Robust Trading Systems
With computers as powerful as they are today it is easy to optimize a trading system
causing it to look exceptional, but an optimized system is not a reliable system. Just simply because a trader can train a computer to have 20/20 hindsight does not mean that future performance will be anything like the past.
The primary problem with optimizing past performance is that markets change. A low-volatility market suddenly becomes a high-volatility market. A market prone to trends becomes a choppy directionless market or, a market that previously had high leverage becomes a market with low leverage. The list is endless.
What tends to happen is that market X will tend to start acting like market Y, and market Y will tend to start acting like market Z. If a trader has thoroughly optimized his system to trade market Z, then he will be in trouble when it starts to trade like market X! This is a problem with many systems, usually stock index systems that tend to be optimized to one market or sector. Despite of their occasional impressive looking results, there's some poison in their mix.
Contrast this last scenario with one in which the systems design works well with most all the markets, A thru Z. Now, it will not matter if market Z starts to act like market Y or market A starts to act like market P. They can change as many times as they want because the systems design will be universally robust with most ALL the various markets. Once again, the market characteristics can reshuffle countless times and the system acts like a Swiss army knife that has proved during historical testing it can deal well with most all those scenarios.
There are a few tip offs to an optimized system.
1. Unrealistic looking performance
2. Only trades one market or sector well
3. Uses different rules (algorithms) for each market
4. Uses different inputs for each market
5. Uses different rules or inputs for entering buys and sells
6. Does not factor in realistic transaction costs (slippage & commission)
7. Uses money management methods that do not include market normalization (like single contract performance only)
8. Uses static numbers for all markets like a $2000 stop or $5000 profit target (some markets could hit those in an hour and others could take weeks).
An important feature of a robust system is that it should weight every market equally. The testing should be done in a way that "normalizes" the difference between the markets. For example, natural gas changes an average of a few thousand dollars a day for each contract; however, Eurodollars change an average of a few hundred dollars a day for each contract. Traders need a way to balance and normalize this difference in testing.
The reason traders need to do this is that what if the system meets most of the above non-optimized rules, but it is trading one natural gas market contract for every one Eurodollar contract. The system would look best if it had many natural gas winners, but what it natural gas starts to have many losing trades and the Eurodollar starts to have many winning trades? Will a few, hundred dollar winning trades in one Eurodollar contract be enough to offset a few THOUSAND dollar losing trades in one natural gas contract?
If a trader is trading 20 markets, it is to get diversification, but if he is trading them all on a one contract basis then he is not diversified. Traders might have 25% of their portfolio making up for 90% of the profits and losses! The problem is that moving forward they will be dependent in those markets. It is far better not to be dependent on any given market in the portfolio. They should all be of equal weight and importance.
In summary, a robust system should do the following.
1. Trade a portfolio of EVERY commodity market
2. Trade that large portfolio over a long test period
3. Use the same rules for every market
4. Use the same input values for every market
5. Have the same logic for entering buys and sells
6. Factor in realistic transaction costs
7. Normalize the markets for risk
After all this, the final step would be to do some walk-forward testing. This means, test and develop systems on data up until year 2000 (for example). Then after doing all the testing, see how it would have done from year 2000 until now. This helps avoid many benefits of hindsight. These are all things we do in the
trading systems development process here at DH Trading Systems.
Robust Trading Systems
By: Mark Soltys
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