Daily MACD Signal-Spread Filter for SPY Position Changes
Summary
This QuantConnect example demonstrates a daily MACD-based rule for SPY. It waits until the MACD indicator is ready, then checks the difference between MACD and its signal line relative to the fast moving average. A positive spread beyond a tolerance moves the portfolio to a full long position when it is not already long; a sufficiently negative spread liquidates the holding. The example plots the MACD components and fast and slow averages.
The code sets a historical date range and starting cash, but supplies no backtest results or performance analysis. Despite a comment describing a short signal, the negative condition liquidates SPY rather than opening a short position. The document is best read as an indicator and event-handling demonstration; it does not specify transaction-cost analysis, risk limits, or evidence that the threshold rule has an edge.
Key ideas
- The example uses daily MACD and signal values to form a normalized spread measure.
- A tolerance band filters small differences between MACD and its signal line.
- A positive threshold moves a non-long portfolio to a full SPY long position.
- A negative threshold liquidates SPY holdings rather than initiating a short position.
- The code illustrates indicator readiness and plotting but presents no performance results.
Tags
Full text
# MACDTrendAlgorithm
# MACDTrendAlgorithm
Simple indicator demonstration algorithm of MACD
## 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>
### Simple indicator demonstration algorithm of MACD
### </summary>
### <meta name="tag" content="indicators" />
### <meta name="tag" content="indicator classes" />
### <meta name="tag" content="plotting indicators" />
class MACDTrendAlgorithm(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(2004, 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", Resolution.DAILY)
# define our daily macd(12,26) with a 9 day signal
self.__macd = self.macd("SPY", 12, 26, 9, MovingAverageType.EXPONENTIAL, Resolution.DAILY)
self.__previous = datetime.min
self.plot_indicator("MACD", True, self.__macd, self.__macd.signal)
self.plot_indicator("SPY", self.__macd.fast, self.__macd.slow)
def on_data(self, data):
'''on_data event is the primary entry point for your algorithm. Each new data point will be pumped in here.'''
# wait for our macd to fully initialize
if not self.__macd.is_ready: return
# only once per day
if self.__previous.date() == self.time.date(): return
# define a small tolerance on our checks to avoid bouncing
tolerance = 0.0025
holdings = self.portfolio["SPY"].quantity
signal_delta_percent = (self.__macd.current.value - self.__macd.signal.current.value)/self.__macd.fast.current.value
# if our macd is greater than our signal, then let's go long
if holdings <= 0 and signal_delta_percent > tolerance: # 0.01%
# longterm says buy as well
self.set_holdings("SPY", 1.0)
# of our macd is less than our signal, then let's go short
elif holdings >= 0 and signal_delta_percent < -tolerance:
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.