Overall Statistics
from Selection.EmaCrossUniverseSelectionModel import EmaCrossUniverseSelectionModel

# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
#
# you may not use this file except in compliance with the License.
#
# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and

import clr

from System import *
from QuantConnect import *
from QuantConnect.Algorithm import *
from QuantConnect.Indicators import *

### <summary>
### In this example we look at the canonical 15/30 day moving average cross. This algorithm
### will go long when the 15 crosses above the 30 and will liquidate when the 15 crosses
### back below the 30.
### </summary>
### <meta name="tag" content="indicators" />
### <meta name="tag" content="indicator classes" />
### <meta name="tag" content="moving average cross" />
### <meta name="tag" content="strategy example" />
class MovingAverageCrossAlgorithm(QCAlgorithm):

def Initialize(self):
'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''

self.SetStartDate(2008, 12, 20)    #Set Start Date
self.SetEndDate(2020, 4, 7)      #Set End Date
self.SetCash(100000)             #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data

# create a 14 day exponential moving average
self.fast = self.TEMA("SPY", Resolution.Tick)

# create a 200 day exponential moving average
self.slow = self.TEMA("SPY", 15, Resolution.Daily)

self.previous = None

def OnData(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''
# a couple things to notice in this method:
#  1. We never need to 'update' our indicators with the data, the engine takes care of this for us
#  2. We can use indicators directly in math expressions
#  3. We can easily plot many indicators at the same time

# wait for our slow ema to fully initialize
return

# only once per day
if self.previous is not None and self.previous.date() == self.Time.date():
return

# define a small tolerance on our checks to avoid bouncing
tolerance = 0.00015

if (self.Portfolio["SPXL"].Quantity) >= 0:
# if the fast is greater than the slow, we'll go long
if self.fast.Current.Value > self.slow.Current.Value * (1 + tolerance):
self.Log("SELL  >> {0}".format(self.Securities["SPXS"].Price))
self.Liquidate("SPXS")
self.SetHoldings("SPXL", 0.95)

if (self.Portfolio["SPXS"].Quantity) >= 0:
# if the slow is greater than the fast, we'll go short
if self.fast.Current.Value * (1 + tolerance) < self.slow.Current.Value:
self.Log("SELL  >> {0}".format(self.Securities["SPXL"].Price))
self.previous = self.Time