def get_order(cls,
data,
segments=2,
window=7,
writer=None,
charts=False,
verbose=False):
''' generate orders from segtrends '''
price = data[cls.field]
x_maxima, maxima, x_minima, minima = segtrends(price,
segments,
window,
charts=charts)
if writer or cls.predict:
features = cls.get_order_features_from_trend(
segments, x_maxima, maxima, x_minima, minima)
vol_pct_change = data['Volume'][-(window +
1):].pct_change()[-window:]
last = data[cls.field][-1]
roll_mean_var = (pd.rolling_mean(data[cls.field][-window:],
window)[-1] - last) / last
roll_median_var = (pd.rolling_median(data[cls.field][-window:],
window)[-1] - last) / last
for add in (vol_pct_change, roll_mean_var, roll_median_var):
features = np.append(features, add)
if writer:
writer.writerow(features)
if cls.predict:
order = -1 if cls.predict([features]) == 0 else 1
return order
else:
return cls.get_order_from_trend(minima, maxima, verbose)
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 get_trendlines(df, window=1 / 3.0, segments=4, charts=False):
#gtrends, maxslope, minslope = trdy.gentrends(df, window=window, charts=charts)
# Modifying the number of segments in trdy.segtrends, modifies the number of S/R lines returned
strends = trdy.segtrends(df, segments=segments, charts=charts)
#trdy.minitrends(df.values)
#strends.append(gtrends)
points = trdy.iterlines(df, window=20, charts=charts)
return strends, points
def orders_from_trends(cls, x, segments=2, charts=True, window=7,
sell_momentum=no_momentum,
buy_momentum=no_momentum,
title=None):
''' generate orders from segtrends '''
x_maxima, maxima, x_minima, minima = segtrends(x, segments, charts, window, title)
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 ... # MUCH BETTER WITHOUT IT
#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
# add momentum for buy
if buy_momentum and (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)
orders[i]=buy_momentum(orders[i-1])#round(math.log(2*orders[i-1])+1)
#else:
# orders[i]=max(1, round(orders[i-1]/2))
# add momentum for sale
elif sell_momentum and (buy==-1) and (orders[i-1]<=-1):
#if sale_price_dec:
#orders[i]*=round(math.log(abs(orders[i-1]*2))+1)
orders[i]=-sell_momentum(orders[i-1])
#else:
# orders[i]=max(1, round(orders[i-1]/2))
# ensure no order are taken at the begining
for i in range(window):
orders[i]=0
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
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
# Download Apple price history and save adjusted close prices to numpy array
import pandas.io.data as pd
x = pd.DataReader("AAPL", "yahoo")['Adj Close']
# Make some trendlines
import trendy
# Generate general support/resistance trendlines and show the chart
# winow < 1 is considered a fraction of the length of the data set
trendy.gentrends(x, window = 1.0/3, charts = True)
# Generate a series of support/resistance lines by segmenting the price history
trendy.segtrends(x, segments = 2, charts = True) # equivalent to gentrends with window of 1/2
trendy.segtrends(x, segments = 5, charts = True) # plots several S/R lines
# Generate smaller support/resistance trendlines to frame price over smaller periods
trendy.minitrends(x, window = 30, charts = True)
# Iteratively generate trading signals based on maxima/minima in given window
trendy.iterlines(x, window = 30, charts = True) # buy at green dots, sell at red dots
if (r_1 == (n / 2)) and (r_2 == (n / 2)):
resistance.append(ltp[i + ((n / 2) - 1)])
# local minima detection
if (s_1 == (n / 2)) and (s_2 == (n / 2)):
support.append(ltp[i + ((n / 2) - 1)])
return support, resistance
###############################################################################
# test supres function
npArray = df['close'].as_matrix()
support, resistance = supres(npArray, 5)
###############################################################################
# test trendy
close = df['close']
close.index = df['date']
cutoff = '2000-08-01'
cut = close[close.index > cutoff]
trendy.gentrends(cut, window=1. / 3, charts=True)
trendy.segtrends(cut, segments=3, charts=True)
trendy.minitrends(cut, window=30, charts=True)
trendy.iterlines(cut, window=30, charts=True)
