Files
quantconnect--lean/Algorithm.Python/ParameterizedAlgorithm.py
T
Martin-Molinero 03f56481d4
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Refactor python algorithm import (#5657)
* Python research import improvements

- Improve start.py for research env
- Remove unrequired imports

* Centralize algorithm imports

* Add regression test GH action

* Unit test python import clean up

* Join research and main imports

* More python import clean up

* Fix failing skipped regression algorithm
2021-06-15 19:06:06 -03:00

58 lines
2.4 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 AlgorithmImports import *
### <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 * 1.001:
self.SetHoldings("SPY", 1)
elif fast < slow * 0.999:
self.Liquidate("SPY")