This post was contributed by a guest author, and does not necessarily reflect the views of Richard or MovetheMarkets.com

Phileo asked me if I favor $10 stocks lately. I have been favoring “cheaper” stocks for two reasons–first, I’m trying to find a low risk swing trading strategy using penny stocks. You’ve seen some results of that here on MTM.

The biggest reason, though, is related to my recent FDX trade. I found a great low risk setup, but I didn’t have enough capital to trade the number of shares I wanted.

When you are daytrading a small account, you’ve only got three chances every 5 business days to make a trade. I want to most efficiently put my capital to work, and so my spreadsheet calculates a ‘% Capital at work’ figure for each planned trade. I look at the entry, the stop, and the maximum risk in dollars that I will take. That gives me a risk-limited position size. I then look at my buying power and the entry price, and determine the maximum position size that my buying power will allow. The smaller of these sizes is the one I trade. Due to the limit on my trading frequency, my objective is to put as close to 100% of my capital to work as I can each trade. A few examples might help to illustrate.

The FDX Trade:

1% of equity ($1000) = $10 = Maximum trade risk
Long Entry: $116.35, Stop: $116.02, Spread = $0.33 per share
Risk-limited Position Size = $10/$0.33 = 30 shares (round down)

Available Buying Power = $1577, Entry: $116.35
Buying Power Position Size = $1577/$116.35 = 13 shares (round down)

Final Position Size = 13 shares; % Buying power at work = 96%; Actual Trade Risk / Max Risk = 39%

For this trade, my trade risk was only $3.96, far below my $10 target. The trade yielded 4.6R, so I made a bit over $18 of profit. If I had my full position size, I would have made $46 of profit! So while the share cost lowered my risk to a loss of 0.39% of my equity, the problem here was that I put too much of my capital to work for not enough potential reward.

Now another example–my AMNT.OB Trade:

1% of equity ($1000) = $10 = Maximum trade risk
Long Entry: $2.45, Stop: $1.45, Spread = $1.00 per share
Risk-limited Position Size = $10/$1 = 10 shares

Available Buying Power = $1685, Entry: $2.45
Buying Power Position Size = $1685/$2.45 = 687 shares (round down)

Final Position Size = 10 shares; % Buying power at work = 1%; Actual Trade Risk / Max Risk = 100%

For this trade, my trade risk was $10, but my entry-to-stop spread was huge. The trade yielded 0.87R, so I made $8.70 of profit. I only put $25 to work, and I used up a precious trading opportunity in doing so. Not an efficient use of capital. I’ve also taken some of these types of trades that didn’t work out.

Most of the setups I daytrade end up having an entry-to-stop spread of around $0.20, on average. If I assume a $10 max risk based on 1% of my equity, then a stock price of $32/share will allow my Risk-limited position size to match my Buying power-limited position size. For a spread of $0.10, that drops to $16/share. So for my capitalization level, cheaper stocks make more sense.

Naturally, all of this relates to expectancy, win/loss rates and the like. In the near future, I hope to do a more thorough study of my recent stats and come up with the ideal risk/reward/$ at work combination for my trading style(s). Which brings me to one more thought–Different trading systems and styles should have different risk parameters! The result is that I end up risking too much on setups that don’t win often, and not enough on setups that do. I got a message from Trader-X the other day, and he told me something wise:

“As I have said in the past, you can focus on just one high-quality, consistent set-up and trade it 2-3 times a week. Your account would not need to be very big, and provided they are quality set-ups you can make good consistent money. But most people are not that patient and usually end up severely overtrading and their focus is all over the map looking for 4, 5, 10 different set-ups.”

My focus has been pretty scattered. I’m going to narrow it down to 2–Trader-X style daytrades, and a swing trade system TBD. I will also have two separate risk profiles based on the respective win/loss rates and expectancy analysis. I also overtraded this month, and used up my 40 free trades from Zecco, so I’ll start new in March.

This post was contributed by a guest author, and does not necessarily reflect the views of Richard or MovetheMarkets.com

This post was contributed by a guest author, and does not necessarily reflect the views of Richard or MovetheMarkets.com

Let’s say that you are engaged in a snowball fight (It doesn’t matter how, why, or where you are engaged in the snowball fight). You are protected behind a wall of snow that you have previously built. It is in your best interest to make this wall as big as possible so that it will offer you even more protection from the snowballs. For each snowball that you throw, there are two types of snowballs being thrown back at you: white snowballs, and black snowballs. The white snowballs will stick to your wall and make it bigger (which is a good thing). On the other hand, the black snowballs will punch a hole in your wall that is equivalent to their size (which of course, is a bad thing). The size of each white snowball can and will vary. The frequency of these snowballs hitting your wall, however, will depend on how often you are throwing snowballs at the opposing side. Furthermore, for each snowball that you throw at the other side, you will get back a certain number of white snowballs or black snowballs (but never both). Your success in this snowball fight is measured by the size of the wall. How will you win this snowball fight?

Before I answer that question, consider that the size of the wall of snow matters quite a bit. For example, if the size of the wall is less than 10x bigger than one of the black snowballs, then you would have to be a cracker jack snowball fighter, because all it takes is a few black snowballs to obliterate your wall of snow. Not only that, but for each black snowball that punches a hole in your wall, you actually need a white snowball that is BIGGER in size than that black snowball in order to repair and restore your wall (it takes more time AND effort to repair something than it is to take it apart).

The other thing to consider is that the general, average size of the your white snowballs actually depends on the size of your wall of snow (among other things). If you have a big wall of snow, then the probability of you getting bigger white snowballs has increased. However, if your wall of snow is small, then chances are, you are not going to see very many big white snowballs. Not only does that makes it harder to recover from a black snowball hitting your wall, but your wall of snow will grow at a much slower rate.

The key to winning this snowball fight then, is to know what is under your control in this snowball fight. You cannot control the size of the white snowballs. You cannot control whether a white snowball hits your wall, or a black one hits your wall. But, you can control the size of the black snowball. And that is the key - make the size of the black snowball about 1/3 the average size of a white snowball, such that one snowball can repair the damage done by almost 3 black snowballs. And, you can always make educated and calculated guesses of the average size of the white snowballs that hit your wall based on past observations of these white snowballs. Follow this (seemingly) simple rule, and you will win the fight.

If any of this sounds familiar to you, then that means you’ve probably read Van Tharp’s famous book “Trade Your Way to Financial Freedom.” In this very comprehensive book, he uses the snowball fight metaphor (where white snowballs represent trading profit, black snowballs represent loss, and the wall of snow represents trading account size) as a way to help the reader understand position sizing, expectancy, and other keys to trading success. This metaphor is much better than the marble analogy that I described in my previous post on keys to effective trading. I’ve modified and extended Tharp’s snowball metaphor as described above so that it really helps to remind me of the critical importance of position sizing and controlling losses. The new insight that I gained from this analogy was how position sizing and controlling risk are actually dependent on each other. When using fixed dollar amounts for entering a position, and separately calculating the stop loss point, that dependency is hidden and will not be clear. But once you incorporate the amount at risk into calculating the size of your position, then it becomes pretty clear. Van Tharp does a pretty thorough job of explaining how to incorporate the amount at risk into calculating position size. If you don’t plan to read Van Tharp’s book, then at least take the time to check out one of the articles that TraderMike wrote regarding position size.

There is one important corollary that needs to be pointed out. My modified analogy above also serves to highlight the problems of being undercapitalized (which Prospectus also discussed here) - the smaller your account size, the slower and harder it is to grow your capital. This is because the size of your wins are relatively smaller. A 3R win on a 30k account (with R=1% of account size) means a 30R win on a 3K account size (with the same R=1%) - and how often will you experience a 30R win from a single trade? Right, so one subtle (yet important) corollary of this snowball fight analogy is that one of the requirements for entering the business of Trading for A Living is having sufficient account capital.

This post was contributed by a guest author, and does not necessarily reflect the views of Richard or MovetheMarkets.com