Basic Swing Trading Strategy

I have been asked by many viewers of this blog for more information on the basic swing trading strategy.

As is true in day trading, a multiple time frame analysis will lead to better success than just looking at an individual time frame.

The longer-term, in this case, weekly, is the signal that determines which way the security is going, i.e. the long term direction.

The shorter time frame, in this case daily, is the signal to follow in the same direction as the longer time frame, weekly.

I am using FedEx (FDX) for this example. The up arrows indicate long, down arrows indicate short. Blue vertical lines indicate neutrality.

We can see by the above chart, that FDX was in an uptrend starting on July 10, 2020.

Swing trading decisions from that date until January 1, 2021, should only be on the buy-side.


The chart below is the daily chart of FedEx, the shorter time frame that should be used to make the actual decision.

On July 11, 2020, the opening price of FedEx was $160.00 and a buy decision could have been made based on the weekly signal.

The initial long position could have been closed, October 28, the day after the neutral signal, at a price of $262.73, the opening price.

As in any trading decision, proper trade management should be used, such as stops.

This is only an example of what could have been done, not any kind of recommendation on FedEx or any security mentioned in any of this blog. Illustrations ONLY.


Relative Strength

In the many past posts, I have recommended that to be successful in swing trading, one has to be aware of the individual securities relative strength as it compares to some index of its peers.

The following chart is an example of  OIH which is the oil VanEck oil services ETF. I have compared this ETF to SPY, which represents the Standard & Poors 500 index.


The prices are as of the close yesterday, January 12, 2021.

The rules are very simple, and the action indicated by the up and down arrows reflects the result of following the rules.

  1. Buy when the target security is stronger, on a relative strength basis than the index.
  2. Buy ONLY when the target is stronger, has positive momentum, and the index is also going up.
  3. Close the position when any condition is violated. Shown by blue vertical lines.
  4. The rules apply to shorting when the actions are the opposite of the buy rules.

High Volatility versus Low Volatility

There is a fascinating article in today’s Wall Street Journal in its quarterly “Investing In Funds & ETFs on page R3.: The Time to High-Beta?

Once a Quarter.

The thesis is that new research conducted says that: “high -beta stocks tend to outperform in just one week per quarter. Only in that week, therefore, does it make sense that traders bet on high-beta stocks. That week occurs in the quarterly earnings season.

The article goes on to that to test the theory, one would invest, during the first week of earnings season in a high-beta stock ETF while shorting an equal dollar amount of a low ETF.

Their example in the article regarding a high-beta fund is the Invesco High Beta ETF. (SPHV) That ETF  contains the 100 highest beta stocks of the S&P 500 index. The 100 selected have the “highest sensitivity to market movements, or beta, over the past 12 months. The fund and the index are rebalanced and reconstituted quarterly in February, May, August, and November.”

The example of low beta is the Invesco S&P Low Volatility ETF(SPLV) which contains 100 S&P 500 stocks with the lowest realized volatility over the past 12 months. It then weighs each stock based on its volatility(well, lack thereof).

I assume it is rebalanced every month, but was unable to speak to anyone at Invesco to give me that information.

The article begs the question of whether a strategy of ALWAYS having a position in being long/short SPHV versus the reverse in SPLV would be successful?

The following illustration says YES!

The green vertical lines indicate long SPHB and short SPLV. The red vertical lines indicate long SPLV and short SPHB.


Swingtrader Suite for Day Trading

Many times I have been asked if the PerfectStorm strategy that works so well for swing trading has any use for the thousands of traders who day trade. Perhaps the following illustrations will be helpful. All graphs are as of the close of business of Friday, June 5, 2020.

The above picture is the daily results of BA versus SPY.

The top is BA, and the next security is SPY. The next line represents the relative strength of BA versus SPY. When the line is going up and GREEN, BA is stronger than SPY. When the line is going down and RED, SPY is stronger than BA.

The Vertical lines represent, when GREEN, that BA should be bought. When the vertical line is BLUE, the trade should be closed. When the vertical line is RED, BA should be short. Many hedge funds, when the trade indicates, will be short the opposite security, that is, when indicated long BA, they will be short SPY and vice versa.


The next picture is of BA versus SPY on a twenty-minute basis. I have left off the vertical signal lines, but a careful analysis will dictate the long/short position.

The next picture is of BA versus SPY on a two-minute chart.

There are thousands of “pairs” that can be traded in the same manner. Just ask Medallion Fund, or Citadel, or World Quant or the many other Quant funds.

I can be reached for further information at or (516) 902-7402

Overall Market Signal

The following chart of the ES Future which represents the S&P 500 index clearly shows that the U.S. equity market, as measured by the S&P 500 index clearly showed a negative daily bias starting at the close of October 4, 2018.

The ES has started to recover in 2019.

For more information on how this kind of quantitative approach can help you with your investment goals, please contact me at or by phone at 516-902-7402.  Also, look at for more information.

Prices are as of 8 AM, July 11, 2019




Relative Strength with Momentum

Readers of the blog will have noticed that the overall theme that I have proposed is that to be a successful swing trader one has to understand the principle of relative strength with positive momentum.

I originally proposed the concept in an e-book that I had offered in 2000. Because of my lack of web marketing, it was only downloaded a few times, although it was free.

Over the past few months, I have received multiple offerings of momentum services that offer similar strategies that I have been discussing on my, and websites for the past ten years or more.

One of the offers was a service, using only four ETF’s, that the provider stated would constantly beat the market.

Over the next few weeks and months, I am going to have on the website an example, updated weekly or daily if necessary, of my 4 ETF strategy. If followed, the strategy should emulate the best of the services being offered. It is certainly not a recommendation of what to buy or sell, but an example of what can be accomplished by using a relative strength with momentum strategy. It is for illustrative purposes ONLY!

The four ETF’s chosen are the result of my own research. They should portray a representation of the changes in market sector rotation. The four ETF’s have a positive and negative correlation with each other. The ETF’s are displayed here on daily charts.

I will update the daily charts when appropriate.

Remember, Green=XLE, Energy. Red=XLY, Consumer Discretionary. Light Blue=XLU, Utilities. Yellow=XTN, Transportation.

Latest update July 11, 2019, 8:00 AM



How to find Swing Trading stocks.

My Wall Street trading career started at Weeden & Company in 1967.

Weeden was one of a few trading firms that made an over the counter (OTC) market in listed shares.

At the time, commissions were fixed, and the commission was the same rate for 100 shares as it was for 10,000 shares or more. Weeden made a market in over 400 listed shares and for many bank stocks which were, at that time, traded only in the OTC market. It was an advantage for an institutional customer to deal with Weeden because his/her net cost would most often be less than to use a NYSE member and pay full commission. An example would be IBM. If the last sale of IBM was $225, a customer might have been able to buy 1,000 shares for $225,000 on the NYSE plus commission of $0.75 per share for a total cost of $225,750. Or, the customer might have been able to buy it at Weeden for $225.25, for a total cost of $225,250, a savings of $500.

Weeden also traded corporate and municipal bonds and notes.

I started as a trainee on the stock trading desk. The trainees did all kinds of chores like delivering coffee, changing the stock tape, and balancing out the positions of the traders to which we were assigned.

Although Weeden made markets in the largest NYSE issues, there were smaller listed and unlisted companies that were of some interest to our customers. One of my chores was to keep a record of “Indications of interest” for the stocks we did not trade.

When a customer had an indication of interest in a stock that we did not trade, I would pick up the phone and note the customer name, the date of inquiry, the name of the issue, whether the indication was a buy or sell, and the current price. I would start a card for the issue if new and note it on the customer’s card. The index cards were the standard 3X5, and we stored them in a box.

If a customer had an interest in an issue we had in the box and, was counter to an indication we already had, that is, a customer was a buyer, and we had a seller, I would contact the other party and negotiate a transaction. The “box’ became a nice profit center.

One of the many customers of Weeden was Buffett Partners, a small investment partnership/hedge fund in Omaha managed by Warren Buffett. Although he dealt with the trading desk, he was an active contributor to the indications of interest box. He was getting a reputation as a very successful hedge fund manager. At this time, in the late 1960’s, there were very few hedge funds, perhaps less than ten in all.

Weeden & Company was founded by Frank and Norman Weeden in San Francisco in 1929. The trading migrated to New York City and was run by Frank’s sons Alan, Don, and Jack. They divided up the responsibilities. Don, aka “Dewey,” lead equities, Alan did bonds, and Jack was in charge of operations. Frank and Norman were active behind the scenes. When Frank Weeden came to visit, we often chatted about the Foreign Exchange market, as I had become the New York arm of the London Eurobond trading desk. He was curious about the various foreign exchange (FX) rates and the flow of buy-and-sell equity orders from Europe. He was trying to get a feel of what caused the flow to go from buy to sell and vice versa, against the change in the U.S. Dollar exchange rates. Frank also had an interest in ‘the box’ especially Buffett’s indication of interests.

Mr. Weeden decided to call Buffett and ask about his investment strategy. I think, but I am not certain, that Frank wanted to invest in one of Buffett’s partnerships. I was a small spoke in the Weeden wheel and had no idea of what the most senior people discussed about investments. Weeden, being only a trading firm, had no need for an equity research department.

A few months later, during Mr. Weeden’s visit to New York, we had a cup of coffee in the dining room adjacent to the trading floor. The conversation was just some small talk about the Euro trading and FX markets. He paused and asked if I had ever heard about Multi Discriminate Analysis (MDA). I answered that I had no idea of what that was. This conversation was early in 1969. He had told me that Buffett had mentioned the term.

It wasn’t until many years later that I remembered the conversation. There was an article regarding Buffett and his new friend, Bill Gates. Buffett was explaining to Gates that he used MDA to find investment ideas. He used the Fortune Magazine list of the largest 500 companies, and he went back in time to discover how they got on the list. What were the attributes of small companies that enabled their growth? What characteristics did they have in common? I think that was what Weeden was talking about many years before. Buffett by this time had reorganized his investment strategy around his investment in Berkshire Hathaway and was well on his way to building the greatest investment success story of our time.

Fast-forward in time to the summer of 1986. Jeff Cohen and I had left our jobs at Dean Witter in 1985 where we were both Senior Vice Presidents to start Cohen Feit & Company, a NYSE member firm structured as a partnership. Jeff did risk arbitrage, and I did convertible arbitrage. Every summer, we hired some interns to get some experience. This summer we hired a friend of one of our limited partners’ sons. He was from Israel and lived in Kenya. He had finished his Israeli army service and was in the middle of his college years in the United States.

We were subscribers to a credit analysis tool called Zeta Services. Zeta Services was an improved version of Z-Score. Z-Score was the concept developed by Edward Altman, a professor of Finance at NYU, which purported to predict a corporate bond default a year or two into the future. It did that by analyzing corporate defaults in the past and using various financial ratios to predict future events. It compared defaults with comparable nondefault using Multi Discriminate Analysis to determine which financial ratios were more important than others over time. Subscription-based Zeta Services, which used additional factors, were more accurate and predicted default more years into the future. The Z-Score study had been published in the September 1968 issue of The Journal of Finance. I asked my intern to use our library of a few years of monthly issues of Zeta Services to determine if the Zeta score changes were a predictor of rating services changes. Rating service’s change of bond ratings would have an effect on the underlying bond price value of the convertible bond. Our intern went to the Moody’s library in Manhattan and compared Moody’s rating changes to Zeta score changes. His results were inconclusive. However, he did discover that changes in the overall Zeta score and certain financial ratios did have an impact on equity price changes. Rising and falling Zeta scores had a corresponding effect on the change in equity prices when compared to the Dow Jones Index (DJIA). A company with a rising Zeta score did better on average than the DJIA. The results on falling Zeta scores were more dramatic, as predicted by the basis of the Zeta score design.

Finding the best candidates for swing trading is different than finding stocks for long-term investment.

Swing trading is, by definition, the holding of equity for a few days or a few weeks and to profit by a rise in price that occurs during this period. Longer-term investors make decisions to hold assets for many weeks and probably many months.

Most swing trading recommendations are made based upon technical indicators and not fundamental factor analysis. These technical indicators portray, on a graph, past price movements and have moving averages and oscillators that indicate that these movements will have their trend continue somewhere into the future, based on the concept of the persistence-of-trend continuation.

Mr. Buffett made long-term equity purchases based on the idea that certain financial characteristics of smaller growing companies would enable them to grow to become much larger companies in the future.

Mr. Buffett talks about the idea the ideal company is a castle, and its management is its resident knights. The ideal castle is surrounded by a moat. As Berkshire Hathaway Vice Chairman Charlie Munger states: “The only duty of [the] corporate executive is to widen the moat.”

To find the best candidates to purchase for swing trading, first, I find those companies with wide moats. The wider and deeper, the better. I use basic financial statements and ratios like free cash flow, asset turnover, return-on-equity, assets, and others. I compute these numbers and ratios in most public companies and sort the results into many baskets. The best companies, those with large moats, are put into the best basket. That basket contains those equities that will be purchased. Second, I found that certain metrics are most important for shorter-term price movement. These metrics were discovered when, instead of looking at financials that influenced long-term growth, I looked at those metrics that impacted shorter-term price movement. I found these metrics by looking at stocks making new highs and worked backward like Buffett to discover why. Third, shares were purchased when my PerfectStorm technical indicators suggested they are on an upswing. The result is that a significantly difficult problem, when and how to successfully swing trade, has been solved.

 The following example of Sherwin Williams Company should illustrate the point.

Near the end of the year, during the week of  December 11, 2011, SHW was purchased at approximately 86.5 as indicated by the up arrows. This position was sold in the middle of August 2013 at approximately 167. During this time the financials of SHW indicated an expanding wide moat. The long position was re-established during the week beginning of April 27, 2013, at approximately 181. This position was sold during early July of 2015 at approximately 275. During this period, SHW was exhibiting sound financial reports. The long position was re-established the week of February 26, 2016, at approximately 271 and then sold during the week of September 2, 2016, at 285. Still moat. Solid financials. The long position was re-established the week of January 13, 2017, at 285 and was sold at approximately 410 on a weekly basis. The position was re-established on the long side the week of June 29, 2018, at approximately 401. The long position was exited during the week of October 12, 2018, at approximately 420. As of the close on November  26, 2018, the price of SHW was 411.54


  Close as of November 26, 2018.  SHW, on a daily chart.






Finding Swing Trading Candidates

“The best-performing stock in the S&P 500 this year was the company behind Invisalign clear braces

  • Align Technology makes “clear aligners” that orthodontists use to straighten a patient’s teeth in lieu of metal braces and headgear.
  • Despite an onslaught of competition, Align Technology was the top-performing stock among S&P 500 companies in 2017.
  • Align Technology’s stock shot up from a $96.49 opening price on the first day of trading this year to an opening price of $223.22 on Wednesday. “(12/27/2017)
  • CNBC

ALGN performance in 2017 was most suprising because of wht it was not. It was not a tech or bitcoin stock. It is a medical device company.

In my previous post on How to Find Swing Trading Stocks (listed below), I mentioned that the basic first step is to find those stocks that have demonstrated, through their income statements and balance sheet, that the company has the potential to become a moat stock.

Once that this potential has been noted, the next step is to find the breakout stage, when the rest of the investing world notices that the company has developed some positive price momentum.

Align Technology started to show early signs of both after the annual report of the year ending December 2010.

The financial results continued to get better. The stock price followed.

The following monthly, and weekly pictures of ALGN should illustrate

the earnings and price growth of this unusual company.

ALGN WeeklyALGN monthly

Pairs Trading Strategy as Proxy for Swing Trading

It should be no surprise to learn that the most successful quantitative hedge fund founders were, at the beginning of their careers, successful convertible arbitrageurs. I was fortunate to be one of the earliest inventors/discoverers of the basic convertible arbitrage strategy.

The basics of convertible arbitrage revolve around the concept of relative value. When the convertible is demonstrably more valuable than the underlying shares, than the convertible arbitrageur purchases the convertible security and shorts the underlying shares. Reversing the trade when the relative values return to normal.

In the early days of the convertible arbitrage strategy, the position carried a positive cash flow and the reversal of the position could take many months until a more favorable opportunity made the position less favorable.

The relative value strategy follows into other quantitative strategies.

Most pairs trading strategies use two securities in the same economic sector that have movements that are highly correlated and co -integrated. They track each other almost perfectly, the ratio of the price of the two such stocks should be almost the same. When their relative movements deviate from their expected behavior, the strategy dictates that the relatively cheaper one be purchased and the more expensive one be sold short. A reversion to the mean relationship. Traders waiting for various deviations, trying to put trades on at maximum deviations. Hundreds if not thousands of pairs traders follow the same highly correlated co-integrated pairs. It becomes a game of chicken. Each trader trying to get the trade on at the best possible time.

Similar to what happened to convertible arbitrage, the returns on the strategy go down as the number of players participating increase. It is a limited universe. The amount of funds devoted to mean reversion pairs trading decreases the amount of profit to be made.

I have developed a swing trading strategy that uses the relative value of the pairs components. Like convertible arbitrage, the strategy uses a large portfolio approach, putting on lots of different positions in differing economic sectors to diversify risk. Many investors may find it useful in a long only portfolio.

Swingtrading for farmers

Corn-Wheat 8-15-2014BTodays(Monday Aug 18, 2014) Wall Street Journal on page C1 has an article “U.S. Farmers Are Up to Ears in Corn”

To no ones surprise, the economic factors that led farmers to plant increasingly more acreage in corn has caused an oversupply of corn just when the demand is declining. This demand fall off is due to a decline in livestock herds and declining purchases from China.

In addition to the supply/demand problem, more farmers in certain parts of the country which have traditional planted wheat have moved to corn due to the changes in weather patterns over the last few years. In other parts of the country, the opposite is happening.

The choice to plant corn or wheat or a new combination of both is happening in farms all over North America.

Fortunately there are ways for farmers to hedge their crops. Traditionally that has been in the futures market.

In September 2011, Teucrium introduced an ETF designed to replicate the returns that mirror the movements in the spot prices of wheat. WEAT. It has developed other commodity ETF’s that follow corn,soybeans and others. The ETF for corn is CORN.  For more information on the construction and costs please go to the Teucrium website.

The following daily chart of CORN versus WEAT illustrates a Swingtrading approach to corn and wheat. Both commodities have been in a decline, but at various times, the better play was to follow the relative momentum. Prices as of the daily close, Friday, August 15,2014

Corn-Wheat 8-15-2014A

and a closer view:

Corn-Wheat 8-15-2014B

For further information on all the topics covered and how you can implement these and many others in your trading plan. Please contact me at See for day trading ideas.