Building a Relative-Price Indicator from Custom IBM and SPY Data
Summary
This QuantConnect example demonstrates using custom daily equity data for IBM and SPY, then applying an indicator extension to divide SPY's one-period simple moving average by IBM's. Since each moving average uses a period of one, the resulting series represents the relative price ratio. The algorithm plots both input series and the ratio, and waits for all indicators to be ready before acting.
Its trading rule buys 100 IBM shares when the ratio is above one and the portfolio has no investment, then liquidates when the ratio falls below one. The example runs over a stated 2014–2018 date range with initial cash of 25,000, but provides no performance analysis. It illustrates data wiring, indicator composition, and plotting rather than a well-supported trading edge; the ratio rule's rationale, costs, risk controls, and out-of-sample behavior are not discussed.
Key ideas
- Custom daily data can feed standard indicators in an algorithm.
- An indicator extension can combine two indicators by calculating their ratio.
- The example uses one-period simple moving averages, so the ratio tracks SPY's price relative to IBM's.
- It buys IBM when the ratio exceeds one and liquidates when it drops below one.
- The document demonstrates implementation and plotting but gives no evidence of strategy performance.
Tags
Full text
# CustomDataIndicatorExtensionsAlgorithm
# CustomDataIndicatorExtensionsAlgorithm
The algorithm creates new indicator value with the existing indicator method by Indicator Extensions Demonstration of using the external custom data to request the IBM and SPY daily data
## 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 *
from HistoryAlgorithm import *
### <summary>
### The algorithm creates new indicator value with the existing indicator method by Indicator Extensions
### Demonstration of using the external custom data to request the IBM and SPY daily data
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="using quantconnect" />
### <meta name="tag" content="custom data" />
### <meta name="tag" content="indicators" />
### <meta name="tag" content="indicator classes" />
### <meta name="tag" content="plotting indicators" />
### <meta name="tag" content="charting" />
class CustomDataIndicatorExtensionsAlgorithm(QCAlgorithm):
# Initialize the data and resolution you require for your strategy
def initialize(self):
self.set_start_date(2014,1,1)
self.set_end_date(2018,1,1)
self.set_cash(25000)
self.ibm = 'IBM'
self.spy = 'SPY'
# Define the symbol and "type" of our generic data
self.add_data(CustomDataEquity, self.ibm, Resolution.DAILY)
self.add_data(CustomDataEquity, self.spy, Resolution.DAILY)
# Set up default Indicators, these are just 'identities' of the closing price
self.ibm_sma = self.sma(self.ibm, 1, Resolution.DAILY)
self.spy_sma = self.sma(self.spy, 1, Resolution.DAILY)
# This will create a new indicator whose value is sma_s_p_y / sma_i_b_m
self.ratio = IndicatorExtensions.over(self.spy_sma, self.ibm_sma)
# Plot indicators each time they update using the PlotIndicator function
self.plot_indicator("Ratio", self.ratio)
self.plot_indicator("Data", self.ibm_sma, self.spy_sma)
# OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
def on_data(self, data):
# Wait for all indicators to fully initialize
if not (self.ibm_sma.is_ready and self.spy_sma.is_ready and self.ratio.is_ready): return
if not self.portfolio.invested and self.ratio.current.value > 1:
self.market_order(self.ibm, 100)
elif self.ratio.current.value < 1:
self.liquidate()
```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.