Overall Statistics Total Trades11Average Win59.27%Average Loss-4.95%Compounding Annual Return197.965%Drawdown37.000%Expectancy9.385Net Profit615.719%Sharpe Ratio1.845Loss Rate20%Win Rate80%Profit-Loss Ratio11.98Alpha0.854Beta0.107Annual Standard Deviation0.468Annual Variance0.219Information Ratio1.627Tracking Error0.475Treynor Ratio8.101Total Fees\$690.24
```# 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 numpy as np
import decimal as d

### <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 ChannelsAlgorithm(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(2016, 01, 01)  #Set Start Date
self.SetEndDate(2017, 10, 19)    #Set End Date
self.SetCash(10000)             #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data
self.SetBrokerageModel(BrokerageName.GDAX)

# create a 15 day exponential moving average
#        self.fast = self.EMA("BTCUSD", 15, Resolution.Daily);

# create a 30 day exponential moving average
#        self.slow = self.EMA("BTCUSD", 70, Resolution.Daily);

# Create Channels
self.channel = self.DCH("BTCUSD",20,20,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;

holdings = self.Portfolio["BTCUSD"].Quantity

# we only want to go long if we're currently short or flat
if holdings <= 0:
# if the fast is greater than the slow, we'll go long

if float(self.Securities["BTCUSD"].Price) > float(str(self.channel.UpperBand)):