtrends(x, segments=2, window=7, charts=False, title=None):
"""
Turn minitrends to iterative process more easily adaptable to
implementation in simple trading systems; allows backtesting functionality.
:param x: One-dimensional data set
:param window: How long the trendlines should be. If window < 1, then it
will be taken as a percentage of the size of the data
:param charts: Boolean value saying whether to print chart to screen
"""
import numpy as np
y = np.array(x)
n=len(y)
movy = movingaverage(y, window)
# Implement trendlines and Find the indexes of these maxima in the data
segments = int(segments)
maxima = np.ones(segments)
minima = np.ones(segments)
x_maxima = np.ones(segments)
x_minima = np.ones(segments)
segsize = int(len(y)/segments)
for i in range(1, segments+1):
ind2 = i*segsize
ind1 = ind2 - segsize
seg = y[ind1:ind2]
maxima[i-1] = max(seg)
minima[i-1] = min(seg)
x_maxima[i-1] = ind1 + (np.where(seg == maxima[i-1])[0][0])
x_minima[i-1] = ind1 + (np.where(seg == minima[i-1])[0][0])
for i in range(0, segments-1):
maxslope = (maxima[i+1] - maxima[i]) / (x_maxima[i+1] - x_maxima[i])
a_max = maxima[i] - (maxslope * x_maxima[i])
b_max = maxima[i] + (maxslope * (len(y) - x_maxima[i]))
maxline = np.linspace(a_max, b_max, len(y))
minslope = (minima[i+1] - minima[i]) / (x_minima[i+1] - x_minima[i])
a_min = minima[i] - (minslope * x_minima[i])
b_min = minima[i] + (minslope * (len(y) - x_minima[i]))
minline = np.linspace(a_min, b_min, len(y))
if charts:
import matplotlib.pyplot as plt
#plt.plot(range(n), movy, 'b')
plt.plot(range(n), x, 'k')
plt.plot(x_maxima, maxima, 'g')
plt.plot(x_minima, minima, 'r')
if title:
plt.title(title)
plt.show()
# OUTPUT
return x_maxima, maxima, x_minima, minima
def orders_from_trends(x, segments=2, charts=True, window=7, momentum=False):
''' generate orders from segtrends '''
x_maxima, maxima, x_minima, minima = segtrends(x, segments, charts, window)
n = len(x)
y = np.array(x)
movy = movingaverage(y, window)
# generate order strategy
orders = np.zeros(n)
last_buy = y[0]
last_sale = y[0]
for i in range(1, n):
# get 2 latest support point y values prior to x
pmin = list(minima[np.where(x_minima <= i)][-2:])
pmax = list(maxima[np.where(x_maxima <= i)][-2:])
# sell if support slop is negative
min_sell = True if ((len(pmin) == 2) and
(pmin[1] - pmin[0]) < 0) else False
max_sell = True if ((len(pmax) == 2) and
(pmax[1] - pmax[0]) < 0) else False
# if support down, sell
buy = -1 if (min_sell and max_sell) else 0
# buy only if lower the moving average else sale
buy = 1 if ((buy == 0) and (y[i] < movy[i])) else -1
# sell only if ...
buy = -1 if ((buy == -1) and y[i] > last_buy) else 1
buy_price_dec = y[i] < last_buy
sale_price_dec = y[i] < last_sale
orders[i] = buy
last_buy = y[i] if (buy == 1) else last_buy
last_sale = y[i] if (buy == -1) else last_sale
if momentum:
# add momentum for buy
if (buy == 1) and (orders[i - 1] >= 1):
#if buy_price_dec:
orders[i] = orders[i -
1] * 2 #round(math.log(2*orders[i-1])+1)
#else:
# orders[i]=max(1, round(orders[i-1]/2))
# add momentum for sale
elif (buy == -1) and (orders[i - 1] <= -1):
#if sale_price_dec:
orders[i] *= round(math.log(abs(orders[i - 1] * 2)) + 1)
#else:
# orders[i]=max(1, round(orders[i-1]/2))
# OUTPUT
return orders
def orders_from_trends(x, segments=2, charts=True, window=7, momentum=False):
""" generate orders from segtrends """
x_maxima, maxima, x_minima, minima = segtrends(x, segments, charts, window)
n = len(x)
y = np.array(x)
movy = movingaverage(y, window)
# generate order strategy
orders = np.zeros(n)
last_buy = y[0]
last_sale = y[0]
for i in range(1, n):
# get 2 latest support point y values prior to x
pmin = list(minima[np.where(x_minima <= i)][-2:])
pmax = list(maxima[np.where(x_maxima <= i)][-2:])
# sell if support slop is negative
min_sell = True if ((len(pmin) == 2) and (pmin[1] - pmin[0]) < 0) else False
max_sell = True if ((len(pmax) == 2) and (pmax[1] - pmax[0]) < 0) else False
# if support down, sell
buy = -1 if (min_sell and max_sell) else 0
# buy only if lower the moving average else sale
buy = 1 if ((buy == 0) and (y[i] < movy[i])) else -1
# sell only if ...
buy = -1 if ((buy == -1) and y[i] > last_buy) else 1
buy_price_dec = y[i] < last_buy
sale_price_dec = y[i] < last_sale
orders[i] = buy
last_buy = y[i] if (buy == 1) else last_buy
last_sale = y[i] if (buy == -1) else last_sale
if momentum:
# add momentum for buy
if (buy == 1) and (orders[i - 1] >= 1):
# if buy_price_dec:
orders[i] = orders[i - 1] * 2 # round(math.log(2*orders[i-1])+1)
# else:
# orders[i]=max(1, round(orders[i-1]/2))
# add momentum for sale
elif (buy == -1) and (orders[i - 1] <= -1):
# if sale_price_dec:
orders[i] *= round(math.log(abs(orders[i - 1] * 2)) + 1)
# else:
# orders[i]=max(1, round(orders[i-1]/2))
# OUTPUT
return orders
