Tip of the Month: WHEN THE MARKET IS BEARISH & IN A DOWNTREND, AVOID SELLING WHEN MARKET VOLATILITY IS UNUSUALLY LOW SINCE IT MAY INDICATE UPCOMING BULL MOVE...

To be successful, a commodity trader must grasp the basic concept of price volatility. Option values are dramatically influenced by changing levels of volatility. If volatility is low to begin with and the market begins to awaken from a slumber, you will see a small movement in the futures compounded into a disproportionately large move in the options.

To get a traders perspective, historical price volatility system (VS) charts are a good place to start; but keep in mind, much like Seasonals, nothing has to happen exactly the same as it did in the past.

Most trading software programs will calculate implied volatility; tracking this over time is probably the most effective way to know if you are selling hefty premiums or selling yourself too short. No matter how you follow volatility, you will eventually get a natural feel for what levels are opportune to sell, and what levels are best left to be bought.

Trading and System Design - Some Trading Systems are Designed to Work on Data for a Short Time Period Based On Hindsight

There is a much less obvious but equally dangerous form of curve-fitting that involves curve fitting the data to the system. We are referring to the increasingly popular practice of using a computer to pick out short time periods during which chosen markets have historically acted similarly.

For example, we might be told that over the past ten years buying silver on May 10 and selling it on June 1 has resulted in a profit every time. The obvious inference is that if we do it this year, we have a 800 chance of winning. There are tables and tables of this meaningless coincidental data being offered to traders in books and almanacs.

Seasonal Characteristics Are Highly Questionable - Part of the theory is that there is some sort of very short term seasonal or cyclical basis for the similarities, although this is patently unprovable.

A properly programmed PC will find literally thousands of "trades" like this over any fairly extensive set of data, just as an optimization involving a great number of variables will almost always find a great number of "profitable" combinations.

Data Optimization Can Fit a System to Arrive at a False Impression of a Seasonal Characteristic
The optimization fits the system to the data, and the seasonality testing fits the data to the system. Both practices result in overly curb-fitted trading results that offer no hope of success in real trading.

The Trouble Is . . . The Markets Don't Listen
Here is another example of something that initially seems conceptually wrong. We are continually told that every market has its individual character, and that therefore a trading system must be tailored to each market.

We are also told: "Don't trade too many markets because it is difficult to watch more than a few at a time," and: don't test more than a few markets because it is unreasonable to expect a trading system to work well over a range of markets."

All of these concepts seem logical at first. The trouble is, the markets won't listen. They are not predictable. They will not act tomorrow in the same way that they did today or yesterday, and you are fooling yourself if you expect them to.

Trading Systems Should Operate on a Wide Variety of Markets and Market Conditions

Trading systems should be designed to operate profitably over a wide variety of markets and market conditions. They should be simple and flexible enough that they won't be thrown for a loop by changing conditions.

There Is No Best Indicator While we are reasonably convinced that there is no best technical indicator, some are less likely to lend themselves to unwanted curve-fitting.

First, we can divide indicators into two major categories: static and adaptive. Static indicators are technical studies or other entry or exit methods that do not "flex" with changing market conditions, especially market volatility.

Good examples of static indicators are those technical studies, stops, and profit targets that are denominated strictly in dollars or market points.

Systems that Use Changeable Targets and Stops are Likely Less Curve-Fitted

Adaptive indicators change stops and targets as the markets change. When these adaptive indicators generate a trading signal, you can say that the market put you into or took you out of a position.

Examples include volatility-based entries and exists, channel breakout systems such as Donchian's weekly rule, entering or exiting on an 'n' day high or low, and using recent swing highs and swing lows as entry, exit or stop points.

As a general rule, adaptive indicators are less likely to become overly curve-fitted to the markets than static indicators because the system designer will not feel the need to optimize them.

This is not because they are any less amenable to over-optimization than static indicators, but because they adapt to changing market conditions while retaining their integrity.

Changeable Target & Stop Methods are Less Likely to Strictly Limit Losses or Profits
The main disadvantage of adaptive indicators is that they do not strictly limit a loss or accurately lock in a profit.lock in a profit.

For example, if your exit to limit a loss is a 10-day low, the 10-day low could be $500 away or $5,000 away. If your account is $20,000 in size, it seems unwise to risk as much as 25% of it in one trade, although 2.5% seems acceptable.

The same is true if you are fortunate enough to be locking in a profit. Adaptive indicators expand with volatility, making it easy for a hard-won profit to disappear as quickly as it was created.

A reasonable compromise might be to allow the markets to dictate your entries and exits under normal conditions, but if a particular market becomes too volatile, limit your potential loss by using a static dollar stop (perhaps keyed to your account size) or avoid the market altogether.

Some Systems are Designed to Work on Data for a Short Time Period Based On Hindsight
There is a much less obvious but equally dangerous form of curve-fitting that involves curve fitting the data to the system. I am referring to the increasingly popular practice of using a computer to pick out short time periods during which chosen markets have historically acted similarly.

For example, we might be told that over the past ten years buying silver on May 10 and selling it on June 1 has resulted in a profit every time.

The obvious inference is that if we do it this year, we have a 800 chance of winning. There are tables and tables of this meaningless coincidental data being offered to traders in books and almanacs.

Seasonal Characteristics Are Highly Questionable
Part of the theory is that there is some sort of very short term seasonal or cyclical basis for the similarities, although this is patently unprovable.

A properly programmed PC will find literally thousands of "trades" like this over any fairly extensive set of data, just as an optimization involving a great number of variables will almost always find a great number of "profitable" combinations.

Data Optimization Can Fit a System to Arrive at False Impression of A Seasonal Characteristic

The optimization fits the system to the data, and the seasonality testing fits the data to the system. Both practices result in overly curb-fitted trading results offering little real hope of success in real-time trading.