from collections import deque
import math
import talib
#no slippage / unfilled orders make testing annoying / volume of some ETFs not high enough?
set_slippage(slippage.FixedSlippage(spread=0.00))
'''
TODO v3
- Experiment use APR instead of ATR to prioritize stocks
- Apply a filter, either using max/min or atr over a longer period
- Bring the turtle back to life
- Widen the stop
- Scale out, not in
- Reduce sector risk
- Add markets (prioritize by volatility, manage drag-down)
- Add filter
- Factor in broker costs(?) and slippage
ROADMAP
* Adapt algo for futures trading with margin and leverage
* Apply genetic algo or some other machine learning technique to tweak APR trendwatching params, params: limit, atr days
* Implement s2 as well, or some trailing algo that expands on bigger trends (KISS?)
'''
def initialize(context):
context.securities = [
#BOND
sid(23921), #20 year treasury
sid(23870), #7-10 year treasury
#CURRENCY
sid(32304), #canada
sid(33334), #japan
sid(27894), #euro
sid(32307), #aussie
#NY COFFEE COKE SUGAR
sid(36468), #coffee
sid(41318), #cocoa
sid(36462), #sugar
sid(36466), #cotton
#COMEX
sid(26807), #gold
sid(28368), #silver
sid(34926), #copper
#NYME
sid(28320), #us oil
sid(33697), #us nat gas
]
schedule_function(rebalance, date_rules.every_day())
context.atr_padding = 14
# ratio
divider = 2.0
# entry / exit
context.days_go_long = 120
context.days_sell_short = 90
# piramiding / profit
context.days_exit_short = int(context.days_go_long / divider)
context.days_exit_long = int(context.days_sell_short / divider)
# risk exposure
context.s1_unit_limit = 1000
def get_account_size(context):
return context.portfolio.cash + context.portfolio.positions_value
def rebalance(context, data):
hist_with_padding = data.history(context.securities, ['high', 'low', 'close'], context.days_go_long + context.atr_padding, '1d')
# adjust for calculating with padding
hist_s1 = hist_with_padding[-context.days_go_long:]
# TODO sort security by volatility - also test results
# sorted(list_of_objects, key=lambda obj: obj.method_name())
# for each security in the list
for security in context.securities:
account_size = get_account_size(context)
price = data.current(security, 'price')
position = context.portfolio.positions[security].amount
# x day ATR
atr_s1 = talib.ATR(hist_s1['high'][security],
hist_s1['low'][security],
hist_s1['close'][security],
timeperiod=context.days_go_long)[-1]
# get x day ATR
N = abs(atr_s1)
# buy and stop units
one_percent = account_size * .01
trade_amount = math.floor(one_percent/(N * price)) # unit
# entry
# go long s1 (because Donchian breakout)
high_x_s1 = max(hist_s1['high'][security][0:-1]) # the last x-1 days, where x = days
if price > high_x_s1 and position is 0: # and filter_small_atr > filter_large_atr:
try:
order(security, trade_amount)
print("GO LONG %s / price %s / past high %s / n %s / orders %s" % (security, price, high_x_s1, N, trade_amount))
except ValueError:
# there are days when the adj close is NaN, this buggers up N
print "error: trade_amount = math.floor(%s / (%s * %s))" % (one_percent, N, price)
# total complete exit
# sell short s1
low_x_s1 = min(hist_s1['low'][security][0:-1])
if price < low_x_s1 and position > 0: # and filter_small_atr < filter_large_atr:
try:
order(security, -position)
print("SELL SHORT %s / price %s / past low %s / n %s / amount %s" % (security, price, low_x_s1, N, -trade_amount))
except ValueError:
print "error: trade_amount = math.floor(%s / (%s * %s))" % (one_percent, N, price)
# exit long when price drops below a y day low, where x > y (pocket profit)
low_y_s1 = min(hist_s1['low'][security][-context.days_exit_long:])
if price < low_y_s1 and position > 0:
try:
order(security, -trade_amount)
print("EXIT LONG %s / price %s / past low %s / n %s / amount %s" % (security, price, low_y_s1, N, -trade_amount))
except ValueError:
print "error: trade_amount = math.floor(%s / (%s * %s))" % (one_percent, N, price)
# exit short when price exceeds a y day high, where x > y (buy)
high_y_s1 = max(hist_s1['high'][security][-context.days_exit_short:])
limit = context.s1_unit_limit * trade_amount
if price < high_y_s1 and position > 0 and trade_amount and position < limit:
try:
order(security, trade_amount)
print("EXIT LONG %s / price %s / past low %s / n %s / amount %s" % (security, price, low_y_s1, N, -trade_amount))
except ValueError:
print "error: trade_amount = math.floor(%s / (%s * %s))" % (one_percent, N, price)
