Selecting Equities by 100/300-Day EMA Trend Strength
Summary
This example shows how to include technical signals in coarse fundamental universe selection. It maintains fast and slow exponential moving averages for each symbol, updated with adjusted daily prices, and retains securities whose fast average exceeds the slow average by a tolerance. Candidates are ranked by the normalized gap between the averages, and the ten highest-ranked symbols are selected. The algorithm assigns holdings when securities enter the universe and liquidates invested securities when they leave.
The example demonstrates an implementation pattern rather than evidence of a profitable strategy: it provides no reported performance results or comparison. Its settings specify daily resolution, leverage, and a historical date range, but these alone do not establish how the method performs. The universe logic also creates indicator state for symbols encountered in the coarse data and relies on both averages becoming ready before updating the trend condition. The approach is therefore a trend-filtered selection example, not a complete evaluation of costs, portfolio risk, or robustness across market regimes.
Key ideas
- The universe selection function updates 100-day and 300-day exponential moving averages for each symbol.
- A symbol qualifies when its fast average exceeds its slow average by a set tolerance.
- Qualified symbols are ranked by the average gap scaled relative to their midpoint.
- The algorithm selects up to ten highest-ranked candidates and liquidates holdings removed from the universe.
- The document illustrates implementation mechanics but supplies no performance evidence.
Tags
Full text
# EmaCrossUniverseSelectionAlgorithm
# EmaCrossUniverseSelectionAlgorithm
In this algorithm we demonstrate how to perform some technical analysis as part of your coarse fundamental universe selection
## 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 algorithm we demonstrate how to perform some technical analysis as
### part of your coarse fundamental universe selection
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="indicators" />
### <meta name="tag" content="universes" />
### <meta name="tag" content="coarse universes" />
class EmaCrossUniverseSelectionAlgorithm(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(2010,1,1) #Set Start Date
self.set_end_date(2015,1,1) #Set End Date
self.set_cash(100000) #Set Strategy Cash
self.universe_settings.resolution = Resolution.DAILY
self.universe_settings.leverage = 2
self.coarse_count = 10
self.averages = { }
# this add universe method accepts two parameters:
# - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
self.add_universe(self.coarse_selection_function)
# sort the data by daily dollar volume and take the top 'NumberOfSymbols'
def coarse_selection_function(self, coarse):
# We are going to use a dictionary to refer the object that will keep the moving averages
for cf in coarse:
if cf.symbol not in self.averages:
self.averages[cf.symbol] = SymbolData(cf.symbol)
# Updates the SymbolData object with current EOD price
avg = self.averages[cf.symbol]
avg.update(cf.end_time, cf.adjusted_price)
# Filter the values of the dict: we only want up-trending securities
values = list(filter(lambda x: x.is_uptrend, self.averages.values()))
# Sorts the values of the dict: we want those with greater difference between the moving averages
values.sort(key=lambda x: x.scale, reverse=True)
for x in values[:self.coarse_count]:
self.log('symbol: ' + str(x.symbol.value) + ' scale: ' + str(x.scale))
# we need to return only the symbol objects
return [ x.symbol for x in values[:self.coarse_count] ]
# this event fires whenever we have changes to our universe
def on_securities_changed(self, changes):
# liquidate removed securities
for security in changes.removed_securities:
if security.invested:
self.liquidate(security.symbol)
# we want 20% allocation in each security in our universe
for security in changes.added_securities:
self.set_holdings(security.symbol, 0.1)
class SymbolData(object):
def __init__(self, symbol):
self._symbol = symbol
self.tolerance = 1.01
self.fast = ExponentialMovingAverage(100)
self.slow = ExponentialMovingAverage(300)
self.is_uptrend = False
self.scale = 0
def update(self, time, value):
if self.fast.update(time, value) and self.slow.update(time, value):
fast = self.fast.current.value
slow = self.slow.current.value
self.is_uptrend = fast > slow * self.tolerance
if self.is_uptrend:
self.scale = (fast - slow) / ((fast + slow) / 2.0)
```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.