Passing Parameters into a Moving Average Crossover Algorithm
Summary
This example shows how a QuantConnect algorithm can read fast and slow exponential moving average periods from job parameters, with fallback values when parameters are absent. That lets the same algorithm configuration be varied for optimization without changing its source. It initializes SPY data and waits until both averages are ready before evaluating signals. The strategy buys SPY when the fast average exceeds the slow average by a small margin, and liquidates when it falls below the slow average by a corresponding margin. The document provides implementation logic, but no performance results or comparison of parameter choices. Its brief sample dates and fixed signal rules do not establish profitability, and the example does not discuss transaction costs or risk controls.
Key ideas
- Job parameters can supply indicator periods, with defaults used when no value is provided.
- The sample compares fast and slow exponential moving averages on SPY.
- A margin above or below the slow average triggers entry or liquidation.
- The document demonstrates configuration and signal logic but reports no backtest evidence.
Tags
Full text
# ParameterizedAlgorithm
# ParameterizedAlgorithm
Demonstration of the parameter system of QuantConnect. Using parameters you can pass the values required into C# algorithms for optimization.
## Source (Apache-2.0)
```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.set_start_date(2013, 10, 7) #Set Start Date
self.set_end_date(2013, 10, 11) #Set End Date
self.set_cash(100000) #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data
self.add_equity("SPY")
# Receive parameters from the Job
fast_period = self.get_parameter("ema-fast", 100)
slow_period = self.get_parameter("ema-slow", 200)
self.fast = self.ema("SPY", fast_period)
self.slow = self.ema("SPY", slow_period)
def on_data(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.is_ready or not self.slow.is_ready:
return
fast = self.fast.current.value
slow = self.slow.current.value
if fast > slow * 1.001:
self.set_holdings("SPY", 1)
elif fast < slow * 0.999:
self.liquidate("SPY")
```Shown in full with attribution under the source's licence. Licence: Apache-2.0
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.