Our Necessasary Keys To Robust Trading Systems

With pcs as powerful as they are these days it is simple to optimize a trading system causing it to appear exceptional, but an optimized system is not necessarily a dependable system. Just simply because a investor can train a pc to have 20/20 hindsight does not mean that future performance will be anything like the past.

The primary dilemma with optimizing past performance is that markets transform. A low-volatility market abruptly turns into a high-volatility market. A market inclined 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 occur is that market X will tend to start acting like market Y, and market Y will tend to start behaving like market Z. If a investor has completely optimized his system to trade market Z, then he will be in trouble when it begins to trade like market X! This is a challenge with numerous systems, usually stock index systems that tend to be optimized to one market or sector. In spite of their periodic remarkable looking results, there’s some poison in their mixture.

Compare this last scenario with one in which the systems model works nicely with most the markets, A thru Z. Now, it will not matter when market Z starts to behave like market Y or market A starts to act like market P. They can change as many times as they want since the systems design will be globally robust with most ALL the various markets. Once again, the market traits can reshuffle countless times and the system works like a Swiss army knife that has proven throughout historical testing it can cope well with most all those situations.

Generally there tend to be a number of tip offs to an optimized system.

1. Unlikely looking performance

2. Just trades one market or sector well

3. Uses different rules (algorithms) for each market

4. Utilizes completely different inputs for each market

5. Uses differing rules or inputs for entering buys and sells

6. Does not calculate in realistic transaction costs (slippage & commission)

7. Utilizes money management techniques that do not incorporate market normalization (like single contract performance only)

8. Utilizes static numbers for all markets like a $2000 stop or $5000 profit target (some markets can hit those in an hour and others could take weeks).

An crucial feature of a robust system is that it must weight every market equally. The testing should be carried out in a way that normalizes the variation between the markets. For example, natural gas moves an average of a few thousand dollars a day for each contract; but, Eurodollars change an average of a few hundred dollars a day for every contract. Investors need a method to balance and normalize this variation in testing.

The reason investors want to do this is that what if the system fulfills most of the above non-optimized rules, but it is trading a single natural gas market contract for every single Eurodollar contract. The system will appear best if it experienced many natural gas winners, but what if natural gas begins to get many losing trades and the Eurodollar starts to have many winning trades? Will a couple of, 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 have diversification, but if he is trading them all on a one contract basis then he is definitely not diversified. Traders may well have 25% of their portfolio creating 90% of the profits and losses! The issue is that moving forward they will be dependent in those markets. It is far better not to be reliant on any given market within the portfolio. They ought to all be of the same weight and significance.

In summary, a robust system ought to do the following.

1. Trade a portfolio of Just about every futures market

2. Trade that large portfolio over a lengthy test interval

3. Use the same rules for each market

4. Utilize the same input values for every single market

5. Have the identical (inverse) logic for getting into buys and sells

6. Factor in realistic transaction costs

7. Normalize the markets for risk

Following all this, the final step would be to do some walk-forward testing. This means, test and create systems on data up till year 2000 (for instance). Then after doing all the testing, see how it would have done from year 2000 till now. This would help prevent many advantages of hindsight. These are all things we perform in the trading systems creation process here at DH Trading Systems.

It is one of the most powerful and least understood concepts with many traders. My research has shown that short term (not day trading) systems can have low correlation to longer-term systems. Some trading systems like moving average systems do not know how much risk they are taking. Trading Systems