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Regarding the perfect trader syndrome or the tendency to fine-tune entry points, we must remind ourselves that the strength of these systems is their high winning percentages As a result, the filtering out of the majority of trades to minimize risk and increase reward will: Always reduce an already small number of trading opportunities Almost always worsen our win/loss ratio Usually transform moderately successful and robust systems into losers Gain and Maintain the Discipline to Exit with Losses It is extremely satisfying to buy low and sell high, and we all like to be right more often than we are wrong Yet without the discipline to exit with losses when dictated by our system, one bad trade will end our career as a speculator.

Business academics were thus given free rein to play God and to do away with the inconveniently nontechnical task of having to describe the actions of God s subjects Interestingly, such swapping did not go along without a ght Today s absolute mathematical dominance of B-school academics may blind us to the fact that not so long ago one could nd erce resistance to the spreading of such analyticalness It could be argued that a civil war of sorts broke out within B-schools, with those trying to preserve descriptiveness on one camp and those trying to culminate the scienti cation revolution on the other That there was resistance to the new scienti c approach in the speci c case of nance is evident, for instance, in the following testimony: In nance, one can almost sense the shifts by lea ng through the volumes.

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As the same can be said for trend trading systems, why do I place such a strong emphasis on the point here Because these systems invariably will stop us out with relatively large losses, and it is very tempting to abandon discipline regarding stop losses when the losses are large relative to average profits Acceptance of such losses means that we will have to enjoy quite a few profitable trades before experiencing a new peak in account equity In addition, because we are fading temporarily unsustainable extremes at the trade s outset, often our stops will be triggered near the ultimate high or low of the market s move Inexperienced mean reversion traders find the temptation to abandon stop order discipline almost irresistible To help with this problem, look at the net profit column in Table 3.

The early years were lled with descriptive articles, with.

9 and remember that every one of these trades was initiated when the market achieved a statistically unsustainable overbought or oversold condition Furthermore, a significant portion of the profits generated by every table in 3 was at the expense of traders in search of a bottom or top Successful mean reversion traders are not trying to catch bottoms and tops; instead, they are attempting to capitalize on a temporarily unsustainable level of euphoria or panic Such trades have a high probability of success, as illustrated by their superior win/loss ratios However, if the market continues trending in the direction of what we previously believed was an unsustainable move, then our assumptions regarding the trend were wrong, and the only acceptable action is immediate exiting of the trade..

(eg. my bank)

Asset Profit # # Max Trades Days Draw MDD MCL P:MD P:L Ratio %W Time %

to gravitate to key horizontal support or resistance levels. As the trend matures, capitulation of those seeking reversal adds to the system s profitability (see Figure 2.16).

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is differentiable at the point t = tm , and differentiating in (5.25) yields f (tm ) Qm (tm ; f ) = 1 (tm t1 )2 (tm tm 1 )2 (2m 1)! f (2m 1) ( (t1 , t2 , . . . , tm , tm )). Set m = (t1 , t2 , . . . , tm , tm ), m = (t1 , t2 , . . . , tm , tm ). Since Pm (t; f ) is a linear functional of f (t1 ), . . . , f (tm ), f (t1 ), . . . , f (tm ) (5.26)

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However, OAS analysis has certain shortcomings The greatest source of error is the potential inaccuracy of the prepayment model A prepayment model can t historical data very well, yet might not be a good predictor of future prepayment rates Changes in demographics, in the economy, the mortgage industry, technology, and the yield curve may cause future prepayment patterns to differ from past ones Furthermore, historical prepayment data are available over a relatively short time period, so their reliability is limited Consequently, even the most robust prepayment model cannot be relied upon with great comfort Models must also be updated continually to take into consideration all new information Using older models can be very disastrous, indeed.

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