Old ideas about trading have been lost in the wake of technological development. At one time, what we knew as money management was the end all of trader sophistication, not to mention something many small traders ignored because they competed in the arena of one-lot positions. However, now savvy traders of all sizes have expanded their ideas of money management into the broader concept of trade management.
Trade management, in short, refers to the processes that flow from your trading plan. It includes money and risk management, portfolio construction and diversification, scaling, expectancy, position sizing and instrument selection. Here, we’ll begin a detailed look at trade management and look at ways technology is making the development of a proper trade management plan easier. Ultimately, we’ll review case studies that demonstrate how to implement these concepts in your trading.
IT AIN’T MAGIC
First, before you embark on crafting any trading plan, your trading strategies must be robust and validated on out-of-sample, real-world data. If they do not perform consistently, as indicated by their past performance, the best trade management plan in the world cannot turn them into winners.
Thus, the first step in any trade management plan is to have a sound trading approach. It is imperative to understand how to develop a reliable and robust system. In a nutshell:
1. There must be a valid premise to the system logic.
2. Optimization must consider robustness. It’s not just about the best performing parameters, but the most stable parameters, measured across a variable range.
3. You need a large number of test cases. A few good trades will not do.
4. If possible and if the system logic allows it, the system should be tested on a basket of markets, and parameters must be selected based on the basket performance.
5. Implement out-of-sample rules validation. The system must perform on data outside of the data used for optimization.
6. Implement real-world logistical validation. Make sure the system is feasible in today’s market structure and that its execution is possible within your own logistical constraints.
The above list is not exhaustive and each step warrants a treatment all its own, but if you have not embarked on this process to build a solid base of a profitable system, trade management can’t turn a non-valid set of entry rules into a profitable strategy.
WHAT IT CAN DO
Trade management combines two classic areas of trading, which in turn are two often-conflicting goals of trading: controlling risk and maximizing returns. Numerous trading concepts attempt to help a trader accomplish these lofty aspirations.
Limiting risk can be accomplished in a number of ways. We can limit the number of positions that can be open at one time. We can limit the number of positions in each sector or group. We can stop trading when a monthly stop level is hit on the complete trading program. We can capture profits and reduce trading size following windfall profits. Portfolio rebalancing can redistribute profits or address losses that have created an uneven distribution of funds. Finally, we can diversify properly.
When it comes to maximizing returns, we’re really talking about properly reinvesting our profits. Ultimately, this involves sizing positions based on the current account size relative to some volatility measure, such as the average true range, in dollars, for each market. Basically, there are two options. We can use fixed-fractional money management, or we can employ fixed-ratio money management. Let’s take a closer look at both areas of trade management, limiting risk and maximizing return.
LIMITING RISK
In practical terms, limiting risk often means limiting the number of positions that can be open at any one time. There are two general approaches to this: limiting the number of total positions regardless of which markets they are in, and limiting the number of positions that can be established in related markets or sectors.
The first approach requires the trader to pick and choose among trade signals. Many stock traders will use concepts such as relative strength, only going long the best performers or shorting the worst. Hence, if you have 100 stocks and want to limit yourself to 10 positions long only, you would only select stocks with a relative strength more than 90%. A variation for commodity traders would be to rank markets by some trend-strength gauge, such as the ADX.
A more thoughtful approach, though, would look at the strongest stocks in a sector or sub-sector and include those in the collection for consideration. For example, if you blindly rank all markets, the top of your list would be dominated by recent strong performers, which are likely to be related (eight out of 10 might be energy stocks, for example). By ranking sectors independently, you stand a better chance of diversifying your holdings.
Another valuable method of risk control is to stop trading and go flat when a given drawdown mark is hit, usually done on a monthly basis. Trading resumes the following month when new entry signals are generated. This artificial time-out, and the breather it forces you to take, can be effective at preventing disaster. Some traders also employ the flip-side of this approach, halting trading when a large windfall profit comes their way; for example, shutting down if you find yourself up 15% in one month.
Of course, with how quickly prices can change, you aren’t always making your exposure or diversification decisions when you’re out of the market. Suppose that you purchased silver on a breakout on Sep. 28, 2007, at 14.20. On Feb. 29, 2008, sliver closed at 19.91, with more than $28,000 profit per contract. Assume, just for the sake of this discussion, that you were holding three contracts in a $20,000 account. You now would have $80,000 in open profits, or 400% of your starting account size (see “What goes up…”). A similar situation has been seen in the grain markets, with wheat, soybeans and corn experiencing some major price surges recently.
Although any account following such run-ups will be able to sustain more positions, this would not be the time to employ your new size. It’s time to lay off some of this silver (or wheat or beans). The reason is simple: You are overexposed to one market. You will want to monitor your positions closely, and rebalance as necessary, to make sure you are maintaining proper diversification, which is critical for solid risk-adjusted returns. Diversification, though, is not simply being active in a large number of markets. You need to go deeper than that.
“Correlation report” shows a correlation matrix for the equity curves of a 20-bar channel breakout on the selected markets. On an equity curve basis these markets are pretty non-correlated, while they don’t show as much diversification in terms of market prices. However, when you search for non-correlated equity curves, you naturally create a condition that can prevent you from hitting on all cylinders, so to speak. When one market is making money, another will be losing it.
The answer is to examine correlation for drawdown (see “Underwater correlation”). The drawdown correlation matrix is totally different. The drawdown of corn to yen and coffee is strong, more than 0.60. Surprisingly, the drawdown correlation between the yen and the dollar index is only 0.212. We also see the dollar index and 10-year Treasury-note correlation is slightly negative on a drawdown basis. It’s also interesting that coffee and cotton are not correlated on an equity curve basis but are highly correlated, over 0.70, on a drawdown basis.
MAXIMIZING RETURN
Maximizing return generally refers to increasing the size of your positions to make the most of your trading power. There are two general ways to do this: fixed-fractional and fixed-ratio money management.
Fixed fractional money management always risks a given percentage of your account on each trade. So, if you decide to risk a percentage of your account on any given trade, then the number of contracts you position yourself in will be determined by your account size, this percentage and the margin or perceived maximum risk of the market in question. Optimal f is a technique, popularized by Ralph Vince, that falls under the category of fixed-fractional.
However, a problem with the basic concept is that it does not work if the amount won or lost in each event differs. This requires a modification to suit trading applications. Vince developed his own set of equations based on the concept of a Holding Period Return (HPR), the rate of return on any given trade plus one. Hence, a 10% return equals 1.10 and a 25% loss equals 0.75. The formula is:
HPR = 1 + f * (-T / BL), where
f is the fixed fraction of the account to trade,
T is the profit/loss of an individual trade, and
BL is the largest losing trade of an entire sequence of trades
This calculating is applied to every trade and when we multiply HPR for each trade, we obtain a multiple of our original stake, the Terminal Wealth Relative (TWR), which is:
TWR = Product (1 + f * (-Trade / Biggest Loser))
The TWR can be maximized by changing the values of f to find the value that produces the highest TWR. The maximum value obtained is the optimal f. Once you have the values of optimal f and TWR, you need to calculate the amount of equity required to trade one unit (U), as below:
U = (ML / - f), where
U is trading units in dollars,
ML is maximum loss in dollars, and
f is the optimal value for f
Using the starting account size, trading units in dollars, and trade history, we can simulate an equity curve for any trading system using optimal f. The problem is that with even a moderately successful trading system, optimal f can call for trading more contracts than is realistic. Another problem is that in an unmodified state, returns of optimal f are based on trading fractional contracts.
Another issue is the requirement of the largest losing trade for calculating TWR, which affects optimal f in a big way. This is not a problem if we work with historical simulations. However, in a real system having different volatile protective stops, defining this going forward is not possible. One way out is to have an excessively large dollar stop. This should not affect the system backtesting, but future results would be affected, as the fixed stops cannot adjust for volatilities.
The other major class of maximizing return is fixed-ratio trading. Developed by Ryan Jones, fixed-ratio trading, or fixed-ratio position sizing, focuses on the amount of profit required to increase the number of contracts by one. Jones called this amount the delta.
So, a delta of $5,000 would mean that if you are trading a single contract, you need to increase your account equity by $5,000 to trade two contracts. For three contracts, you need an additional profit of $10,000, and for four contracts, an additional profit of $15,000. Here’s the equation:
N = 0.5 * [(1 + 8 * P/delta) ^0.5 + 1], where
N is number of contracts,
P is total closed trade profit, and
delta is the value you input for increasing your trade size
The profit (P) is the accumulated profit over all trades leading up to the one required to calculate the number of contracts. This means that the number of contracts for the first trade is always one, as the starting profit is zero. Moreover, as profits accumulate, the number of contracts increases more slowly — that is, a $5,000 profit made earlier in a trade sequence increases the number of contracts more than a $5,000 profit made after subsequent profitable trades.
Considering the fixed-ratio equation, the trade risk is not as much a factor as in the case of fixed-fractional trading. The fixed-ratio method works better for new traders because the number of contracts always starts at one. However, for larger accounts, to increase the same number to a level that takes full advantage of the available equity might not be feasible due to time involved in recalculations.
While each of these money management techniques has pros and cons, the big one is inescapable. If the system fails, you cannot do anything to prevent the loss of gains. That is why a solid trading plan — from the system to your execution method—is critical to long-term trading success. In the next installment of this look at trading plans, we’ll more closely examine some specific approaches from top to bottom.
Murray A. Ruggiero Jr. is a consultant in East Haven, Conn. His firm, Ruggiero Associates, develops market-timing systems. He is the author of “Cybernetic Trading Strategies” (John Wiley & Sons). E-mail him at
ruggieroassoc@aol.com .
