A Daily SPY Strategy Using a 15/30 EMA Crossover
Summary
This example presents a long-only moving-average trend rule for SPY using daily data. It calculates 15-period and 30-period exponential moving averages, waits until the slower average is initialized, and enters when the faster average is sufficiently above the slower one. A small tolerance is applied to the entry comparison to reduce repeated or marginal signals. The position is liquidated when the fast average falls below the slow average.
The example also limits processing to once per day and checks the current holding before placing an order. Its source configures a historical period from 2009 through 2015 and starting cash, but the document reports no returns, benchmark comparison, transaction costs, or risk measures. This is a simple trend-following illustration rather than evidence of profitability; it does not describe short entries, position sizing beyond full portfolio allocation, or protective stops.
Key ideas
- The example trades SPY using daily 15-period and 30-period exponential moving averages.
- It enters a long position when the faster average rises above the slower average by a small tolerance.
- It liquidates the long holding when the faster average falls below the slower average.
- The algorithm waits for indicator initialization and processes data no more than once per day.
- The historical configuration is given, but no strategy performance or risk results are reported.
Tags
Full text
# MovingAverageCrossAlgorithm
# MovingAverageCrossAlgorithm
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.
## 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>
### 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.set_start_date(2009, 1, 1) #Set Start Date
self.set_end_date(2015, 1, 1) #Set End Date
self.set_cash(100000) #Set Strategy Cash
# Find more symbols here: http://quantconnect.com/data
self.add_equity("SPY")
# create a 15 day exponential moving average
self.fast = self.ema("SPY", 15, Resolution.DAILY)
# create a 30 day exponential moving average
self.slow = self.ema("SPY", 30, Resolution.DAILY)
self.previous = None
def on_data(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
if not self.slow.is_ready:
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["SPY"].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 self.fast.current.value > self.slow.current.value *(1 + tolerance):
self.log("BUY >> {0}".format(self.securities["SPY"].price))
self.set_holdings("SPY", 1.0)
# we only want to liquidate if we're currently long
# if the fast is less than the slow we'll liquidate our long
if holdings > 0 and self.fast.current.value < self.slow.current.value:
self.log("SELL >> {0}".format(self.securities["SPY"].price))
self.liquidate("SPY")
self.previous = self.time
```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.