People are always looking for the ‘real’ secrets of trading success, but mental biases have them looking in the wrong places and at the wrong things. Consequently, they search for great entry systems that they think will help them pick the right stock. Picking the right stock has nothing to do with success and neither does the accuracy of your stock picking.
All Market Wizards agree that the key ingredients to your success are (1) the golden rule of trading (cut your losses short and let your profits run); (2) position sizing (the part of your trading system that tells you how much); and (3) the discipline to do both. The golden rule of trading describes exits-abort losses and ride winners. In this game, position sizing controls how much equity you risk on any given trade.
By the way, in real trading you achieve your objectives through position sizing. Your system (i.e., entry and exit) only determines how easily it will be to achieve your objectives through position sizing.
R and R-Multiples
Before you can effectively apply position sizing strategies, you must understand the principles of R and R-multiples. R stands for the risk you take on any trade when you enter the market. Risk is the amount that you are willing to lose on the trade in order to achieve a profit. In terms of price, R is the point at which you plan to get out of your position in order to preserve your capital. It’s the place where your rules say the reward-to-risk ratio will not be profitable on this trade, and it’s better to exit now rather than lose more.
For example, if you buy a stock at $50 and you plan to get out if it drops to $47 or below, then your R-value per share in this trade is $3.00 (i.e., $50 – $47 = $3.00). If you buy 100 shares of stock, your total risk for the trade is $300-which is your total 1R value.
Your R-multiple is simply the amount that you profited or lost in terms of your initial risk. If you purchased that stock at $50 with the initial stop price of $47 and exited at $47, then you have a -1R trade. You lost what you risked – $3. If, however, the stock went up and you exited at $56, then you had a +2R trade because you earned twice what you risked ($56 – $50 = $6, $6 ÷ $3 = 2)
You want your losses to have an R-multiple between 0 and -1. Losses can be bigger than -1R when the market gaps against you and goes through your ”get me out” price. They can also be bigger than -1R when you make psychological mistakes and fail to get out at your stop point. Excessive costs (commissions and slippage) can also result in larger negative R-multiples.
You want your profits to be large, i.e., much bigger than +1R. For example, if 1R per share is $3 as above, then a $15 gain per share means the position earned a 5R profit.
Now suppose you have a trading system where you make +5R on the winning trades. When your trades lose, however, you lose only -1R. If your system is right one time (+5R) for every three losses (3 × -1R = -3R), then the system averages a 2R gain (+5R – 3R = +2R) over four trades.
If we continue with this example and risk $3 for each share, then over four trades we would expect to see $9 in losses and $15 in gains for a net profit of $6 per share ($6 per share = $3 per share × 2R). Imagine that! You are right 25% of the time and you still make money. (If you were to risk 1% of your equity on each trade, this system would generate about a 2% gain in your equity every four trades.)
The principle of cutting your losses short (so you will have small R-multiple losses) and letting your profits run (so you will have big R-multiple gains) is critical for profitable trading. The first level of this game introduces how you might apply position sizing strategies to a simple system.
Expectancy versus Probability
Expectancy is a mathematical formula that tells you how much you will win on the average per dollar risked. It takes into account both the probability of winning (or losing) and the size of the R-multiples. Casino gambling games are all negative expectancy games; you cannot make money in the long run unless you can do something to change the odds. In trading, you must play a different game from gambling. You must have a positive expectancy game on your side in order to make money in the long run. Expectancy is actually the average R-multiple that your system will give you per trade.
Most people look for trading systems that make them right. That is a mistake. Such games can have a negative expectancy (meaning that you’ll lose money overall) if some of the losers have large R-multiples. More importantly, some of the best trading ideas have large R-multiples in your favor, but only make money 25-40% of the time.
Let’s look at an example. Suppose you buy a stock at $50 and plan to get out when it drops against you by a dollar to $49. However, when you are right you expect that stock to move 30%. In this case, a 30% move is an additional $15.
When a trade fails, you lose one dollar per share. When a trade works, you make 15R or $15 per share! What if you were only right 30% of the time and you make money in three of ten trades? In ten trades you’d make $15 per share an average of three times. Your total gain would be $45 per share. In the same ten trades, you’d lose $1 per share on the average seven times. Your total loss would be $7 per share. Over the ten trades you’d end up making $38 per share (or 38R), even though you were only right 30% of the time. Large R-multiples in your favor are much more significant than ‘being right’ for making money in the market. Remember that! And if you had risked 1% of your total equity on this system, you would have been up about 38% at the end of 10 trades.
Even though more trades lose than win in that example, the large size of the winning trades outweigh the losses so the system has a positive expectancy. To calculate expectancy, determine the average R-multiple for the system, taking into account both the positive and negative Rs. The mean R is the system’s expectancy.
Another (more difficult) way to determine expectancy is to multiply each R-multiple (both negative and positive) by its probability of occurrence. Then sum the results (i.e., subtracting the values of the negative R-multiples) to get the total expectancy. All of the probabilities, of course, must add up to 100%. If not, it means that you have missed some. In the case of our stock example just above, you multiply 0.3 by 15 (which is 4.5) and 0.7 by minus 1 (which is minus 0.7). When you add 4.5 and minus 0.7, you have a total expectancy of 3.8R. This means that you will average in gains, over many trades, 3.8 times your risk on each trade.
There is a critical aspect to expectancy that you must understand. Expectancy and probability are not necessarily the same. As I said earlier, you must have expectancy on your side, but you don’t need to have probability on your side. Let’s look at the example given earlier. You win 30% of the time, and when you win it’s a 5R gain. You lose 70% of the time, and when you lose it’s a 1R loss. You only make money 30% of the time. Thus, the odds are against you. However, the game has a positive expectancy, giving you an average of 3.8 times your risk each trade or 3.8R