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Selecting a Daily Equity Universe by Dollar Volume

Article Strategy library · Author: QuantConnect
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Summary

This educational example shows how to define a dynamic equity universe using a built-in selection helper. It selects the three stocks with the highest dollar volume and applies daily data resolution to securities added through the universe. The algorithm responds to membership changes: it submits market orders for newly added securities and liquidates holdings that leave the selection. The example also records whether the securities-changed event was called and raises an assertion at the end if the event never occurred.

The code illustrates universe membership handling rather than a complete investment strategy. It uses a short sample date range and a fixed order quantity, and it does not provide performance analysis, transaction cost assumptions, or a portfolio allocation method. Ranking by dollar volume favors actively traded stocks, but the example does not address survivorship bias, rebalancing effects, or whether the resulting holdings suit a particular objective. Its main value is as a compact demonstration of universe selection and lifecycle events in an algorithmic trading framework.

Key ideas

  • A built-in universe helper can select a fixed number of stocks ranked by dollar volume.
  • Universe settings determine the data resolution for selected securities.
  • The securities-changed event provides lists of additions and removals to process.
  • The example buys newly added names and liquidates invested names that leave the universe.
  • This is a framework demonstration, not evidence of a profitable portfolio strategy.

Tags

EquitiesPortfolio constructionExecution
Full text
# UniverseSelectionDefinitionsAlgorithm


# UniverseSelectionDefinitionsAlgorithm









This algorithm shows some of the various helper methods available when defining universes

## 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>
### This algorithm shows some of the various helper methods available when defining universes
### </summary>
### <meta name="tag" content="using data" />
### <meta name="tag" content="universes" />
### <meta name="tag" content="coarse universes" />
class UniverseSelectionDefinitionsAlgorithm(QCAlgorithm):

    def initialize(self):
        # subscriptions added via universe selection will have this resolution
        self.universe_settings.resolution = Resolution.DAILY

        self.set_start_date(2014,3,24)    # Set Start Date
        self.set_end_date(2014,3,28)     # Set End Date
        self.set_cash(100000)            # Set Strategy Cash

        # add universe for the top 3 stocks by dollar volume
        self.add_universe(self.universe.top(3))

        self.changes = None
        self.on_securities_changed_was_called = False

    def on_data(self, data):
        if self.changes is None: return

        # liquidate securities that fell out of our universe
        for security in self.changes.removed_securities:
            if security.invested:
                self.liquidate(security.symbol)

        # invest in securities just added to our universe
        for security in self.changes.added_securities:
            if not security.invested:
                self.market_order(security.symbol, 10)

        self.changes = None

    # this event fires whenever we have changes to our universe
    def on_securities_changed(self, changes):
        self.changes = changes
        self.on_securities_changed_was_called = True

    def on_end_of_algorithm(self):
        if not self.on_securities_changed_was_called:
            raise AssertionError("OnSecuritiesChanged() method was never called!")

```

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.