Michael Handschuh
18a559943e
- Adds log to display the python version the algorithm is using. - Fixes python algorithms that were failing because of small subtleties like leading zeroes. - Updates pythonnet with a version compiled with python 3.6 flags - Changes in DockerfileFoundation: we now use miniconda to manage the python environment. - Took the opportunity to add NTLK (#1349), Tensorforce (#1369) and PyTorch/Pyro (#1385). - Changes readme in Algorithm.Python to show steps to install miniconda
68 lines
2.7 KiB
Python
68 lines
2.7 KiB
Python
# 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 clr import AddReference
|
|
AddReference("System")
|
|
AddReference("QuantConnect.Algorithm")
|
|
AddReference("QuantConnect.Indicators")
|
|
AddReference("QuantConnect.Common")
|
|
|
|
from System import *
|
|
from QuantConnect import *
|
|
from QuantConnect.Algorithm import *
|
|
from QuantConnect.Indicators import *
|
|
from QuantConnect.Parameters import *
|
|
import decimal as d
|
|
|
|
### <summary>
|
|
### Demonstration of the parameter system of QuantConnect. Using parameters you can pass the values required into C# algorithms for optimization.
|
|
### </summary>
|
|
### <meta name="tag" content="optimization" />
|
|
### <meta name="tag" content="using quantconnect" />
|
|
class ParameterizedAlgorithm(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(2013, 10, 7) #Set Start Date
|
|
self.SetEndDate(2013, 10, 11) #Set End Date
|
|
self.SetCash(100000) #Set Strategy Cash
|
|
# Find more symbols here: http://quantconnect.com/data
|
|
self.AddEquity("SPY")
|
|
|
|
# Receive parameters from the Job
|
|
ema_fast = self.GetParameter("ema-fast")
|
|
ema_slow = self.GetParameter("ema-slow")
|
|
|
|
# The values 100 and 200 are just default values that only used if the parameters do not exist
|
|
fast_period = 100 if ema_fast is None else int(ema_fast)
|
|
slow_period = 200 if ema_slow is None else int(ema_slow)
|
|
|
|
self.fast = self.EMA("SPY", fast_period)
|
|
self.slow = self.EMA("SPY", slow_period)
|
|
|
|
|
|
def OnData(self, data):
|
|
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''
|
|
|
|
# wait for our indicators to ready
|
|
if not self.fast.IsReady or not self.slow.IsReady:
|
|
return
|
|
|
|
fast = self.fast.Current.Value
|
|
slow = self.slow.Current.Value
|
|
|
|
if fast > slow * d.Decimal(1.001):
|
|
self.SetHoldings("SPY", 1)
|
|
elif fast < slow * d.Decimal(0.999):
|
|
self.Liquidate("SPY") |