CanOz
Home runs feel good, but base hits pay bills!
- Joined
- 11 July 2006
- Posts
- 11,543
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- 519
I see, its like the vix for bonds....volatility has dropped so much, so thats why system did well recently and not in the past. The market has changed.
Thanks Sinner.
CanOz
This is the Hang Seng with an optimized ATR trail and trade carry over to the next session....yeah i know but i does some sweet things to the PF.
CanOz
The DAX is untradeable. In desperate need another filter of some sort.c:
Yes Canoz must be tempting to buy the system and trade it , if you bought the open code at least it is a learning excerise as well but depends how much it is I guess
Check out this system that trades 5 contracts on the HSI. It scalps for quick profits and only trades for the opening 45 minutes. It back tests over as much OOS data as i can throw at it with the same results.
Can anyone tell me the problem with it? OR at least what i suspect the problem is...
CanOz
Suspect either some untradable condition
First, let me introduce one way to measure mistakes' impact on your trading. Trader efficiency is a measure of how effective a trader is in making mistake free trades. So a person who makes five mistakes in 100 trades is 95% efficient. In the last five years I’ve requested that my Super Traders document their mistakes so that we can look at their efficiency levels. I have found that 95% is actually a very good trading efficiency level; many traders can’t even trade at 75% efficiency””which is terrible. That’s one mistake in about every four trades.
He's taking the p!ss.... surely??!!
:freak3:
http://www.traderplanet.com/newsletter_articles/view/8068/distribution:8/
in mistakes.
So let’s look at the psychology of trading from the angle of mistakes. When you don’t follow your rules, you make a mistake. It’s that simple. And making the same mistake repeatedly is called self-sabotage. Self sabotage is another area of psychology rich with the opportunity for understanding yourself to improve your trading results. Here, however, we’ll focus on mistakes relating to some broad categories of traders.
First, let me introduce one way to measure mistakes' impact on your trading. Trader efficiency is a measure of how effective a trader is in making mistake free trades. So a person who makes five mistakes in 100 trades is 95% efficient. In the last five years I’ve requested that my Super Traders document their mistakes so that we can look at their efficiency levels. I have found that 95% is actually a very good trading efficiency level; many traders can’t even trade at 75% efficiency””which is terrible. That’s one mistake in about every four trades. This is most important for one category of traders: rule-based discretionary traders. In my opinion, when rule based discretionary traders become efficient, they are by far the best type of trader.
There are two other groups of traders I’d like to talk about: 1) mechanical traders and 2) no-rule discretionary traders.
First, we’ll look at mechanical traders. Mechanical traders believe that they can eliminate psychologically related trading problems by becoming mechanical. Many people aspire to be mechanical traders, letting a computer make all the decisions for them, because they believe it factors out many human-based errors.
In fact, one of my best trader friends said to me once that psychology didn’t enter into his trading because his operation was totally automated. My response was “You could decide not to take a trade.” About 18 years after I made that statement, his CTA business closed down. His partner decided against taking one trade””the trade that would have made their entire year had they taken it.
I’ve always said that people can only trade their beliefs about the market, so let’s look at some of the most important beliefs that a pure mechanical trader might have:
With mechanical trading, I can be objective and not make mistakes (except the psychological mistake of overriding my system).
Mechanical trading is objective. My system testing will allow me to determine my future results.
Mechanical trading is accurate.
If a system’s underlying logic cannot be turned into a mechanical trading system, it probably isn’t worth trading.
Human judgment is too prone to errors. I can eliminate those through mechanical trading.
So then, is mechanical trading truly objective? I tend to think not because there are all sorts of errors that can creep into an automated trading system: data errors, errors in the software platform, errors in your own programming, and many more. (Interestingly, one of the main categories of errors that my Super Traders come up with consistently is programming errors.)
Let’s consider data errors. Is your data accurate or does it have bad ticks and other issues with it? Mechanical traders are always dealing with data errors of some sort. For example, price errors can show up in streaming data quite regularly. Sometimes those are resolved within seconds and the error “disappears” but, by that point, the bad data may have triggered a trade already. Additionally, historical stock data may or may not have dividend and split adjustments. And what happens when a company goes bankrupt? What if it goes private or is bought out by another company? Those companies’ data may simply disappear from your data set.
I once wanted to research an efficient stock trading system. We looked for efficient stocks (moving up without much noise) and bought them with a 25% trailing stop. We had an S&P 500 data set going back 40 years that was supposed to be clean and adjusted for splits and dividends. I was very pleased with the results because my system made a small fortune. I didn’t realize this at the time, however, but the system traded on “inside information.” Because of the data set, I was able to buy stocks at the IPO that would later become part of the S&P 500. Thus, my system, in back test, bought Microsoft, EBay, Intel, and many other companies before anyone knew they would become part of the S&P 500. Why? Because, as I said, my data set was today’s S&P 500 going back 40 years.
And what about Thursday May 6th, 2010? The Dow Jones dropped 1,000 points in the space of about 20 minutes. Blue chip stocks like Procter & Gamble dropped over 20 points, and Accenture even went to a penny per share briefly. While there may not have been one root cause for that mini-crash, it had a major effect on mechanical trading systems. Things like that happen in the markets; such are the challenges (error/mistakes) for mechanical systems. (That afternoon’s swing affected lots of regular traders, too. One client said he used 25% trailing stops on all of his positions and got stopped out of every single stock.)
Meanwhile, one of our instructors, Ken Long, trades rule-based discretionary systems and made 100R in that same week. As always, he was very conservative in his trading and very careful to make sure that he fully managed his risk.
There is another class of error that is made by mechanical trading systems: the error of omission. Because the criteria by which trade setups and entries are so precisely defined, mechanical systems miss many good (or great) trades that a discretionary trader would spot easily.
For example, suppose your systems screens for five consecutive lower closes. After you get five consecutive lower closes you then look for an inside day. Now you have your full setup. Your entry is a few cents above yesterday’s high.
So let’s look at some examples of other entry signals you might miss by being so precise. You could have four down days that were extreme””perhaps the price is down 30%. Or you could have less than four down days that where the price is 30% lower or more. However, neither of those example would be an adequate down move according to your strict mechanical criteria.
Let’s say you found something that had five days of new lower lows but the fifth down day might open on a new low and then close on a new high. That’s usually an extremely bullish signal, but you’d miss it by your precise definition. Or, you could have five days of lower closes and the sixth day opens on a new low but closes on a new high. Thus, the precise entry definition would miss a trade opportunity with even more weakness followed by an extreme bullish signal.
There are a lot more variations of this entry that a mechanical system would miss, but you get the point. As soon as you state your rules so precisely that a computer can execute the trades, you open yourself to errors of omission””good or outstanding trades that your automated system cannot take because of its precision.
Those missed opportunities don’t qualify as mistakes but they severely limit the potential results of the underlying logic behind the system. The mechanical system results will look rather weak next to the results of a trader who used that same system and was allowed some discretion to take the all of those other trades that didn’t quite fit the precise mechanical system rules.
Next week we’ll look at mistakes and another type of trader: the no-rule discretionary traders.
Dr. Van K. Tharp is the founder and president of the Van Tharp Institute and stands out as an international leader among professional trading coaches and consultants. Helping others become the best trader or investor that they can be has been Tharp’s mission since 1982.
Tharp collected more than 5,000 successful trading profiles in a 10-year study of individual traders and investors
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