The Finance Book Club

Chapter 14 - Success is what you make it

1. Personal attributes required for success:
   1. Self-confidence - realization that your mind can learn the truth and apply it with positive results in every realm of life
   2. Self-motivation and commitment; ability and willingness to put the time and energy into learning what to trade and how to trade it
   3. Intellectual independence - ability to stand on your own judgement based on facts as you see them
   4. A fundamental personal honesty, a total commitment to identifying and dealing with the truth about yourself, the markets, your decisions
   5. A sincere love of what you do; the greatest reward comes from the work process itself, not money or fame

2. In a country abounding with financial opportunity:
   1. It’s strange that people have a love-hate relationship with money
   2. Some people see it as something that enslaves them. “If I weren’t entrapped by the pursuit of the almighty dollar I’d be free to do what I want and be fulfilled in life”
   3. Others spend their lives in pursuit of money without ever learning to enjoy it

3. For Vic money is “a tool that allows [people] either to exchange the product of their labour for the products and services of others or to save the results of their efforts for the future. Money is a means with which to transform personal energy into matter, a vehicle to turn dreams into reality.”
4. The power of the subconscious mind is an enormous resource
5. Activities that are ‘fun and effortless’ - are in alignment with the subconscious
6. When you have a meltdown the subconscious is not aligned
7. Personal greatness is a direct result of the ‘supercomputer of the mind’ functioning according to design
8. Attaining a positive and motivated state should be a goal
9. Goals, actions, awareness, change
10. Make a commitment to success
11. Output depends on input, introspection is using your conscious awareness to access the subconscious, desire to change our current state, the need for ‘true motivation’, remove limiting beliefs etc. etc.
12. This goes on for pages and pages. Vic gets steadily more grandiose, more Randian, and more abstract with every page, until you are either ready to join his cult or ready to move onto the next chapter.

Chapter 15 - Changing so it sticks

1. To be a successful trader you have to obtain the mindset that following the rules is pleasurable and breaking the rules is painful
2. Success is a process of change driven by the desire to change current mental and physical state
3. Lack of will to execute is the result of limiting associations, values and beliefs
4. Unlock the content of the subconscious mind, find limiting associations, values and beliefs - replace them with new ones which move us on to the path of our choosing
5. Three tools to achieve a state where pursuing goals is pleasurable and not pursuing them is painful
   1. The body
      1. Being miserable takes more energy than being positive
      2. Posture - stand up straight, erect, shoulders square, look up at the ceiling; big smile, no matter how stupid this feels
      3. It is hard to feel depressed when you force yourself to do this
      4. Vic recommends Anthony Robbins
      5. The athletic ritual - designed to achieve a state of peak performance
      6. What you feel is a big part of who you are
      7. Grasp the moment of hesitation and do it.
      8. Exercise is more important when you feel down.
      9. Exercise is an investment

   2. The conscious mind
      1. Use your conscious mind to evaluate the decisions made by your subconscious
      2. Does the trade fit with your rules?
      3. Observe, focus, think
      4. If you continually make mistakes, find the incongruity in yourself, don’t blame it on outside factors
      5. Awareness
      6. Finding focus
      7. “With enough knowledge and practice I can master anything I set my mind to.”
      8. The importance of fun - an association that makes learning easier
      9. Questions are the best possible way to shift the focus of awareness
      10. Be aware of your self-talk and avoid recriminations

   3. Subconscious mind
      1. Actions are not so much decided as they are driven by programming of the subconscious
      2. We are motivated by
         1. Desire for pleasure
         2. Avoidance of pain

      3. Your overall ethical structure determines your character
         1. Vic again uses the example of someone who sees money as a means to desirable ends (the healthy view)
         2. The other sees money as the root of all evil
         3. The latter is miserable, of course

   4. Values and character
      1. What you do depends on what you value
      2. How you do something depends on your beliefs
      3. There are means values and ends values
      4. Means values carry us forward in life
      5. Ends values are states of emotional pleasure we want to attain
      6. Every ends value has a negative counterpart - a disvalue
      7. Quickly answer what are the most important things in your life
         1. Getting to the end of this blither blather
         2. Finishing the book
         3. Starting a new book

      8. Limiting beliefs are at the core of most failure
         1. Repetition!

      9. List all your limiting beliefs
      10. Define new rules you want to adapt
      11. Rig the game of life to make it easy to win
      12. Life is too short and precious to waste time by setting ourselves up to experience futility
      13. ‘Significant time’ spent on these exercises will give you a grasp on the values and beliefs that limit you and the ones that you want to replace them with
      14. Knowledge is not enough - you need a method of execution

Harnessing the power of the subconscious

1. Significant events in our lives
   1. Some painful to recall
   2. Some pleasurable

2. To install a new positive value or belief you have to convince the subconscious that it will lead to enormous amounts of pleasure
3. To eliminate a limiting association we need to convince the subconscious that it will cause lots of pain
4. Anthony Robbins’ ‘Dickens Pattern’
   1. Pick 2 limiting beliefs you want to change
   2. Think about all the consequences of having these beliefs, all the pain, etc.
   3. With your eyes closed think about the pain, and how they make you feel? Do they make you feel in control? Obviously not.
   4. Move one year into the future projecting the pain ahead into your life. What will it cost you in terms of your career, your relationships?
   5. Project five years into the future, What does it cost you in terms of self-confidence, self-image? What do you say to yourself? How do you feel about yourself? What are the costs in every area of your life?
   6. Repeat for 10 years ahead
   7. Repeat for 20 years ahead
   8. This process is very painful if you carry through and do it right
   9. Do whatever it takes to make the pain of the future feel real

5. Come back to the present, shake out your body, take some deep energizing breaths
6. Pick out a new positive belief you want to install in place of the limiting belief
7. Perform the same steps but this time enjoy the positive changes of your new belief
8. Look at both destinies and decide which one you’re committed to having.
9. “Feel the excitement and unlimited possibilities that creating a your new future will bring.
10. Sit down and write down how each new belief you install is going to enhance your life”

This might work, if you can face it?

Anchoring

1. Subconscious mental process that links sensory stimulus to an emotional state
2. For ex. the flashing red lights in your rearview mirror - produces an unpleasant sensation
3. A favorite song that reminds you of good times
4. Physical rituals are triggers for anchors established in the subconscious

1. Intense emotional state
2. The anchor trigger (established via some sense)
3. Some unusual stimulus - something you aren’t doing all the time
4. Replication of the stimulus

The ability to consistently act in accordance with your knowledge, and overcome the conflict of emotion and reason is by understanding the supercomputer model of consciousness. Establish goals backed by consistent and viable motivation and commitment.

Such is his passion for this topic that Vic is not yet finished with it. Next chapter examines why some people have a self-destructive bent.

Chapter 16 - Conquering False Pride
The most important reason people fail in trading

1. Self-delusion
2. Neurosis and Human Growth by Dr. Karen Horsey
3. The False Pride system
   1. Idealized self-image
   2. Search for glory
   3. Tyranny of the shoulds
   4. Neurotic pride

   
   

4. Self-awareness is more important in the financial world than in other professions
   1. Traders must confront their own failures daily, other professions can find refuge in rationalizations

   
   

Your Evil Twin : the idealized self-image

1. Your core self = given potentialities
2. To protect ourselves against pain we ascribe to ourselves a set of false potentialities
3. We have a sense of what our lives should be and strive to realize our true potential
4. Our evil twin strives to make good on unrealistic and idealized potentialities
5. This subverts the growth of the core self
6. “Through the use of imagination, the evil twin builds a fortress against the perception of a hostile reality. For protection, gradually and unconsciously, the imagination sets to work and creates in his mind an idealized image of himself.”
7. In this process he becomes a hero, a genius, a supreme lover, a god (overconfidence?)

The Search for Glory

1. Self-idealization turns into a search for glory
2. Followed by the need for perfection, neurotic ambition, and the need for a vindictive triumph
3. It’s hard to accept that mistakes and pain are inevitable and essential part of life
4. It’s hard to accept this because it upsets our idealized image of ourselves
5. The need for perfection manifests itself in a rigid set of ‘shoulds’ and ‘taboos’ that are derived from what we think attaining the perfect idealized self requires
6. Vic uses the example of missing some commodity trades because his focus was elsewhere. Instead of just admitting he wasn’t focused he gets furious with himself for not being perfect as a trader.
7. The need for perfection - the tyranny of the shoulds
8. Neurotic ambition - the compulsive drive for success
9. Someone lacking real self-confidence seeks high ranking on a comparative scale in an attempt to achieve ‘pseudo self-esteem’.
10. Genuine talent and ability that is compulsive and driven in nature, that never results in personal fulfillment
11. Vic uses the example of Ivan Boeskey.
12. “If I were rich I’d be happy” neurotic ambition is at the root, also “burnout”
13. Vindictive triumph - epitomized in the character of Gordon Gecko in ‘Wall Street’; making money wasn’t his main ambition, he wanted to destroy and displace people who represented a challenge to his idealized view of himself
14. Vic dislikes the portrayal of traders in ‘Wall Street’ but he admits that he knows of at least one person who ruined someone else’s career after gaining their trust

Compulsion and Imagination

1. ‘Healthy motivation’ is distinguished from ‘search for glory’ by two characteristics
   1. Compulsive nature
      1. Arising out of a need to avoid a falsely perceived pain

      
      

   2. Imaginative character
      1. When the object of our actions becomes purely imaginary, imagination becomes a destructive agent

      
      

   
   

“The distinguishing characteristic of all wishes that are claims is that people holding them feel like exceptions to the rules. They are somehow different; they belong to the rare few who understand love, justice, human nature. They are the traders who understand trading rules are necessary but their ‘superior wisdom’ releases them from being bound by the rules.”

Vic continues to explore this topic in great depth, constructing different characters who handle ‘the search for glory’ in different ways. One of these is someone who seeks mastery, and they end up being stubborn, if not bad traders. They thrive on admiration, but they always fall short of their internal ideal.

Another type values ‘freedom’ and sees any restriction or necessity as being coercive, so that even important things like trading rules, marital duties, or work ends up arousing conscious or unconscious resentment, which as a consequence makes him listless and cause him to rebel, doing only what he wants when he wants. If he can’t be the ultimate, of piety, chastity, sincerity then he transforms into the opposite and becomes thoroughly “bad”.

Another type is looking for an idealized love and they end up being passive?

I just don’t know how much stock to put in any of this. I suppose it's possible one of these archetypes is similar to me, and if I'm not careful I might make the same mistakes. I just keep thinking about how psychology is at the bottom of the heap of sciences for a reason. It's hard to prove any of this.

Chapter 17 - Finding your Freedom

1. Freedom is easier to find if you don’t have to worry about disaster striking your family or yourself
2. “The world doesn’t have to be perfect to be loved by me”
3. Values, not disasters are the motive power of life
4. You can’t pursue a negative
5. If avoiding pain is your life focus your positive action is limited
6. If pain becomes dominant in your life you sabotage your ability to think, grow, produce
7. Life involves risk
8. When you don’t win, be willing to pick up and do it all over again
9. “To present yourself to the world; to offer it your products and services in trade; to bestow upon it your achievements, your productive capacity, your energy, your thoughts and opinions, all of these are the things that make life rich, both spiritually and materially.”

I wasn't expecting this level of poetry from Sperandeo, who looks like a mob boss. In fact I wasn't expecting any poetry in a trading book. Is this still just a book about trading? I'm inclined to say for better or worse the last third of this fits squarely in the ‘self-help’ category. Also for better or worse, we have reached the end.

Conclusions
I think chapters 6 & 7 were very useful and I’m particularly indebted to the 1-2-3 trend change idea, as it’s elegantly simple and can be applied without any strenuous mental exertions.

I’m not sure how valuable the bits about Dow Theory will be to you, but for me, I’m inclined to leave them where we found them. The trading rules in chapter 12 were solid, however I think not many of them would be considered original research.

The psychology (chapters 13-17) behind the reasons for trader self-destruction might be extremely important if you are a discretionary trader who relies entirely on being at the top of your game at all times. And it may be important if you ever find yourself doing something that wasn’t part of your trading plan and later on you think to yourself, “why did I do that?!” But then again, it might not be. I don’t generally go in for the ‘psychology of trading’ angle because I feel that if your system is solid then your psychology will get behind it in due course. However Vic devotes more pages and has clearly put in more thought to this subject than nearly anyone besides maybe Mark Douglass? So if this is your thing I can see why this book would be important to you.

I think the ‘Dickens Pattern’ is quite intriguing (though it’s not Vic’s invention) and some day if I’m feeling self-assured I may even try tackling it. If I do and if it has positive results I’ll surely mention it in my trading journal.

As for the rest of it, if you are not a libertarian, with an axe to grind against the Fed, the Government, and the many other threats to ‘Capitalism’, then much of this book is simply distasteful, probably bordering on propaganda.

From https://www.victorsperandeo.com/post...-and-the-fed-4

This (red wave) didn’t happen. Maybe the elections were rigged (again)? Despite this, I think fiendbear.com is an interesting read. It's probably dangerous to identify with a bear or a bull too much though.


Next: Alpha Trading by Perry Kaufman

I forgot to give you footnotes for Vic's book.

Alpha Trading: Profitable Strategies that remove directional risk (2011)

 

1. Perry Kaufman (PK from now on) wanted to write this after the tech bubble collapse in 2000 but finally got around to doing so after the subprime disaster of 2008
2. Investors should not be subject to the tremendous losses that the market serves up. And traders do not need to make a commitment to a long or short position all the time.
   
   Attached Image

   
   
   I think this is a mindblowing game-changer, but it took me a while to get there.
   
3. Unconscious risks
   1. If you trade more than one stock, each stock should have equal risk
   2. If you don’t do that you are saying that you think the trade with the largest risk is most likely to give you the best return
   3. If that’s true then you should only make one trade in the best item and forget about diversification

4. About process more than results
5. The book is for traders but not necessarily intraday traders
6. The strategies fall under ‘statistical arbitrage’
7. You cannot just believe that something works; you need to prove it to yourself.

Chapter 1 - Uncertainty

1. The US dollar strengthened against the Euro by about 15% prior to the crash of 2008
2. The USA originated the crisis but investors still moved their money to the USA for safety
3. The right decision is only known afterwards
4. If you can't manage risk, then your interim losses may be too big to ever see the profits.
5. In a crisis, Cash, or guaranteed government debt, is the only safe place, (but not PIGS or emerging markets)
6. Understand that you can't change the odds to have better than a 50% chance that you will profit from a price shock. Realistically, you would be lucky to have 50% of the price shocks in your favor
7. When more people hold the same positions, any surprise that is contrary to that direction will have a greater impact while surprising news in a favorable direction will have little effect.
8. Given how ill prepared and undercapitalized many traders are, one large price shock is all they need to be forced out permanently.
9. PK makes it clear that he is a quant from his first example, which is, to say the least, inscrutable.

“Using this chart, we choose two price-shock thresholds, 3.0 and 4.0, to compare the impact of what we will call more shocks and fewer shocks.
The fewer case is also larger shocks. We run a test of moving averages using calculation periods from 10 days to 200 days over the past 10 years. The rules are that a long position is entered when the moving average turns up, and a short is entered when the moving average turns down. The system is always in the market. A $25 round-turn commission is charged to cover all costs. Results are shown as Net PL in Figure 1.3, along with the net results of the 1-day price shocks. The performance pattern of the S&P begins with large losses for faster trends and finally shows profits for trend periods approaching 200 days.”Get that??

Despite the bewildering complexity of the statement, the conclusion seems solid enough.
Simple systematic trend following can perform well when traders can’t.

“Price shocks represent the worst-case scenario for traders. They are unexpected, violent, and most often generate losses. Even more important is that most traders don't plan for a price shock. You can plan to survive a price shock by holding large reserves so that a 4-standard deviation event will not produce a loss that you can't handle. But to do that, you need to give up leverage. More often than not, without the leverage, the result will be returns that are not justified by the risk.”

Algo traders are not immune to this. They test their systems on historic data that contain past shocks. There are countless new market surprises.

 

1. What can you do to avoid price shocks, even if you can't predict them?
2. The practical solutions are:
   1. Don't trade.
   2. Stay out of the market as much as possible.
   3. Choose a faster system so that you're not holding the same position as everyone else.
   4. Trade a hedged or market-neutral position.

3. No one reading this will pick ‘don’t trade’. However staying out of the market is very practical. If you hold positions for ⅓ of the trading days you have a 66% chance of missing a price shock. “By not holding the same position as everyone else, and getting net long or short frequently, you have a good chance of a positive price shock 50% of the time.”
4. Long holding periods are not compatible with a mean-reverting method because then you fight with the trend.

The Plan
“trading a spread or market-neutral position, which takes price shocks out of the picture completely. Of course, there is a chance that the two markets that are spread will not react as expected, but that is a very small chance. In addition, mean reverting strategies, which represent most of the market-neutral programs, are fast trading and require two or more markets to diverge; therefore, they are in the market for a relatively short period of time.”

Contagion risk
“It is the complexity of the markets that we don't understand, the way money interacts with it all, causing the possibility of sequential failures.”

Black boxes and counterparty risk

1. “If it is at all possible, you need to manage your own investment portfolio.
2. Your common sense is enough to keep it on a safe track.”

Taking Defensive Action
“We propose that the solution to all this risk and uncertainty is to trade in a way that removes exposure to directional risk. That include all forms of statistical arbitrage (stat-arb), from spreads to yield-curve trading to program trading, and from the simplest pairs trade to large-scale market neutral strategies. These methods extract alpha from the market, profits over and above what might be gained, or lost, by a passive investment in the stock or bond market.”

Not high-frequency trading
“Besides computerization, the edge in this method is that the cost of trading is near zero for these investment houses. We can't possibly compete with that, but we can compete in a different time frame.”

 

1. Pairs trading
2. Longer moves of 1-3 days
3. During extreme stress, markets move together, and we can take advantage of those opportunities, even if they last only three months or a year.
4. Data-driven methods
5. Fundamentals only define the strategy, such as relative-value trading
6. “we need to act often and precisely, which can be done only with a computerized trading program continually evaluating price changes”

Accepting performance for what it is

1. No matter how clever you are, you cannot eliminate risk
2. The only way to reduce future risk is to reduce leverage
3. Lower leverage means lower returns
4. If you find a past situation that caused exceptionally large risk, and create rules to eliminate it from future trading, you'll be following in the steps of Long-Term Capital Management. These risks never repeat in the same way and may even get bigger.”

Chapter 2 - the Importance of Price Noise

1. Market noise - up and down erratic price movement that goes nowhere but often causes you to be stopped out of a trade only to see prices reverse
2. Most price shocks are an extreme case of noise
3. If prices continue in the direction of the shock then it’s not noise, it’s structural change
4. How do you tell the difference? When is a move part of the trend and when is it just noise?

Noise explained

1. noise is a day with very high volatility but a close nearly unchanged, or a day when prices closed sharply lower, then reversed nearly the entire move the next day.
2. Noise is getting stopped out of a long position when prices break a key level, but that turns out to be the low of the move, and we're out at the worst price of the day.
3. Noise is a disorderly move; not necessarily volatile but erratic and unpredictable
4. Econometricians say that when you remove the trend, the seasonal pattern, and the cycle, the three main components of price movement, what you have left is noise

Calculating Noise

The net move from A to B, divided by the sum of the individual moves (1-7). All values positive.

1. In other words, the straighter the path, the less noise
2. This can be expressed as the ‘efficiency ratio’ (ER)

1. “Because the calculation is greatly dependent on the starting and ending values, some mathematicians consider it unstable. However, averaging the values over some period of price history minimizes that problem.”

Volatility neutral

1. Whether price moves up by $1.00 or $5.00 if it does so without retracing the ER is still 1.0
2. This means that a good ER is not necessarily sufficient for trading. A separate calculator could ‘easily’ solve this problem

Fractal Efficiency

1. Benoit Mandelbrot wrote The Fractal Geometry of Nature (W. H. Freeman, 1982)
2. Best describes the way prices move from one point to another

Calculating the ER
Kaufman promises us spreadsheets but I cannot find them on his website. So what do I do now? Painstakingly reproduce the formulae for every cell?
No, make my own spreadsheet
PK's formulas seemed to have some errors which I think I ‘fixed’ rather than make them worse, but if anyone notices otherwise, please let me know.

If you look at my spreadsheet you’ll see the noise level has possibly increased since PK compiled his figures, but to know if that’s a recent change or a gradual one, you’d need more data for the SP500 than I can easily find online.

 

1. The lowest 10 ratios in the first set of columns are all equity indices
2. That means the index markets as a whole are the noisiest of all markets traded
3. The Russell and SP500 are the noisiest
4. Newly trading markets and emerging markets with lower volume, dominated by a few large companies tend to have lower noise
5. New markets with low volume tend to be dominated by commercials
6. Commercials use the markets to hedge or to provide liquidity
7. Many commercials have the same view of the markets
8. The result is prices move in the same direction more often, with fewer and larger trades
9. Interest rates have the lowest noise
10. Of currencies, EURUSD has the most trend, and AUDUSD the most noise (still true? I guess I’ll have to make another spreadsheet)

What can we do with this?
“When we look at various trading strategies that hold outright long and short positions, the primary choices are trend following, taking a position in the direction of the current price move, and mean reversion, trading against the current move in expectation of prices correcting back the mean (average price). If prices move very smoothly, going from one price level to another with only small retracements, then a trend approach should work well. On the other hand, if prices keep changing direction and rarely sustain a continuous move in one direction, then mean reversion should be the preferred approach.

According to the ratios in Table 2.2, we should be using a trend strategy to trade the Eurodollars and all other interest rates but a mean reverting method to trade the equity index markets, with the exception of the DAX.”

And this statement is absolutely worth checking out.

However PK does his own test, quant that he is, if we’re willing to trust his data and assume that markets don’t change (too much) over time.

“For technical traders, we point out that using the direction of the moving average, rather than a price penetration, for determining the change of trend works best for longer-term trends. It reduces the number of false penetrations and greatly reduces the costs.” Does it though? This would surely depend on the period of the MA?


PK plots the profit factor against the Efficiency ratio of market indices. There does seem to be a clear line of fit (not shown) with higher ERs being more profitable than lower ERs. PK tells us we can also assume the low ER markets will be profitable with a mean-reverting strategy.

It’s quite interesting to see that profitability of a system may have just as much to do with the market being traded as with any system rules.

Time Frame Helps

1. a trend system is more likely to be successful if the calculation period is longer, for example, 40, 60, or 80 days,
2. and a mean-reverting system is best when using calculation periods from 3 to 8 days.
3. This could be why macrotrend strategies use calculation periods from 40 to 200 days, with most of them closer to 80 days. Mean reverting is often applied to intraday moves.

“noise appears to be a greater part of price movement when seen up close, and the trend is more visible when viewed from afar”

Volatility is still important

1. Without volatility individual trades may not move enough to cover the costs of doing business.

Robustness of noise measurement

Futures markets sorted by their average noise over 10 years based on four calculation periods (in days)

 

1. Again interest rates show the least noise (most trendy)
2. Longer maturities get noisier
3. The DAX is the least noise index
4. Palladium is consistently low noise

Or it was until 2020. I wonder how much of this data is cyclical and how much of what PK saw in 2011 is still in effect?

1. Attached Image (click to enlarge)

   
   
2. The 80-day calculation period shows a clear pattern where higher values (less noise) produce better returns, indicated by the higher info ratio. Anything below zero IR is a loss. The three diamonds at the far right are the short maturity Euribor, Short sterling, and Eurodollars. Is it any wonder these are rarely offered as CFDs by FX brokers?
3. The negative ratios are the equity index markets, 30 year bonds and gold
4. PK goes on to look at charts of 8-day efficiency ratios, and wonders if the trends he sees can be used to switch from a trending strategy to a mean-reverting one. He doesn’t answer the question though, leaving it as an exercise for the reader. Promising only that “this is an exercise worth some investment of time.”
   1. My own feeling about this is that it’s unlikely any such pattern or cycle will hold for long, especially once it becomes known. However, what the heck, it’s easy enough to create an ER chart and overlay it with a price chart, isn’t it?

Do you see any correlation here? Not enough data I think.

Let’s see what I can come up with.

Attached Image (click to enlarge)

The blue line is the Efficiency Ratio. The other line is the relative price of the index. I know lots of economists would find this interesting. Is it possible to say that when the ER starts moving out of its lows that price starts moving? Not enough data, I think.

Chapter 3 - Pairs Trading : Understanding the Process

1. Not an industry secret
2. Basic arbitrage
3. Choose 2 companies that are related
4. The best is between 2 competitors like Dell vs. HP
5. Prices may move apart but they will be similar
6. If the prices are too close there is limited opportunity
7. Be vigilant about structural changes in one of the companies
8. An earnings report is not a structural change
9. Sell the stronger stock and buy the weaker, expecting their prices to correct or return to an equilibrium close to each other
10. Exit when prices merge
11. The art is in selecting the pairs, the size of the distortion targeted, the position sizes, and exit criteria

The process (of developing a pairs trading strategy)

1. Keep it simple and robust
2. Think of problems that can be encountered in advance
3. Use data and tools wisely
4. An advantage of a rule-based system, once implemented for a pair using a spreadsheet or computer program, it can easily be extended to other instruments
5. If it works for Dell vs. HP it should work for others; this leads to confidence and sector diversification
6. All trades entered and exited on daily closing prices; actual trading is more flexible
7. Every time we encounter a problem we learn more about the process and the solution

The basics

1. Start by choosing 4 US airline stocks
   1. They react to the same fundamentals (intuitive belief)
   2. American, Continental, Southwest (the only domestic), US Airways
   3. Choose American and Continental because both had max data, both are major domestic and international carriers
   4. PK accurately predicts that one of these companies might no longer exist by the time we read this (Continental merged with United)

   
   

1. Prices are similar, 0.94 correlation
2. Correlations use price differences, not the actual prices, and differences are returns, and best calculated as percentage change
3. (Price_today / Price_previous) - 1

Time Perspective

1. Aim for a few big profits or many small ones?
2. PK prefers many faster trades, even though losses may end up the same
3. Faster trading allows for more consistent performance, because individual trade risk is smaller (but costs are higher)
4. Risk increases with holding time, increasing proportionally (not linearly) so it’s not possible to have a long-term trade with low risk
5. More trades gives greater statistical confidence
6. “operating in a shorter time period emphasizes price noise, and taking a longer view emphasizes the trend. Pairs trading assumes that differences between the two stocks will be corrected; therefore, we are dealing with noise. Using a shorter time horizon will benefit us.”

Note on Data and Costs

1. Make sure all the data is in the same format
2. Costs are not considered in PK’s examples; pro traders might pay less than $0.001 per share. YMMV

Correlations and Common Sense

1. PK notes that Southwest’s price pattern and business might not make it a good pairs trade candidate but the other 3 are. I thought we had already made our selections though? Why are we backtracking now?

Percentage Deviation

1. The basic approach to pairs trading is to look at the percentage differences in the daily moves of the two stocks,
   1. in this case AMR minus CAL.
   2. When the difference is too high, we sell short the stronger (AMR) and buy the weaker (CAL);
   3. if too low, we buy AMR and sell short CAL.

   
   

2. Based on our observation and the correlations, we can expect this divergence to correct within a few days.
3. Short selling might be a problem for some, PK promises to show us how to use sector ETFs as a ‘simple alternative’
4. To get a better understanding of the range of values we can expect from AMR - (minus) CAL, PK looks at the daily percentage difference between the two

1. The volatility of the AMR-CAL differences ‘changes drastically’ over 4 years
2. Trading before 2007 the rules might have been:
   1. Sell short AMR, buy CAL on the close when daily change in AMR exceeds change in CAL by 2.5%
   2. Sell short CAL and buy AMR on the close when daily change in CAL exceeds the change in AMR by 2.0%

   
   

3. This ‘threshold’ accounts for the upward bias we see in the chart, says PK, where max was 8% and min was 5%.
4. After May 2007 volatility increases; by mid-2008 max difference has become 17.5%
   1. Trading with the old rules ‘we would have all rushed for the exit’ when the trade reached 10%
   2. The basic method has too much risk and too much variability
   3. It may have worked in the 1960s but not today
   4. The skew in prices is because the stocks have been moving in one direction, or the volatility of one stock is greater than the other
   5. The safest approach will always be a symmetric solution

   
   

5. Changing volatility is a good lesson in why more data is better than fewer data for testing
6. When you see a structural change in the volatility, you need to consider a more dynamic way to adjust to the market.
7. “What is needed is a way to adapt the entry levels to changes in market volatility. We not only are concerned with increased volatility and the associated increased risk but also realize that if volatility falls, as it did in 2006, and we're waiting for a 2.5% difference for an entry trigger, then we could wait months before seeing a new trade.”

Relative Differences

1. The solution to adapting to changing volatility
2. One way to recognize relative differences is using a momentum indicator (RSI, stochastic, MACD)
3. PK chooses the stochastic, and claims it has less lag, specifically the ‘raw stochastic’ and he gives this as the formula for its calculation

1. Where Ct is today’s close
2. min(Ln-days) is the minimum price over the past n days
3. max(Hn-days) is the maximum price over the past n days
4. The denominator is the max to min price range of the past n days
5. PK claims most trading software show a slower version of the stochastic that uses a 3-day average of the raw stoch, that subsequently takes a 3-day average of that result producing a 4.5 day lag.

1. 14-day stochastic for both stocks
2. Both markets move in a similar way between 0-100
3. However they don’t reach their highs or lows at the same time
4. This phase difference is where the opportunity lies, without it there would be none
5. The ‘first step’ (what have all these other steps counted as? zero??) is to show the phase differences more clearly

Exits

1. Should always be at a stochastic difference of zero (equilibrium)
2. Also consider exiting shorts above zero and longs below zero. This cuts profits short (naughty!) but assures a safe exit more often
3. “There is always a temptation to hold a short position until the stochastic difference moves from the high entry to the low point, where we would reverse and enter a long position, for example, from a stochastic value of 80 down to 20. Profits would be much bigger and transaction costs less important. But that's not the way the market works. A relative distortion, as we recognize with the momentum indicator, is likely to return to near normal but has no reason to reverse. In our 6-month example, CAL tends to lead AMR, then fall back to normal, and then lead again. We would be exposing ourselves to very high risk unnecessarily if we waited for AMR to lead CAL in order to exit a long position.”

This chapter is a mega-chapter, and we are not nearly done. Next: implicit bias.

Implicit Bias

1. Looking at fig. 3.5 (above), we see if we sell above 45 and buy below -45 there is one short trade and four long trades.
2. If we choose ±25 there will be a lot more trades, but it would force us to hold those trades with larger unrealized losses
3. This is a classic trade-off that will be considered later
4. The strategy rules presented will not consider asymmetric parameters despite the fact that there has always been a bias in the stock market; history has shown a steady increase in the average price of a stock or an index
5. “A bear market begins when the DJIA turns down by 20%, and a bull market begins after the DJIA turns up by 20%. However, after a decline of 50%, a rally of 20% is actually a recovery of only 10% of the value lost in the downturn. Then the threshold can be twice as large to enter a bear market as a bull market, a definite bias toward the upside.”
6. “The rules in this strategy, and others given later in this book, will all use symmetrical thresholds. A short sale signal will occur when the stochastic difference moves above 40, and a buy on the first day that the stochastic falls below –40. Exits will initially be at retracements to zero.”

Results for Stochastic Differences
“In this first test, the threshold values of ±40 generated only five trades in a year. We would prefer to trade more often.”

Different Position Sizes

1. The sizes of the CAL positions are different from the nominal AMR position of 100 shares
   1. That’s because a volatility adjustment was made
   2. the size of the two positions will be an important way to control the risk and improve the chance for a profit

   
   
   

2. To find the vol-adjusted position size
   1. Assign a fixed size to one leg
   2. AMR always trades at 100 shares
   3. Calculate the ATR of each leg measured over the same n days
   4. If the ATR of AMR was $0.50 and the ATR of CAL was $1.00 trade twice as many shares of AMR as CAL
   5. This is a critical step in trading two markets simultaneously, because they must each have the same risk exposure; if missed there is no way to correct it later

   
   
   

3. PK shows us what happens if we don’t correct for volatility, where the original test had a $350.89 profit and $0.47 per share, the results of trading 100 shares for both stocks returns $178.00 with $0.18 per share
4. PK also points out that it depends on whether the leg with higher volatility was the one generating profits; however on balance it is better to adjust for volatility each leg of the trade.

Alternate Approach to Position Sizing

1. The idea behind vol-adjusting position sizes is that every trade should have an equal risk. If we don’t do this, as PK has said, we are implicitly assuming one trade is better than the others and in that case we should just put our eggs in that basket.
2. In the first example he sized one leg at 100 shares, and then found the number of shares in the other leg that equalled that level of risk
3. A different approach is to assign an arbitrary investment size to each stock, say $10,000 and then
   1. Calculate the ATR of the stock price over the past 20 days
   2. Divide the nominal investment size by the ATR
   3. The result is the number of shares needed to equalize the risk of the two stocks

   
   
   

Testing a Range of Stoch Values

1. PK says it’s impossible to avoid optimizing now (!)
2. He wants to increase the number of trades, and the returns, of course
3. He assumes
   1. As the entry thresholds are moved further apart
   2. We get fewer trades
   3. The profits get bigger
   4. The risk gets larger because
   5. The holding period is longer

   
   
   

4. If we hold the entry levels constant and move the exit points closer
   1. Trades should increase
   2. Profit size should reduce
   3. Overall risk should decrease because the holding period will decrease

   
   
   

5. PK also changes the calculation period for the stochastic indicator
   1. He shortens it
   2. This will produce more signals
   3. Faster signal frequency means smaller moves, smaller profits
   4. Shorter time frames means more noise
   5. This should favour a mean-reverting method

   
   
   

Using in-sample and Out-of-sample data

1. When developing a strategy from a new concept
   1. Divide the data into in-sample and out-of-sample partitions
   2. Test all the new concepts on the in-sample data, until satisfied with rules and results
   3. Run the best rules and parameters through the unexplored out-of-sample data
   4. Expect the results to be worse, but hope they are not, for a variety of reasons not the least of which is that there are more patterns than we can possibly foresee
   5. If the return to risk ratio on in-sample was 2.0
   6. If the return to risk ratio on the out-of-sample was 1.2
   7. That’s a success
   8. If the out-of-sample test is a complete failure, with a ratio near zero, the method is also a failure
   9. You cannot review the new results, find the problem area, and fix it, because that is feedback
   10. Improvements will simply be overfitting the data

   
   
   

Which parameter to test first?
Are these results robust?
PK goes into this section in some detail but his results are very specific to his trades and this period of time. I don’t know if we can take any generalized/universal lessons from the results that he gets. As he says himself, we could use this to convince ourselves that this is a cromulent way to trade, but we must never take anyone else’s word for it, we have to test it ourselves.

The next question is whether the results he’s obtained are ‘robust’ meaning more than just the results of random chance. Do they have a sufficiently low p-value?

Pros

1. Pairs trading is a basic and believable concept
2. Varying values allows for control of number of trades and holding periods

Cons

1. Combinations of parameters have been tested
2. Some are likely to be profitable after enough testing, but statistics tells us that “a small number of profitable results within a larger set of tests do not have predictive qualities.”
3. Fewer trades than desired
4. Some results show small net profits and even some losses

Consider %age of profitable results over all tests

1. A large percentage would tell us this method is sound even though some returns are small while others are large
2. Apply this method on other sectors with similar fundamental relationships
   1. Similar results would contribute to improved confidence
   2. The knowledge from those new pairs can be used to diversify

   
   
   

PK concludes there is nothing wrong with his results, but they are ‘not sufficient to draw a conclusion’. (not enough data!) He also would prefer a pair that trades more often.

Target volatility

1. PK notes that the standard deviation of returns is 12% for all his tested pairs
2. This is ‘target volatility’
3. To equalize the risk of each pair relative to another, scale the number of shares traded in each pair to a level that represents a target volatility
4. 12% target vol = annualized std deviation of the daily returns = 0.12
5. To get this number
   1. Record daily net profits and losses of both legs of the pairs trade
   2. Find the std deviation of the entire series of profits and losses
   3. Multiply standard deviation by square root of 252 in order to annualize
   4. Investment size necessary to trade pair z with 12% volatility is
      1. Investment(z) = annualized standard deviation / target volatility

      
      
      

   5. If the standard deviation of daily returns = $100
      1. Annualized volatility = $1,587
      2. Target vol of 12% needs an investment of 1587/0.12 = $13,229 for that pair

      
      
      

   
   
   

6. This works well for comparing performance of individual pairs but doesn’t work for combining pairs into a portfolio
   1. To do that we need the same performance volatility for each pair relative to the same investment size
   2. Choose an arbitrary investment size. If the amount is $100,000 and there are six pairs, each pair gets ⅙ or $16, 666
   3. Divide the dollar value of daily returns by investment size to get %age daily returns
   4. Find annualized volatility of the returns: standard deviation x square root of 252
   5. Divide target volatility, for example 12%, by the annualized volatility, giving us the adjustment factor, AF
   6. Multiply all position sizes for each pair by AF
   7. This adjusts all position sizes for all pairs to create the same risk for each trade

   
   
   

7. A target vol of 12% means that there is a 16% chance to lose more than 12%, and a 2.5% chance to lose more than 24% over one year
8. Trader comfort range could go as high as 17% target vol and for fund managers as low as 6%
9. Trading stocks doesn’t ensure that with a target vol of 12% the system performance will permit that much leverage

Filtering volatility
Ok, time for a recap

1. Instead of picking buy and sell thresholds based on absolute price differences between two stocks
2. PK uses the difference between two stochastic values to normalize volatility and make buy/sell levels adaptive
3. PK also uses volatility to determine position sizes in the two legs
4. This prevents the returns of one stock from overwhelming the other when one of the stocks has much greater volatility

Now, consider the relationship of profits to volatility

1. If price volatility is too low, it will be impossible to produce a profit
2. On the other hand if volatility is so great there might be a point where the risks outweigh the returns
3. PK goes through a lengthy and detailed analysis of the best volatility measure but he sticks with the ATR in the end, as it is ‘more intuitively robust’.
4. Is performance dependent on volatility? Will we be more successful during periods of low or high volatility?
5. Again PK goes through highly technical contortions to answer these questions, including devising a low volatility filter, and a high distortion filter (with a distortion ratio).
6. In the end he is unsure if his results (improved only via the high distortion filter) are due to overfitting or not.

What we learned

1. Low volatility is not good for this strategy
2. Filtering the volatility removes too many trades
3. Finding too many solutions to the emerging problems will result in overfitting
4. The market doesn’t hand profits to you
5. The airline sector is too low volatility and another sector needs to be examined
6. PK thinks he may consider the housing construction sector

And then he does. For pages and pages,

1. He reproduces the previous steps of the airline comparison for use with construction companies.
2. He asks again if he is overfitting the data.
3. He introduces the concept of pseudo-leverage, as if the original leverage concept is not complex enough.
4. He finds the low volatility filter isn’t as useful as he thought it would be.
5. He questions whether what he has found is an anomaly, and considers capping his results.
6. He discusses the statistical problems of zero-value returns, cross-margining, and counterparty risk.
7. He looks at ETFs instead of stocks.
8. He applies capping to ETFs.
9. He uses ETFs as substitutes for short sales.
10. He builds a portfolio of home builder pairs, and puts it together,
11. analyzing the NAV using annualised rates of return.
12. He discusses execution,
13. He debates the use of stop losses. Interestingly, he is not a fan of using stop losses. They alter the balance of the system and don’t guarantee profitability.
14. He discusses intraday trading, saying that trading after economic or corporate announcements could be an advantage

Wow. It’s a lot. I respect PK for not dumbing things down for the mass-market, but is this actually useful for traders without a lot more background education/experience?

Key points

1. This chapter is as much about the process as about the strategy. No kidding! A lot of editing would have made this much clearer, more readable, and more useful for the novice.
2. We started with a premise, believed to be sound
   1. 2 stocks in the same sector, affected by the same macro conditions, will perform similarly
   2. in-sample/out-of-sample data procedure was skipped (thank goodness)
   3. Instead the method was tested on one market and one sector, then applied it to another market in another sector. This approach is weaker, but sufficient.
   4. Markets were picked for liquidity
   5. Cross-correlation calculations were not found to be useful
   6. Trading rules were calculated showing relative differences between pairs
   7. Position sizes were volatility adjusted
   8. Tests on multiple pairs looked at average results for all pairs, not individual results
   9. Robustness was confirmed because all combinations of parameters were profitable for average results, and was continuous when parameter values were increased or decreased
   10. Perfect performance is not needed for profitability. Some losses were left in and not optimized away.
   11. Volatility adjusted each pair to a target volatility
   12. Tested a low volatility filter
   13. Trading at target volatility exceeded investment size on about ⅓ of days
   14. Thus the size of positions on those days needed to be capped, by scaling down the position size to satisfy the cap, and adjusting the daily returns by that ratio. This improved returns.
   15. “Using the capped profits and losses, we found the new investment size and created return series for each pair, each with its own investment size. We averaged the daily returns and found that the risk had dropped by about 40%. We created the new portfolio NAVs from the aggregate investment size and the average daily returns. Because of the nature of stock investments, we were unable to leverage our returns to the target volatility and needed to settle for about half of that, or 6%.”

After all that, we don’t even get our 12%!

Chapter 4 - Pairs Trading Using Futures

1. Pairs trading ‘clearly works’
2. The returns on many stocks are small and it demands good execution (recall Sperandeo - never trade if it requires good execution)
3. Airlines are not volatile enough
4. Homebuilders are only marginally better
5. Filters improve results but at the cost of fewer trades
6. Capping improved gains, and reduced risk - unexpected bonus
7. Pairs trading is robust because it works across a wide spectrum of parameters
8. What we need is more volatility or leverage
   1. We could get that by borrowing part of the capital needed to trade
   2. If interest rates are low and returns are high, borrowing makes sense
   3. We could also use stock options
      1. No restrictions on going short
      2. By selling (writing) one leg we receive the premium to offset the cost of buying the other leg
      3. We need to evaluate the slippage, or bid-ask spread combined with the net premium of both legs to know if options are viable
      4. That’s not PK’s focus

Futures

1. Satisfy all liquidity and leverage problems
2. Fewer choices in the markets that can be traded
3. Trading hours are different in NY vs. London
4. No matter when you buy (PK has been buying at the close) expect to pay a bid-ask spread
5. For pairs on the same exchange, spread orders are the way to go, to guarantee a minimum differential between the two prices
6. However with one leg in New York and one in London this isn’t possible
7. It may be safer to trade a few minutes before close to control the fill price
8. “It's necessary to be sure you wait for a spread difference large enough to absorb a poor fill and still net a profit”
9. For the equity index markets, which we will apply in these examples, only the U.S. and European markets will be used
10. To compensate for holidays: “If either market posts no price change, we cannot enter or exit a trade.”
11. Best performance is when we trade a US against a Euro market
    1. Globalization pushes traders to move markets in similar directions
    2. Traders in Germany don’t want to wait til 10pm to trade the US market

Check the data

1. Futures markets have day sessions, night sessions, and overnight sessions (how much of this is still true? I don’t know because I don’t trade futures)
2. “Big point” or “handle” is the insiders’ way of saying that the price to the left of the decimal point has changed.

Markets close at different times

1. PK outlines the differences between various market opening and closing times
2. I don’t know how accurate any of this is in the 9 years since the book was published. It’s fairly complicated though, especially when you consider the difference between closing prices and settlement prices.
3. You'll need to be sure that you have a data series that has the last price instead of the settlement.
4. when actually trading [...] pairs, entries and exits to both legs must be done simultaneously, when both markets are trading

Mechanics of a Futures Pairs Trade

1. No distinction between long and short sales
2. No need to set new positions or cover previous shorts
3. No uptick rules, no restrictions on shorts
4. No need to borrow stock
5. Leverage trades without borrowing funds
6. Earn interest on money on deposit in your account to offset commission costs
7. Margin is not the same as with stocks - it is just a good faith deposit
8. For most trades that is about 10% of the contract value
9. Putting up $10k to trade a gold contract with gold at $1100/oz is the same as buying $110k in assets at a leverage of 11:1; the leverage is actually lower because the broker will ask for more in the event the trade goes wrong
10. Futures requires margin to be restored to the full amount once balance falls below 75% of the initial margin
11. Brokers may want a 20-30k deposit just to trade one contract
12. You are responsible for all losses even if they exceed the amount on deposit in your account
13. “A typical investment manager will keep leverage to 4:1. Then, for one gold contract, they will have $40,000 on deposit, earning either money market interest or 3-month T-bill rates on up to 90% of the balance.”

next:
Example of trading signals for futures

Example of trading signals for futures

1. PK uses a heating oil vs natural gas pair
2. Two profitable trades but with large equity swings
3. Using a 14 day calculation period
4. Enter when the momentum difference exceeds ±50 and exit when the momentum falls to 10 for shorts and rises to –10 for longs

Why are there so many columns if this is just two trades?

 

1. Trade 1
   1. Lasted only two days
   2. momentum difference on 6/9/2006 is –58.37, below the –50 entry threshold
      1. Difference between NG momentum of 29.55 and HO momentum of 87.93
      2. NG is oversold; HO is overbought
      3. Entry prices are closing prices, but back-adjusted

   3. The following day NG momentum rises, HO drops, resulting in net difference of -0.53
      1. An exit is triggered because the momentum diff is above -10 threshold
      2. Most of the profit is due to HO, net gain of $32,040
      3. Position size was determined from vol calculation, the 14 day ATR
      4. Same period used for the stochastic
      5. On June 9 NG had an average daily range of $2837
      6. HO a daily range of $2424
      7. With lower vol HO will get more contracts than NG, -12 to 10

   4. Second trade on 6/14/2006 is more volatile
      1. NG now OB with stoch at 82.80
      2. HO now OS with stoch at 14.52
      3. Volatility is nearly the same as 3 days prior, so buy 10 NG and sell 12 HO
      4. First day NG jumps 3.85%
      5. HO gains only 0.02%
      6. Net loss of $62,641
      7. Then both markets work back for a net gain of $43,919 when momentum diff goes negative
      8. PK points out how such a trade could not use a stop-loss order, as it would exit the trade at the worst time, locking in a large loss
      9. Natural performance profile is a few large losses and more smaller profits

Energy market trade setup

1. PK decides to focus on energy markets because of their ‘tremendous interest’ in the past 2 years
2. When trading stocks the basic unit size was 100 shares for leg 1; the second leg’s position was modified depending on volatility in order to equalize risk
3. For futures the starting size will be 10 contracts for leg 1. The second leg will be adjusted the same way. 10 contracts doesn’t give the same accuracy as the 100 shares example.

Margin and leverage

1. Each crude oil contract is 1,000 barrels (bbl)
2. Current price is $75/bbl
3. Contract size is therefore $75,000, the minimum trading unit
4. Because of margin you don’t have to put up the full amount, only 10%, or $7,500 of the contract value
5. If crude goes up by $1, you gain $1000. On a $7500 deposit that’s a return of 13.3% This can easily happen in one day.
6. However if price drops $1 you lose 13.3%
7. If price rises to $85 (possible) you have earned 133% but if it drops $10 (equally possible) you lost your deposit of $2500 and must pay the broker the remaining $2500 within 24 hours. To continue trading you must deposit more money.
8. Most of the time your deposit should be high enough to cover most ‘normal’ moves
9. You might also be required to deposit $25,000 and only trade one crude contract. Only big banks are given enough latitude to lose all their money.

Trading energy pairs

1. For simplicity only the US primary energy markets will be traded, Crude Oil (CL), heating oil (HO), reformulated blendstock (RB) which is the precursor of gasoline, and natural gas (NG)
2. The RB contract changes often due to environmental regs. Watch the volume to see when a new contract is more active
3. Commercials can trade the ‘crack spread’ which involves buying CL, and selling RB and HO in a 3:2:1 ratio. This gets a favorable margin from the exchange. If producing margins are low they can do a ‘reverse crack’ where it sells CL and buys the products.
4. “It is generally unproductive to trade the crack and try to compete with commercials at their own game.” So only spread pairs and decide positioning based on volatility.
5. HO contract is 42k bbl, RB is 42k bbl, NG is 10k BTUs
6. If HO moves from $2.25 to $2.26/gallon the profit/loss is $420, same for RB
7. If NG moves from $6.500mmBtu to $6.600mmBtu, the profit/loss is $1000
8. Smallest moves in each instrument then is, CL - $10, HO - $4.20, RB - $4.20, NG - $10

Trading Hours

1. All four futures markets have a pit session that opens at 9:00AM, and closes at 2:30PM ET; there is also an ‘electronic session’ that starts 6:00PM and closes the next day at 5:15PM. PK speculates that the markets will soon be all electronic.
2. Since this is likely outdated info I’m skipping the rest
   

Next: Fundamental Background

Fundamental background

1. HO and NG both have a role in home heating but most people can’t switch between sources
2. HO is also diesel fuel; there is no potential arbitrage between EU and NA markets because shipping costs/time
3. HO and RB are refined products of CL
4. Production of HO increases in early summer to build inventories
5. RB production increase in april in anticipating of ‘driving season’ (??) which starts memorial day in the US
6. RB price increases during high-demand summer season
7. HO price increases beginning winter
8. CL has no clear seasonal pattern; but susceptible to geopolitical risk, manipulations by OPEC
9. CL follows changes in the USD
10. Attached Image (click to enlarge)

    
    
    Despite fundamental differences, energies move together
11. The lead and lag in these moves provides trading opportunities

Revisiting momentum with energy markets

1. Energies have been rising for 2 years (since 2009)
2. CL topped at $150bbl
3. Attached Image (click to enlarge)

   
   

Opportunity and risk

1. During a crisis, money moves the market, not common sense
2. Attached Image (click to enlarge)

   
   
3. Attached Image (click to enlarge)

   
   
4. “Normally we would want to use the percentage changes, but most futures long-term data is continuously back-adjusted from individual futures contracts. In the worst cases, where there is a significant price gap between the contract that is expiring and the next contract to be used, the aggregate back adjusting can cause the oldest prices to become negative, or near zero. For those markets, percentages don't work. For commodity prices such as energy and metals, cash prices offer a good alternative; however, using differences will give similar results if you stay aware of the data problems.”
5. HO and RB are always highly correlated to CL prices
6. A long-term correlation of 0.351 for CL and NG shows some correlation, but it is not clear whether they would be profitable for pairs trading
7. Attached Image (click to enlarge)

   
   
8. It does seem to be a rough correlation at best. Maybe for the very patient?
9. PK points out that the lower correlation can be more profitable but also riskier.
   

Next: Trading Energy Pairs

Trading energy pairs

1. Once again PK pretend-trades these pairs using the stochastic momentum values but this time charges a $25 cost per contract
2. “We calculate the stochastic momentum values of the two futures markets separately over the same time period, subtract the two values, and test that difference against an entry threshold.”
3. After testing PK finds that
   1. “the pairs made up of crude and its products all fail to generate profits after costs.
   2. On the other hand, the natural gas pairs were all successful, net of costs, for all combinations of calculation periods and entry thresholds except the fastest, 4 days.”
   3. He considers numerous factors, including maximizing number of trades, profits per contract, indicator periods
   4. He mentions that he hasn’t considered exit thresholds which could only complexify this process
   5. There is no best choice, only trade-offs and trader preference. Those options are left to the reader.”
   6. For that reason I won’t go into these results in much detail.

4. Attached Image (click to enlarge)

   
   
5. PK goes on to create a ‘miniportfolio’ of NG pairs and compares them to see if there is a diversification benefit. I won’t go into as much detail as he does. But the steps are
   1. Calculate the daily P&L
   2. Align the P&L streams (if trading markets in different timezones, the dates need to be cleaned up so that each leg is in the right cell in Excel)
   3. Target volatility and investment size (as seen)

Energy summary

1. Only 3 tradeable pairs found (NG vs CL, HO, RB)
2. Good news is not many trading methods hold up across different markets, especially moving from stocks to futures
3. The CL products pairs were removed from consideration based on ‘market experience’ which must ‘always play a role’
4. “We need to look at markets in which there is a fundamental relationship, or a psychological one, and the correlations are less than 0.80.”
5. The most profitable pair was NG-CL, which was a surprise as NG-HO was expected to be the best (was still profitable) because they’re both home heating products
6. Sloppy correlations can provide more opportunity
7. Returns are better when volatility and prices are higher

Inflation pairs : Crude, EURUSD and Gold

1. If only we could trade only at the most extreme market moves and avoid the other times
2. Volatility might be the key to recognize a change in price regimes
3. However there is always a lag in recognizing a change and usually during that time we give up more than we gain by switching
4. “The key to profits in trend following is the fat tail, the occasional very large profit from an extremely long trend that offsets many small losses that came before. If you use a stop-loss with a long-term trend, you exit the trade with expectations of saving money, but the trend is not over; that is, it hasn't changed direction, and it may only have taken a mid-trend correction. If you're wrong and the trend stays intact and eventually becomes one of the few big winners, you've lost your chance at net profits.”
5. The US gov measures inflation without energy but it’s really the best inflation measure there is
6. The relationships between these three markets strengthens when inflation is in the news. And it is very much in the news now. Let’s see if this theory bears out!
7. Attached Image (click to enlarge)

   
   
   
   Components of inflation. EURUSD, crude oil, and gold prices using back-adjusted futures.
8. All 3 markets peak mid-2008, drop fast, then rally through 2009
9. To find out if we should trade these markets, we need to answer some basic questions:
   1. Would pairs created from these three markets have made money using our strategy during the past three years?
   2. Could we have known when to start trading them?
   3. What parameters would we have used?

Different values, different volatility

1. Each of the three markets has different contract sizes, therefore, different risk
2. EURUSD futures contract has a face value of $125,000
3. CL is 1,000bbl x $80 = $80,000
4. 100 troy oz of XAU x $1,200 = $120,000 at the current price in May 2010
5. Volatility expressed in dollars per day is easiest way to use common term

Holidays

1. The systematic program should not trade if one market is closed
   1. Assume no trade if data is omitted
   2. Assume no trade if data is duplicated (forward-filled due to market close)

Results from 2007 through 2009

1. Again PK tests using a variety of settings but this time he adds exit thresholds
2. Entry is ±50
3. Exit threshold greater than zero means an earlier exit. So if it’s 20, the stochastic only needs to drop below 20, not zero
4. This means, earlier exits, more trades, smaller profits
5. 15 of 18 tests were profitable; all the unprofitable tests used the longer 14 day holding period
6. The better ratios are at the 5 day calculation period
7. Exit threshold of ±10 is “very good, with a ratio of 0.728, but the exit of ±20 is marginally lower and would get us out sooner. Therefore, we expect to trade these pairs with a momentum of 5, an entry of ±50, and an exit of ±20.”
8. Crude-EURUSD
   1. Most consistent, with profits in every combo and ratios from 0.3 to 0.9
   2. Momentum of 5 and exit of 10 seem ‘reasonable and somewhat typical’

9. Crude-Gold
   1. Overall a poor performer
   2. Needs to be eliminated

10. EURUSD-gold
    1. Widest range of performance
    2. Highest average ratios
    3. Momentum 5 and exit 10 from lower periods is safest

11. Attached Image (click to enlarge)

    
    

Next: Could we have known when to start trading these pairs?

1. “Figure 4.10, the annualized volatility of the three futures markets is seen to change in late 2007, synonymous with the spike in crude oil but a full nine months before the subprime crisis. Had we started trading these pairs at that time, we would have captured the big moves in all three markets. By September 2009, the volatility of the EURUSD and gold had dropped back to previous levels. Crude followed in December 2009. Had we stopped trading when the volatility returned to normal, we might have netted profits in all three pairs.”
2. Higher volatility seems to generate higher correlations, and increase liquidity

How to decide what parameters to use in advance?

1. The most difficult question
2. We can look at another period when inflation and volatility were in the news and test that data
3. PK tries to compare apples to oranges, looking at the 1980s, but he admits gold is an unreliable indicator of inflation, and the Euro didn’t yet exist
4. “There is often a point in developing a trading system when you must make a leap of faith after you've done as much work as possible. Tying performance to volatility seems to be a very small leap.”

Last word about inflation pairs

1. “Markets that have caught the interest of the general public can offer great opportunity, triggered by a clear increase in volatility.”
2. PK observes that past correlations didn’t correspond to the best performing pairs
3. “If the correlations don't tell us anything, (they don’t!) then we can only conclude that it's the money that moves the market.”
4. Attached Image (click to enlarge)

   
   

Equity Index pairs
By now this shaggy dog story is beginning to sound familiar. Let’s cut to the conclusions, shall we?

1. Faster trading is better
2. Higher Info ratio, and lower correlation = better performance
3. “Within Europe, the most profitable pairs are between the British FTSE and the German DAX or the EuroStoxx.”
4. The only US equity indices to ‘make the cut’ are the Russell-NASDAQ, but they have a high correlation of 0.933
5. None of the momentum differences between S&P and Dow e-mini reaches ±40.
6. Unsurprisingly S&P and DJIA don’t work as they contain many of the same stocks
7. Attached Image (click to enlarge)

   
   

London Metals Exchange Pairs

1. Despite many similarities, and lots of work, the results are dismal
2. Leverage is dangerous. Cap it.
3. Attached Image (click to enlarge)

   
   
4. Who is PK trying to convince here, himself or us?

Volatility Filters

1. Basic pairs trading concept
   1. Enter when two prices are relatively far apart (and we have reason to think they’ll come back together)
   2. Exit when they do
   3. Adjust the two legs to have equal risk and equal opportunity
   4. Worked well for housing sector and equity index markets
   5. Metals markets don’t work well under extreme volatility
   6. A more volatile leg could overwhelm the profits and losses, causing risk balancing to fail
   7. “You may rationalize the results and say that we should expect those differences and that if the threshold is only shifted up or down by some small amount, then the answer is still valid. It may not be possible for anyone to say whether that is right or wrong. The market will tell you.”

   
   
   

Interest Rate Futures

1. US: Eurodollars, 5 year notes, 10 year notes, 30 year bonds
2. Euro: Euribor, Eurobobl, Eurobund, Gilt, Short sterling
3. Stronger correlations than between equities
4. Stronger correlation means lower profits in the other examples
5. However PK says there is opportunity when one leg is US and the other is Euro
6. He spares us the analysis in this mega-chapter though, saving it for Ch. 7, (a digestif?)
7. “To implement this, you will need to put this into a spreadsheet or computer program and verify all the results. You cannot rely on anyone else's numbers when it's your money that is at risk.”
8. Understand the process, do the calculations, place the orders with precision

Chapter 5 - Risk-Adjusted Spreads

1. Mean-reversion isn’t the only way to trade without directional risk
2. Sometimes two related markets move steadily apart - like when two competing companies experience varying fortunes because one company has a better business model
3. For ex. Dell vs Compaq
4. Compaq was an early success story
5. Dell ate Compaq’s lunch when it began to sell computers with no retail outlets

Dell and HP

1. Share prices have been flip-flopping for the past 10 years
2. A clever fundamentals trader can pick out which company has a better business model
3. But PK’s focus is purely on systematic algo trading
4. A basic way to track the market is with a 200 day moving average
   Attached Image (click to enlarge)

   
   
5. Simply buying when the trend turned up and selling when it turned down would have been a successful strategy
6. But if you did that, from 2003-2005 you would have been long both companies
7. And in 2001, and 2008-2009 you would have been short both
8. PK claims this means half the time you were exposed to directional risk
   1. The point of the book is to avoid directional risk!

9. Attempting to trade only when the two stocks are trending in opposite directions, would give you hedged positions, create a lot of small trades, and many losses.
10. The averages look smooth but the eye is easily deceived; during ranging markets there are plenty of ‘wiggles’, causing false signals
11. “The better way to look at the Dell-HP trade is by using the ratio of their prices, in this case Dell divided by HP. Some analysts use the difference in prices, but that creates a very different picture. For example, when Dell and HP were both $25, the ratio would be 1.0 and the difference would be zero. If Dell moved to $50 while HP remained at $25 then the ratio would be 2.0 and the difference $25. The ratio indicates that Dell is twice the price of HP, even if Dell was $100 and HP was $50.”
12. the ratio remains a percentage measurement and offers more consistency
13. Attached Image (click to enlarge)

    
    
14. The ratio performs better than the difference
15. Net returns using differences are likely to be a large loss
16. To trade the ratio, we buy Dell and sell HP short when the trend of the ratio is up, and sell Dell short and buy HP when the trend of the ratio is down.
17. The positions must be balanced according to their volatility to maximize diversification and reduce risk
18. Volatility was calculated using the average true range over the past 120 days
19. HP was more volatile than Dell; consequently, it has only 79 shares compared with Dell's 100
20. “The percentage returns over less than nine years were greater than 11% per annum.
    1. More important, the big picture satisfies our general concept of when we would go long and short these two stocks, given a strategic approach to trading.
    2. And, of course, there is no directional risk.”

Trading both long-term (hedged) trends and short-term mean reversion

 

1. In general any calc period < 10 days is targeting noise
2. Periods > 30 days are targeting the trend

Balancing Fundamentals and Technicals

 

1. Both can be good or bad
2. If you have an opinion on the direction of the market then use technicals for timing
   1. For ex. You believe the US dollar will decline because of debt and Asian econ strength
   2. Buy EURUSD or sell USDKRW but only when the trend turns down
   3. If you’re wrong, the trend will turn up and you can exit
   4. As long as you believe the dollar will weaken you don’t go long but wait for another chance to go short
   5. You’ve got a risk management plan on top of a calculated decision

Gold, Platinum, Silver

1. A relationship studied for decades
2. Used to be XAU:XAG ratio was 35:1; or 1lb of meat for 1oz silver
3. Now (then) gold is $1200/oz and silver $20/oz; 64:1
4. Is it adjusting to a new level or is it a false concept?
5. “If you don’t like this explanation you can substitute your own.” Financial commentators always sound smart when explaining what happened yesterday.
6. Holding gold to back up a currency is not as common as it was
7. Gold was fixed at $35/oz before Bretton Woods
8. 1970 the Hunt bros. tried to corner the silver market
9. Silver moved from $11 to $50 peaking Jan. 1980 before ‘one of the greatest collapses in history”
10. Gold rose and plunged with it; at the peak the ratio was 16:1 showing silver was the driver
11. The general public always seems to have the largest position at the worst time
12. Silver never recovered from the crash
13. PK examines the gold/silver ratio and concludes that it’s too risky because there are large intervals where the ratio trends up or down

Platinum / Gold ratio

1. Platinum is a precious metal “unlike silver”
2. So the relationship should be more stable
3. Both have industrial uses
4. Attached Image (click to enlarge)

   
   
5. Keep in mind this is being written in 2010 for publication in 2011
6. Gold 2 years later

But the Platinum chart is even crazier.

And in modern times it doesn’t seem to be highly correlated?

1. PK thinks the platinum/gold ratio is better than gold/silver

Trading the platinum/gold ratio

1. Trading is easier than stock pairs because the only components are
   1. Trend of the ratio
   2. Position sizes

2. 60 day moving average of the ratio is calculated
3. Buy the spread (long XPT, short XAU) when the ratio turns up, sell when it turns down
4. Equalize the two legs for volatility
5. 10 contracts for leg1, adjust for leg 2
6. Over 10 years, trading the ratio would have resulted in losses from 2002-2006 but then big profits afterwards
   Attached Image (click to enlarge)

   
   

Using Volatility

1. To expect a trader to start trading this in 2000 and endure through 2006 is not realistic
2. The solution lies again with volatility
3. Inflation scares create volatility that improves performance - windows of opportunity that surface during critical economic times
4. Abnormal increases in volatility signal stronger correlation between markets and a potential profit
5. The opposite is listless price movement; risk may be low because movement is quiet but transaction costs then take their toll
6. PK claims to see a clear picture of higher volatility starting in 2006, using the ATR. And if you trade starting in 2006 there is a %10 per annum return with %12 vol

Implied yield

1. Some commodities are pure carry markets
   1. Forward prices always reflect the cost of holding
   2. Cost is interest on the money needed to buy the product, insurance, storage, inspection fees, etc.

2. Gold is the easiest case to understand
   1. Forward month of gold reflects cost of holding, with each month always more than the one before

3. Copper is more likely to be in ‘backwardation’
   1. Deferred contracts sell for less than the nearby month
   2. Caused by falling demand and purchasers (mfr’s) who wait to the last minute to buy to avoid carrying charges and holding inventory

4. Crude oil is similar to copper
   1. Sometimes spends years in backwardation

5. These markets may experience manipulation but PK is not going into that
6. Because forward gold prices are always in ‘contango’, traders can keep the ‘term structure’ in line by arbing the delivery months
   1. If current gold price is $1000
   2. 12 months forward price is $1050
   3. This is a simple carrying charge of 5%
   4. The six month forward should be $1025 all things being equal, a return of 2.5% for half the time
   5. If the six month was $1035
      1. This is too high
      2. Implies annualized yield of 7%
      3. The play
         1. Buy physical gold and then
         2. sell 2x as many of the 6-month delivery
         3. Buy the 12-month delivery
         4. This is called a ‘butterfly’ (one foot in the cash market)
         5. At some point the 6mth and 12mth yields will align (otherwise gold goes into backwardation which is ‘unheard of’

7. Attached Image (click to enlarge)

   
   
   The problem with all of this is that pro arbs are ‘hovering like vultures’ for this opportunity so the ‘term structure’ never goes out of line enough to take advantage
8. PK goes to some pains to show the net return is about 2% and that you will lose this profit from the exchange rate when you repatriate your Euros.

Counterparty Risk

1. The risk of bank failure became real in 2008
2. Bank CDs pay more than government debt, but there is this risk to consider
3. ETFs are popular but are subject to ‘counterparty risk’
   1. Short-sale repurchase agreements with 3rd parties who cannot fulfill their deal
   2. Also
      1. Label risk - shares or futures in the ETF may not be the ones you expect
      2. Tracking risk - using fewer shares to duplicate a sector might not work
      3. Spread risk - stocks/futures get changed and rebalanced; the transactions are subject to the bid-ask spread and are larger when the ETF is larger
      4. Fees - usually applied daily that keep the ETF price suppressed

4. If you buy an ETF through an agency you depend entirely on the integrity and financial strength of the issuing company rather than on an exchange
5. Exchanges guarantee delivery but they don’t guarantee solvency
6. Gold ETFs are more complicated
   1. If sold via exchange-traded futures there is a guarantee and little risk
   2. If sold via forward contracts (maybe issued by a gold dealer) there is no guarantee
   3. Physical gold - you own the bar and there is no risk (except theft, or dropping it on your foot)
   4. A gold certificate from a bank - you own the bank’s debt, denominated in gold; similar to a bank CD where you own the debt denominated in some currency
   5. A gold ETF may make claims about inventories but without an audit you depend on the kindness of strangers
   6. If the issuing ETF company goes under it’s not clear who gets first claim on the gold, if any
   7. This is not surprising as we saw after Enron, for ex. where even a stock listed on an exchange is no guarantee of fraudlessness
   8. PK goes into great detail about the guarantees of ETFs, derivatives, and OTC derivatives but the lesson is simple - beware of counterparty risk; don’t assume everything is fine.
   9. Other than the sovereign debt of major industrialized countries, ETF futures have been the most secure (and we’re no longer so sure about sovereign debts)

Yield Curve

1. Interest rates - used in the stratagems of investment banks and hedge funds so there is likely not much room for the armchair trader
2. And that warning is enough for me. I’m going to skip this section.
3. Oh except for this bit
   1. If volatility calculations aren’t ‘lagged’ somehow you often have a situation where the relative value is declining even though the overall value is still high
   2. The solution is to use an absolute value, “trade when the dollar value of volatility, measured by the ATR, is some multiple of our costs, or above an absolute level of, say, $250.”
   3. OR calculate over a much longer time period, 1-3 years so that a 3 month run-up and run-down will still all be recognized as ‘high volatility’.

Trend Trading London Metal Exchange Pairs

1. PK outlines a commodities trade that is similar to the Dell-HP example, where one market is expected to steadily move away from the other
2. Learn about metals!
   1. Tin and zinc are both noncorrosive and used to plate steel
   2. Stainless steel - coated with tin!
   3. Galvanized steel - coated with zinc!
   4. Tin cans - made with aluminium and steel (not tin (anymore)!)
   5. Brass = copper + zinc
   6. Bronze = copper + tin
   7. Copper - hot water pipes
   8. Uneven demand can cause one metal to trend while the other falls for prolonged times

3. We can therefore capitalize on a trend while taking advantage of long-term correlation
4. Buying one and selling the other maintains a neutral position overall
5. This trade is a ‘directional spread’

Creating the trend trade

1. Trends can easily be measured with a simple moving average
2. Don’t overcomplicate a trend strategy

Table 5.6 - the information ratio for six moving average calculation periods and 15 LME pairs

1. Nearly every test is profitable
2. Again PK looks at NAVs and Volatility filters
3. He finds portfolios of trades do better
4. “If we consider the data beginning in 2005 and see that the markets are currently performing at a much higher level, we can rerun the 80-day moving average test from 2005 to get the profits per contract that we can expect under current market conditions.” (Can we/should we?)
5. “We can conclude that the LME nonferrous metals move in a way that can be exploited using trends where the two legs are volatility adjusted to equalize risk”
6. Trading during higher volatility periods is more profitable, as usual

Summary

1. Stat-arb trading
   1. Buy and sell abnormal differences in related markets and profit when they return to normal
   2. Has withstood the ‘test of time’
   3. Evolved into HFT
   4. There may be a ‘major shift’ happening
   5. Noise dominates in the short-term
   6. Trends dominate in the long-term

2. Most of the opportunities for this emerged in ‘recent' years (ca. 2005) to which PK attributes the higher vol from market crises and stress
3. Again, use a spreadsheet and computer program
4. Verify all results
5. Understand the process, etc.

Next: the stress indicator!