The Secret Methods that Turned Ordinary People into Legendary Traders


Trading is about buying at one price and then selling at a higher price later or selling short at a particular price and then buying to exit the short position at a later point. When they are determining when to enter a market, most beginners employ a strategy that is no better than throwing darts at the chart. Experienced traders would say that their strategy has no edge.

The term edge is borrowed from gambling theory and refers to the statistical advantage held by the casino. It also refers to the advantage that can be gained by counting cards when one is playing blackjack. Without an edge in games of chance, you will lose money in the long run. This is true in trading as well.

If you do not have an edge, the costs of trading will cause you to lose money. Commissions, slippage, computer costs, and exchange and pricing data fees add up very quickly. An edge in trading is an exploitable statistical advantage based on market behavior that is likely to recur in the future. In trading, the best edges come from the market behaviors caused by cognitive biases.

Elements of an Edge

To find an edge, you need to locate entry points where there is a greater than normal probability that the market will move in a particular direction within your desired time frame. You then pair those entries with an exit strategy designed to profit from the type of moves for which the entry is designed.

Simply put, to maximize your edge, entry strategies should be paired with exit strategies. Thus, trend-following entry strategies can be paired with many different types of trend-following exit strategies, countertrend entry strategies can be paired with many different countertrend exit strategies, swing trading entries can be paired with many different types of swing trading exit strategies, and so on. To understand why this is important, let’s dig further into the components that make up the edge for a system. System edges come from three components:

To understand why this is important, let’s dig further into the components that make up the edge for a system. System edges come from three components: • Portfolio selection: The algorithms that select which markets are valid for trading on any specific day • Entry signals: The algorithms that determine when to buy or sell to enter a trade • Exit signals: The algorithms that determine when to buy or sell to exit a trade It is possible for an entry signal to have an edge that is significant for the short term but not for the medium term or long term.


Mature understanding of and respect for risk is the hallmark of the best traders. They know that if you don’t keep an eye on risk, it will set its eye on you.

Akey question, perhaps the only question, to ask when you are considering a system-based trading strategy or trying to select a fund advisor who uses such a strategy is: “How can you know whether a system or a manager is a good one?” In general, the answers the industry offers are various takes on the following: The strategy or manager with the highest risk/reward ratio. Everyone wants to make the most money for a specific level of risk or incur the least risk for a particular level of expected return.

On this point, we are almost all in agreement: traders, investors, fund operators, and so forth. Unfortunately, there are many different opinions on the best measures of the risk and reward parts of the risk/reward ratio. Sometimes the financial industry defines risk in such a way that the description completely blinds it to certain kinds of risks, and those risks are just as likely to bite them in the ass as are the ones with which they do concern themselves.

The large losses incurred in the implosion of Long-Term Capital Management are a good example of risks that existed outside the traditional measures. This chapter will review those risks and ways to account for them, and then propose some general mechanisms for estimating risk and reward for trading systems by using historical data. Rich and Bill were very concerned with the size of our positions because they knew that there was a risk of losing their entire net worth if those positions were too large during a large adverse price movement.

A few years before starting the Turtle program, they had traded during a period when the silver market was locked down limit for days and days. This meant that there was no opportunity to exit because there were no traders willing to buy within the limits imposed by the COMEX futures exchange on how much the price of silver could change in a single day. This is the futures trader’s worst nightmare. Each day you are losing more and more money and there is nothing you can do about it.

Measuring What You Cannot See

There are many ways to quantify risk, which is one way to factor in the pain you would have encountered while trading a particular system. Here are some common measures that I find useful: 96 • Way of the Turtle

1. Maximum drawdown: This is a single number that represents the highest percentage loss from peak to subsequent equity low during the course of a test. In Figure 7-4 this would be the 65 percent drawdown that was due to the price shock of the 1987 crash.

2. Longest drawdown: The largest period from a peak in equity to a subsequent new peak. This is a measure of how long it would take to regain new equity highs after a losing streak.

3. The standard deviation of returns: This is a measure of the dispersion of returns. A low standard deviation of returns indicates that most returns are near the average; a high standard deviation indicates that returns vary more from month to month.

4. R-squared: This is a measure of smoothness of fit to the line that represents the CAGR%. A fixed-return investment such as an interest-bearing account would have an R-squared value of 1.0, whereas a very erratic set of returns would have a value lower than 1.0.

The Sharpe Ratio

The Sharpe ratio is probably the most common measure used by pension funds and large investors in comparing potential investments. The Sharpe ratio was invented by the Nobel laureate William F. Sharpe in 1966 as a measure for comparing the performance of mutual funds. This measure was introduced as a reward-to-variability ratio but subsequently came to be referred to simply as the Sharpe ratio after its originator.

The Sharpe ratio takes the differential return, which is the CAGR% for the period being measured (i.e., a monthly or yearly period subtracts what is known as the risk-free rate or the rate of interest one could get by investing in a risk-free bond such as a T-bill) and then divides it by the standard deviation of the returns being measured (generally monthly or yearly). Keep in mind that the Sharpe ratio was conceived as a measure for comparing the performance of mutual funds, not as a comprehensive risk/reward measure.

Mutual funds are very specific types of investment vehicles that represent an unleveraged investment in a portfolio of stocks. The original role of the Sharpe ratio as a tool for comparing the performance of mutual funds gives important clues to the types of risks it does not contemplate. Mutual funds as they existed in 1966, when the Sharpe ratio first was proposed, were unleveraged investments in portfolios of U.S. stocks. Thus, a comparison between mutual funds was one between investments in the same markets and with the same basic investment style.

System Death Risk Revisited

One of the most interesting observations I have made about trading systems, strategies, and performance is that those strategies which historically appear to offer extremely good risk/reward ratios tend to be the ones that are the most heavily imitated by the broader trading community. Soon you end up with billions of dollars in trades chasing that strategy, and as a result, those strategies can implode as they outgrow the liquidity of the markets in which they are traded.

They end up suffering from early system death. Arbitrage strategies are perhaps the best example of this. An arbitrage in its purest form is an essentially risk-free trade. You buy something in one place, sell it at another place, subtract the cost of transportation or storage and pocket the difference. Most arbitrage strategies are not quite that risk-free, but many come close.

The problem is that these strategies make money only when there is a spread between the prices at different locations or between the price of one instrument and that of a similar instrument. The more traders implement a particular strategy, the more the spread will drop as those traders start to compete for essentially the same trades. This effect kills off the strategy over time as it becomes less and less profitable.


Ruin is the risk you should be concerned with the most.
It can come like a thief in the night and steal everything
if you aren’t watching carefully.

Like many of the concepts we use in trading, expectation, edge, risk of ruin and so on, the term money management comes from gambling theory. Money management is the art of keeping your risk of ruin at an acceptable level while maximizing your profit potential by choosing an appropriate number of shares or contracts to trade, something we refer to as the size of the trade, and by limiting the aggregate size of the position to control exposure to price shocks. Good money management helps ensure that you will continue to be able to trade through the inevitable bad periods that every trader experiences.

Most discussions of the topic make use of countless formulas and cover different methods for determining precisely the number of contracts one should trade. They approach risk as if it were a definable and knowable concept, but it isn’t. This chapter won’t duplicate those discussions.

Risk of Ruin Revisited

Earlier we discussed the concept of risk of ruin: The possibility of losing so much capital as a result of a string of losses that one is Risk and Money Management • 113 forced to quit trading. The definition as most people use it applies to a random set of outcomes using a fairly simplistic formula based on probability theory. Most people think in terms of the risk that you will experience ruin due to a bad period of losses in rapid succession.

I believe that this is not generally what brings traders to ruin. Traders do not fall prey to a period of randomly adverse market behavior very often. It is far more likely that they have made some serious mistakes in their analysis. Here is what I believe accounts for a trader’s lack of success in trading commodities:

• No plan: Many traders base their trades on hunches, rumor, guesses, and the belief that they know something about the future direction of prices.

• Too much risk: Many otherwise excellent traders have been ruined because they incurred too much risk. I’m not talking about 50 percent or 100 percent more risk than is prudent. I have seen traders who trade at a level that is 5 or 10 times more than I consider prudent even for aggressive trading.

• Unrealistic expectations: Many new traders trade with too much risk because they have unrealistic expectations about how much they can earn and what sorts of returns they can achieve.

This is often also the reason new traders believe they can start trading on the basis of fundamental data; they believe they are smart enough to “beat” the market with little or no training and very little information.

Turtle Money Management Means Staying in the Game

The primary goal of trading should be to stay in the game. Time is on your side. A system or method with positive expectation eventually will make you rich, sometimes beyond your wildest dreams. This can happen only if you can continue trading. For traders, death comes in two forms: a slow painful death that causes traders to stop out of anguish and frustration and a spectacular rapid death we refer to as a blowup.

Most new traders overestimate their tolerance for pain, believing that they can live through a 30 percent or 40 percent—or perhaps even a 50 percent or 70 percent—drawdown when they can’t. This can have an extremely adverse effect on their trading because it usually results in their stopping completely or changing methods at the worst possible time: After they have incurred a drawdown and suffered significant losses.

The uncertainty of the future is what makes trading so difficult, and people do not like uncertainty. Unfortunately, the reality is that the markets are unpredictable and the best you can hope for is a method that generally works over a relatively long period. For this reason, your trading methods should be designed as much as possible to reduce the uncertainty you can expect to encounter when trading. The markets are already uncertain enough; there is no sense adding to that variability with poor money management practices.

Facebook Comments


Please enter your comment!
Please enter your name here