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Rats beat Yalies: Doing better by getting less information?
Louis Menand's review of Philip Tetlock’s book “Expert Political Judgment" makes the point that in "more than a hundred studies that have pitted experts against statistical or actuarial formulas, ... the people either do no better than the formulas or do worse". Menand suggests that the experts' downfall "is exactly the trouble that all human beings have: we fall in love with our hunches, and we really, really hate to be wrong". Tetlock puts it like this (p. 40): "the refusal to accept the inevitability of error -- to acknowledge that some phenomena are irreducibly probabilistic -- can be harmful. Political observers ... look for patterns in random concatenations of events. They would do better by thinking less."
Tetlock suggests that humans perform worse in this experiment because we have a higher-order, more abstract intelligence than rats do: "Human performance suffers [relative to the rat] because we are, deep down, deterministic thinkers with an aversion to probabilistic strategies... We insist on looking for order in random sequences." Menand, on the other hand, thinks it's just vanity:
The students looked for patterns of left-right placement, and ended up scoring only fifty-two per cent, an F. The rat, having no reputation to begin with, was not embarrassed about being wrong two out of every five tries. But Yale students, who do have reputations, searched for a hidden order in the sequence. They couldn’t deal with forty-per-cent error, so they ended up with almost fifty-per-cent error.
But why does more information make for worse performance? We're used to seeing evolution develop optimal solutions to such basic problems as choosing where to look for food. So what's gone wrong here? If animals have accurate estimates of how much food is likely to be where -- however those estimates are learned -- then the rule of "[proportioning] their choices in accord with the relative expected rates" is the students' solution, not the rat's solution. The rule says to allocate your foraging time among the alternative locations in proportion to your estimate of the likely pay-off. That's what the students did. But the maximum-likelihood solution is to put all your chips on the option with the highest expected return -- what the rat did.
The question is how many traders reading this are TOO STUBBORN to apply this new information? I would wager more traders will cling to their "squiggly lines" than admit the rat will beat them and trade like a rat.
Can you help answer these questions from other members on NexusFi?
"Rats lay down urine trails (visible under blacklight). Like Hänsel and Gretel they only need solve the problem once. Planaria lay down mucus trails to the same end."
Mon 7 Jul 2008
Rats and Monkeys Beat Pundits and Yalies
On our latest Bloggingheads.tv chat, George Johnson and I riff on Chris Anderson’s WIRED essay “The End of Theory.” One of the essay’s implications is that dumb, number-crunching computers can do better than theory-guided human experts. This idea reminds me of a 2005 review by Louis Menand of the book “Expert Political Judgement,” by Philip Tetlock, a Berkeley psychologist. Tetlock carried out a 20-year study of the ability of 284 experts in politics and economics to make predictions about current affairs. The experts did WORSE than random guessing, or “dart-throwing monkeys,” as Tetlock puts it. Tetlock also cites an experiment at Yale in which rats seek food in a maze. Food was placed on the left side of a fork in the maze 60 percent of the time; otherwise, the placement of the food varied randomly. After figuring out that the food was on the left side of the fork most of the time, the rats turned left every time and so were right 60 percent of the time. Yale students, discerning illusory patterns of left-right placement, guessed right only 52 percent of the time. Yes, the rats beat the Yalies, just as monkeys beat the political pundits. Followup experiments showed that these disturbing findings do not hold for science journalists who graduated from Columbia.
Ok back to the discussion.. TRO I have been messing around with the strategies you mention with some market replay and came across scenarios where a target nor stop out occurred before the bar closed..
I am wondering if I should just exit the play as I have seen where the condition changes and the bar changes to the wrong color while I stay in the play...
That is my original question in All You Need thread, but I meant for EURUSD.
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For EURO FX, it seems quite volatile even for 10pips stop loss. Since you have all the stats on your side, would you mind sharing some insights on some reasonable ranges for stop loss and profit taking? Thanks.
* Price either goes up or down.
* No one knows what will happen next.
* Keep losses small and let winners run.
* POSITION SIZE = RISK / STOP LOSS
* The reason you entered has no bearing on the outcome of your trade.
* You can control the size of your loss (skill) but you can't control the size of your win (luck).
* You need to know when to pick up your chips and cash them in.
Expectancy = (Probability of Win * Average Win) - (Probability of Loss * Average Loss)
You can not control the probabilities of wining or losing.
You can not control your average win size.
The only part of the equation of the equation that you can control is your average loss size.
Account balance = $1000
Maximum Risk per trade = 2%
Maximum Risk dollars = $1000 * 2% = $20
POSITION SIZE = RISK / STOP LOSS
STOP LOSS = 10
POSITION SIZE = 20 / 10 = 2
Expectancy = (Probability of Win * Average Win) - (Probability of Loss * Average Loss)
Expectancy = (77% * 5) - (13% * 10) Note: Using minimum win size and not including break even trades.
Expectancy = (3.85) - (1.3)= 2.55
Now, this is HINDSIGHT. It is not based on what may have happened within the 60 minute candles.
But this should give you an idea that this can be EXPLOITED.
What is reasonable to one may not be reasonable to another. Some things you have to decide for yourself.