Who can't predict? .... you? That doesn't mean those who trade profitability with automated predictive models, myself among them, can't do so. Keep in mind that it is a logically fallacy to prove a negative premise based on a limited set of observations.Quote from highlifejoker:
ok, whatever you think.
how about the billion dollar fund manager who had a fight with his wife the night before and decides to liquidate just to burn her ass?
what about seth tobias types? think you can anticipate their moves?
emotion still plays big part, but its now individuals emotion/ideas who control the large capital pools, not the masses of participants----if it ever was the masses. the very premise of TA is destroyed yet the TAers keep coming back for more punishment.
can't predict it, dont try.
HLJ
ps. by the way seth tobias movie/documentary on tonight CNBC.
enjoy!![]()
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My comments were partially satire on highlifejokeâs rather silly statement that computerized trading eliminates the ability to predict future market moves.
In actuality in predictive models you can detect a boundary in the patterns that have predictive power in about 1995 and 1996. I suspect this is around when automated trading became a significant driver of market moves. This complicates model training in that what is learned on historical data prior to that point has more erratic results when applied in post boundary zone testing or current live trading. For those engaged in predictive modeling I'll share my solution.
1) Model and test OOS on data prior to the mind 90's. Save predictive signal sets with decent performance.
2) Model and test OOS on data after the mind 90's. Save predictive signal sets with decent performance.
3) Compare 1) and 2) above and tag those that give the same performance in both time groups. Those are good as they haven't changed.
4) Combine 3) with 2) above and you have what will work in the market in a computerized trading environment.
Jerry030
