# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from AlgorithmImports import *

### <summary>
### Strategy example using a portfolio of ETF Global Rotation
### </summary>
### <meta name="tag" content="strategy example" />
### <meta name="tag" content="momentum" />
### <meta name="tag" content="using data" />

### <summary>
### Strategy example using a portfolio of ETF Global Rotation
### </summary>
### <meta name="tag" content="strategy example" />
### <meta name="tag" content="momentum" />
### <meta name="tag" content="using data" />
class ETFGlobalRotationAlgorithm(QCAlgorithm):

 def initialize(self):
 self.set_cash(25000)
 self.set_start_date(2007,1,1)
 self.last_rotation_time = datetime.min
 self.rotation_interval = timedelta(days=30)
 self.first = True

 # these are the growth symbols we'll rotate through
 growth_symbols =["MDY", # US S&P mid cap 400
 "IEV", # i_shares S&P europe 350
 "EEM", # i_shared MSCI emerging markets
 "ILF", # i_shares S&P latin america
 "EPP" ] # i_shared MSCI Pacific ex-Japan

 # these are the safety symbols we go to when things are looking bad for growth
 safety_symbols = ["EDV", "SHY"] # "EDV" Vangaurd TSY 25yr, "SHY" Barclays Low Duration TSY
 # we'll hold some computed data in these guys
 self.symbol_data = []
 for symbol in list(set(growth_symbols) | set(safety_symbols)):
 self.add_security(SecurityType.EQUITY, symbol, Resolution.MINUTE)
 self.one_month_performance = self.mom(symbol, 30, Resolution.DAILY)
 self.three_month_performance = self.mom(symbol, 90, Resolution.DAILY)
 self.symbol_data.append((symbol, self.one_month_performance, self.three_month_performance))

 def on_data(self, data):
 # the first time we come through here we'll need to do some things such as allocation
 # and initializing our symbol data

 if self.first:
 self.first = False
 self.last_rotation_time = self.time
 return
 delta = self.time - self.last_rotation_time
 if delta > self.rotation_interval:
 self.last_rotation_time = self.time

 ordered_obj_scores = sorted(self.symbol_data, key=lambda x: Score(x[1].current.value,x[2].current.value).objective_score(), reverse=True)
 for x in ordered_obj_scores:
 self.log(">>SCORE>>" + x[0] + ">>" + str(Score(x[1].current.value,x[2].current.value).objective_score()))
 # pick which one is best from growth and safety symbols
 best_growth = ordered_obj_scores[0]
 if Score(best_growth[1].current.value,best_growth[2].current.value).objective_score() > 0:
 if (self.portfolio[best_growth[0]].quantity == 0):
 self.log("PREBUY>>LIQUIDATE>>")
 self.liquidate()
 self.log(">>BUY>>" + str(best_growth[0]) + "@" + str(100 * best_growth[1].current.value))
 qty = self.portfolio.margin_remaining / self.securities[best_growth[0]].close
 self.market_order(best_growth[0], int(qty))
 else:
 # if no one has a good objective score then let's hold cash this month to be safe
 self.log(">>LIQUIDATE>>CASH")
 self.liquidate()

class Score(object):

 def __init__(self,one_month_performance_value,three_month_performance_value):
 self.one_month_performance = one_month_performance_value
 self.three_month_performance = three_month_performance_value

 def objective_score(self):
 weight1 = 100
 weight2 = 75
 return (weight1 * self.one_month_performance + weight2 * self.three_month_performance) / (weight1 + weight2)