Commodity Price Quotes is a
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Today's date is a good time to think about trading for profit. There's really no good way to design a trading system without a knowledge of past prices or past patterns, we submit the following observations:
We once traded newspaper headlines only. The way it worked was this: every day, we would check the headlines in a major daily newspaper (i.e. LA Times). I would also check for major breaking stories. I would look for unexpected events which would catch most traders (and people) by surprise. For example, the Tylenol scare, Valdez oil spill, large plane crashes, invasion of Iraq, etc.* The reason I did this, was that in the past, I had observed that almost every time there would be an immediate reaction and it was generally an over-reaction.
We would track the price forward from the time of the event and when we thought the time was appropriate . . . which was my judgment call, I would fade the action . . . go long after bad news and a decline. Inevitably, the price would return to its original price or almost, sometimes higher before the event and I would get out with a profit. This worked great and I won almost every time, if price did not move at all after I got in, I would get out with only a tiny loss. The biggest problem I had with this system was that it was very boring waiting for the next news break. It was almost like what I heard a pilot say about flying an airplane, hours of boredom occasionally interrupted by moments of terror.
Another problem was sometimes entering the market too soon, especially if it was a commodity (too much leverage); then, I might have to endure a paper loss while waiting for price to recoup.
Now, assuming that this is indeed a system, my question is this: Is this a curve fitted system? When I entered the market, I had little knowledge of past price history or fundamental knowledge of the company or industry or commodity. I had to know the current price to figure out how many shares to buy. If it was a commodity, I had to know what the margin requirement was to figure out how many contracts to buy.
Since I did not look at a chart, I had no idea of what patterns might be there. Was this system subjective or objective or a blend? I did not use a computer or any fancy indicators, I just tried to use common sense and the observation noted above.
The main reason this system worked, I figured, was that it was taking advantage of human nature to sell on bad news. Others might say that it was the specialists who are the floor traders controlling the market by using the news to their advantage.
This is probably why major news breaks are released to the floor before the public!
Even the cold fusion announcement was no exception; after going straight up, palladium eventually returned to its original price. I did not fade this particular move because with an engineering background, I felt the risk was much too high. If cold fusion proved out to be true, the price of palladium would have gone through the roof, the whole world would have been affected by dirt cheap energy and I would have been stopped out with a loss.
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.
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There is no obvious best technical indicator and are reasonably convinced there is no actual best technical indicator. However, 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. Click-here for Trading Tip-of-the-Day.
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.
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 negative form of curve-fitting that involves curve fitting the data to the trading 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 100% 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
Trading system optimization adjusts the system to the price data, and the seasonality testing fits the data to the system. Both practices result in overly curb-fitted trading results offering little hope of lasting success in real-time trading.