Have you ever thought about the similarities between trading and gaming? No? Me neither! Well until now, that is. A common misconception about both, is that it’s all about entry techniques. You have … an absolutely … breathtaking heinie. But much like in gaming, a great entry is far from necessary to be successful in the markets. In his book Trade Your Way to Financial Freedom, Van Tharp aims to bust this entry myth once and for all. He argues that a trading system is only 10 percent entry, and if someone tries to sell you a trading program that says otherwise – don’t just walk away, run! Instead, he argues that a great trading system requires personal fitting, good position sizing and just like in gaming – plenty of opportunity … … and a smooth exit. Takeaway number 1: Trading that fits. Trading is not one-size-fits-all. We are all special snowflakes, and this must be your starting point in designing a profitable system. Questions such as the following should be answered: How much money do you have? Are you only managing your own money, or other people’s money as well? How much time can you devote to trading daily? How much money do you need to make per year from your trading to put food on the table? I will illustrate this by presenting four different characters – each with their own unique situation, which will influence how they should design their systems. Meet Beginner Ben, who’s in his senior year of high school. During the last summer, he was able to save one thousand dollars from working at a local restaurant. He was recently introduced to trading by a friend of his. Ben’s smaller account will be a problem if he wishes to do short term trading. Commissions from his broker will simply devour any potential profits from such a strategy. On the other hand, Ben can afford to take higher risks, and apply aggressive position sizing to his trading, as learning the craft at an early age will far outweigh any loss on a $1000 account in the long run. Our second character is Manager Michael, who has a huge fortune to allocate. He’s the chairman of a medium-sized hedge fund, managing a total of 2 billion of other people’s money. Michael doesn’t have to worry about commissions, but he must make sure that losing streaks are kept to a minimum. A few consecutive months of underperformance could cause the clients to leave the fund and move their capital elsewhere. Our third character, Software Sara, has different “problems”. She’s a successful businesswoman at a software company, and a mother of three. Therefore, she has at most one hour per day that she can dedicate to trading the markets. Because of this, day trading is not really an alternative. On the other hand, her computer skills could help her in automating parts of the system so that she doesn’t have to trade it on a daily basis. Finally, we have Bold Bernard, who decided to quit his 9-5 and become a full-time trader. His only income will be that which he can generate from his trading account. He is single, and he needs approximately $25k per year to keep up his current living standards, and the size of his account is currently at $200k. The goal that Bernard has is achievable, but it might put pressure on him that influences his trading style. He will have to be extra careful so that the strain doesn’t get the best of him. Takeaway number 2: The notion of R. One of the most common (and biggest) mistakes a trader can make, is to not define his risk, which Van Thorpe refers to as R, before entering a trade. The golden rule of trading advocates that you must cut your losses short and let your winners run. If you don’t predefine your risk, you are always risking everything! That makes it quite difficult, to say the least, to follow that golden rule. Great traders know how much they are risking. For instance, if you would buy Amazon today at around $1,600, and put up an automatic stop-loss at $1,500, we can say that 1R, or what you’re risking, is $100. In this way, if you don’t remove the stop-loss, you’ve predefined your risk. What’s interesting from here, is that every trade you make can be measured in multiples of R. For example, let’s say that you managed to sell your Amazon stock at $2,000. This means that you were able to make $400 or, expressed in how much risk you took to make that trade, 4R. Have a look at these two bags of marbles, which characterizes two separate trading systems. Each marble represents an individual trade and the outcome of the trade is expressed in terms of R. Which of these systems would you rather trade? If we sum up the value of the marbles, and divide it by the number of them, we will get the expectancy of the system. The expectancy represents how much, in terms of R, that you can expect to earn on average from the system on every trade. We can see that system A is better than system B in this regard. The expectancy is 0.7 R for A versus 0.2 R for B. Always try to visualize what the distribution of R-multiples looks like. If you understand this distribution, you typically understand what to expect from the system. Look at the distributions of system A and B. Which one is following the golden rule of trading? That’s right, it’s A. Takeaway number 3: Exiting techniques. Trading is not a marriage. When finding your significant other, you probably do better in making sure that you find the right person. If you are thinking about divorce as your lips utter the words “I do”, you’re probably in for a disaster. When trading, it’s the other way around. Exits are way more important than entries. Think about takeaway number 1. You must build your exit strategy so that it matches your personality. Otherwise you won’t be able to stick with it. Could you handle being wrong 10 times in a row? Many profitable systems are built on large profits, but small frequent losses, just like system A in the last takeway. Even though these systems are profitable in the long run, you could easily run into 10 losing trades in a row. With system A, the likelihood of drawing orange marbles (ie making a loss) 10 times in a row is 11 percent! Here are 3 types of exits that you might want to consider: 1. Percentage exits This is a very common kind of exit. For instance, William O’Neal in his book How to Make Money in Stocks, talks about never allowing a position to lose more than 7-8% before you step out. He argues that the more a position goes against your trade, the more likely it is that you’re wrong. Take the loss, and move on. A percentage exit can also be used to secure profits, by letting the stop move as the trade is making new highs or lows. 2. Time exits Perhaps your trade is based on fundamentals and every time a certain type of announcement is made, you expect a reaction from the market. In this case, it might make sense to use a time exit. For example, if the move you’re looking for doesn’t happen within a week or so, after the announcement is made, you step out. 3. Volatility exits When the market makes a move against your trade that is unlikely to be just market noise, step out. This is useful for cutting losses short and securing profits alike. No matter which exiting strategy you pick, losses are unavoidable. Not accepting losses is like breathing in, but then refusing to breathe out. No, I couldn’t do it! Takeaway number 4: Opportunity How much you can expect from each trade in terms of R forms the base of a square. Adding how often you get the opportunity for such a trade, adds a third dimension, which transforms the square into a cube. The cube represents the results that you can expect given a certain opportunity, or “expectunity”, as Van Tharp likes to refer to it. Let’s return to the two bags of marbles from takeaway number 2. Which of these two systems is the most profitable? Well this depends on how often you can trade them! Let’s say that system A has a lot of setups and conditions that must be met before a trade can be executed. Therefore it can only be traded once every month. As a consequence, you can expect to profit 8.4 R on a yearly basis. On the other hand, let’s say that trading system B, which appears to be less attractive at first glance, can be traded once every week. Thus, it will return on average 10.4 R each year. That’s almost 24% more profit per year than system A! This is a simplified picture of what your system is likely to return over a year though. There’s still commissions, taxes and psychological mistakes to be paid. Check out my videos on Thinking Fast and Slow, and the biases that might affect you in the stock market for that last part. Take away number 5: Position sizing – the most important part of a system. “Wait a minute, are you saying that there’s something even more important than having a positive expectancy an opportunity to trade?” Okay okay. Just to clarify: Position sizing is the most important part of a system, because it has as much impact as expectancy and opportunity, but it’s even more common that it’s overlooked. To demonstrate the importance of position sizing, we’ll return to the bag of marbles that represents system A. Also, I want you to meet 3 new characters: Stubborn Steve, Risky Rachel and Conservative Charlie. They all have $10,000 to trade, and they are identical in their trading approach. Except when it comes to position sizing. Stubborn Steve is always risking $2,000, or in other words, one R equals to $2,000 for him. Risky Rachel applies another strategy, which Van Tharp refers to as the “percentage risk model”. She’s always willing to risk 10% of her capital in any given trade. So 1 R=10%=$1,000 initially. Her percentage model makes it so that she will increase her bets if she’s on a winning streak, and decreased them if she’s losing. Conservative Charlie applies the same strategy as Risky Rachel, but 1 R in his case is always equal to 2% of is capital, or $200 at the beginning of our illustration. Now I will pull a random string of 10 marbles from the bag, and we’ll see what happens to our traders. Orange Orange Orange Orange Orange again … Orange Orange Oh! Green! Orange, noooo Blue! Yipiii! Notice out three persons, using the same trading technique, got vastly different results by only applying different position sizing. Stubborn Steve lost all of his capital at round 5, and Risky Rachel had Conservative Charlie beaten by $1,684, achieving a total return of 29% on her account versus Charlie’s 11%. Van Thorp thinks that a percentage system such as the one used by Rachel and Charlie is a good position sizing technique. But he would lean towards the sizing of Charlie’s rather than that of Rachel’s because of what happened to Rachel’s account after seven trades. It takes a lot of willpower and mental stability to keep trading a system after you’ve lost half of your capital like that. Once you have evaluated what the distribution of R looks like for your trading system through back testing, you should simulate many scenarios of trading it. Constructs your position size thereafter. Take your objectives into account. If you can’t handle losing more than, say, 25% of your account for instance, then pick a position sizing that allows for you to reach that objective. (maybe you shouldn’t be as aggressive as Rachel in that case) Quick recap of the takeaways: Build a system that fits your character, knowledge and situation. Always predefined your risk R, and express the expectancy of a system in multiples of this. Use an exiting strategy that maximizes the expectancy of your system, but always remember that it must also fit your personality. A system with a high expectancy per trade and a lot of opportunity can be highly profitable in the long run. And lastly: Position sizing is the most overlooked aspect of a trading system, but it can mean the difference between financial excellence, and personal bankruptcy, even with an otherwise perfect system. Thanks for watching guys! Cheers!