Position sizing strategies have been driving the great performance of some of the best traders in the world in my opinion. In this article, I would reveal 6 different extremely successful position sizing strategies. Let’s have a look below:

1. William O’Neil’s CANSLIM

The approach that he describes is based on owning stocks. Therefore, he does not address the question of “how much to own” in a certain position. In his own words, even a multimillion dollar portfolio should only have 6-7 stocks. Investors with a portfolio of $20,000 to $160,000 should invest in up to 4-5 stocks. Traders with $5,000 to $20,000 should not invest in more than three stocks. People with less money than that should probably not invest in more than 2 stocks in total. O’Neil’s approach teaches that you should divide your capital into equal amount of money you have.

2. Warren Buffet’s Approach 

Buffet is interested in owning only the creme de la creme of stocks, in other words the very best of businesses. They need to meet his exceptional criteria. The idea behind his approach is to own as much of those few business as he can. He does not plan selling those businesses in the long-term, since they are exceptional and they should bring him amazing returns. This is a rather unique style of position sizing, but who can argue such a stellar performance as his…

3. Motley Fool Foolish-Four Approach

With this approach, you are only buying four stocks- it is an equal units approach with a twist. The twist is that you need to make one of the units twice as big as the rest. This unit is the stock that has the best chances of making a big return- the stock that has the second highest yield in the Dow Jones. The remaining stocks have an equal weight in the basket and are only half as big.

4. Kaufman Adaptive Moving-Average Approach

Kaufman doesn’t really discuss position sizing in his writings. He does discuss some of the results of position sizing such as risk and reward. He describes risk as the annualised standard deviations of the equity changes and by reward he means the annualised compound rate of return. He suggests that when two systems have the same returns, the rational investor will choose the system with the lowest risk. Kaufman also advices that one can control the worst-case scenario by looking at the standard deviation of his/her risk. For example, if the investor has 40% return and the variability of his/her drawdowns suggests that 1 standard deviation is 10%, then the following are true:

The investor has a 16% chance (1 standard deviation) of a 10% drawdown.

The investor has a 2.5% chance (2 standard deviations) chance of a 20% drawdown.

The investor has a 0.5% chance (3 standard deviations) of a 30% drawdown.

5. Gallacher’s Fundamental Trading

In his book Winner Take All, Gallacher says that risk is directly related to exposure in the market. He states that “an account trading two contracts of the same commodity and risking $500 is a much less risky proposition than an account trading two contracts of the same commodity and risking $250 on each.” Glacier’s statement is completely true and everyone accepting the percent risk model should understand it. The stop is only the price at which your broker is told to sell. It does not in any way guarantee that price. This is one reason, the percent volatility model is good for anyone who wants to trade tight stops. Among other things, Gallacher points out that your risk increases not only with your exposure, but with time, as well. The longer you trade in the market, the better your chances are to “meet” a black swan.

6. Ken Robert’s 1-Z-3 Methodology

Ken Robert’s approach is targeting mostly smaller retail traders with account sizes between $1,000-$10,000. He advices that traders do not trade more than one contract in any commodity. In this way they can take relevant risks and not risk more than $1,000 for the bigger accounts. Roberts suggests that traders should not trade in riskier markets like S&P 500, yen, and perhaps even coffee, because the risk involved would be typically over $1,000.

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