How to Build a Trading Strategy – Part 3

Matt Radtke is Senior Researcher for Connors Research. Mr. Radtke graduated magna cum laude from Michigan State University with a degree in computer science. He has 25 years of software development experience in companies large and small, including Hewlett-Packard and Bell Northern Research.

Mr. Radtke has been actively trading stocks, ETFs, and options since 2008. Over the past several years he has become increasingly involved with the Connors Group family of companies, first as a student, then as a member of Chairman’s Club, and finally as a consultant, researcher, and author.

Universe & Liquidity

Defining a universe for our strategy has two closely-related purposes:

  1. It constrains the set of securities to which we will apply the strategy on a going-forward basis
  2. It allows us to create a set of securities that we can use for back-testing, thus evaluating the historical performance of our strategy

Intuitively, it makes sense that we would test a strategy against a set of securities that is as similar as possible to the securities that we actually intend to trade against. This doesn’t mean that our test universe needs to be comprised of the exact same ticker symbols that we will trade (although it could), but rather that we have a well-defined set of rules that describes the universe. For example, the following all describe valid universes:

  • Leveraged ETF’s with a 21-day average trading volume of at least 100,000 shares
  • Stocks with a minimum price of $10/share and a maximum price of $100/share
  • The ticker symbols SPY, QQQ, and IWM
  • SPY options that have more than 8 days and less than 50 days until expiration

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Recall that in Part 2 of this series we stated our central thesis as:

Central Thesis

              Long Trades: Buy a stock that opens X% lower than the previous day’s lowest price.

              Short Trades: Short a stock that opens X% higher than the previous day’s highest price.

Our central thesis has already narrowed our tradable universe by stating that we will buy and short stocks. That means that ETFs, options, futures, bonds and other financial instruments are off the table.

For stock universes, one important thing to consider is liquidity. This is especially true for strategies that include a short component. From a practical perspective, thinly-traded stocks are often not available to borrow from your broker. By requiring a sizable trading volume, we increase the chances that the short trades that are entered by our back test would have actually been tradable if we’d been using our strategy in the past. Higher volume also reduces the size of the bid-ask spread for both long and short trades, meaning that we are more likely to enter and exit our trades at a favorable price while minimizing slippage. And finally, depending on the size of your account, if the trading volume on a stock is too low then you run the risk of actually influencing the price with your entry and exit orders.

To address all of these concerns, we will require a 21-day (one trading month) average volume of 2.5 million shares per day. We could express this rule as:

             MA (Vol, 21) > 2,500,000

The second important consideration for stock universes is the price range. Very low-priced securities, sometimes called penny stocks, often have very volatile prices, and are sometimes manipulated by unscrupulous traders. For that reason, many professional traders prefer to stay away from these stocks. Our research has shown that a lower price boundary of $5/share is sufficient to filter out these unpredictable stocks.

Depending on your typical position size, you may also wish to filter out higher-priced equities. For example, if you usually put $2000 into each position, you may not want to buy shares of a $700 stock like Google. For the strategy that we’re developing here, we will allow high-priced stocks, but filter out the low priced ones. Thus, our final universe-limiting rule can be articulated as:

             Close > $5

Now that we’ve defined our universe, we’re almost ready to do some testing. Our central thesis defines a simple entry rule for both long and short trades. However, we have not yet decided on any exit rules. For now, let’s assume that we want to avoid overnight risk, and thus we will close out all our positions at the end of each trading day. We now have a very simple long/short strategy which we can be expressed quantitatively as:

Long Rules

  1. A Buy Setup occurs when all of the following conditions are true:
    • MA (Vol, 21) > 2,500,000
    • Close > $5
  2. Buy the stock when:
    • The previous day is a Buy Setup
    • Today’s Open < Previous Day’s Low * (1 – X%)
  3. Sell at the close on the day of entry

Short Rules

  1. A Short Setup occurs when all of the following conditions are true:
    • MA (Vol, 21) > 2,500,000
    • Close > $5
  2. Short the stock when:
    • The previous day is a Short Setup
    • b. Today’s Open > Previous Day’s High * (1 + X%)
  3. Cover at the close on the day of entry

In the next installment of this series, we’ll discuss back-testing our minimalist strategy to see whether the results warrant additional refinement.

Click here to read How to Build a Trading Strategy – Part 1

Click here to read How to Build a Trading Strategy – Part 2

Click here to read How to Build a Trading Strategy – Part 4

Click here to read How to Build a Trading Strategy – Part 5

Click here to read How to Build a Trading Strategy – Part 6

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About Matt Radtke

Matt Radtke is Senior Researcher for Connors Research. Mr. Radtke graduated magna cum laude from Michigan State University with a degree in computer science. He has 25 years of software development experience in companies large and small, including Hewlett-Packard and Bell Northern Research. Mr. Radtke has been actively trading stocks, ETFs, and options since 2008. Over the past several years he has become increasingly involved with the Connors Group family of companies, first as a student, then as a member of Chairman’s Club, and finally as a consultant, researcher, and author.
Matt has co-authored several quantified strategy guidebooks including ETF Trading with Bollinger Bands, Options Trading with ConnorsRSI, Trading Leveraged ETFs with ConnorsRSI, and ETF Scale-In Trading.

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