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Suppose I have an algorithm that provides a prediction of the daily high and low of some security n periods in the future, a confidence interval for each of these predictions, and the correlation coefficient for the errors of these pairs of predictions. And of course, I have the current price now. Suppose, moreover, that all of these predictions are well-validated and correct – not perfect, but with the imperfection correctly described by the terms given above.

Each time a period (whether hour, day, week, or whatever) goes by, I then do a new forecast with an additional period's data, and get all the same estimates.

Although that seems like a lot to know, and like more than most investors do know, I am having great difficulty in translating this prediction information into a trading strategy. By a trading strategy here I mean a time path or decision rule that tells me whether I initially go long or short or wait, and then, having established a position, on doing my next estimate, whether to liquidate my position, double up on it, liquidate and go the opposite way, wait and do nothing, or what? I am essentially looking for the strategy that lets me use these forecasts to profit from short-term price movements. It seems like I have learned a lot about forecasting, and about portfolio construction, but almost nothing about this sort of, I suppose one might say pejoratively, speculative trading.

Is there a literature that describes optimal trading of a single security given information of this sort? Are there standard strategies in common use, that I could compare and choose between?

Note that I am not looking for the sort of strategies that I think of as more typical of technical traders. My forecasts are from ensemble averages of, e.g., penalized cointegrated VECMs, penalized ARIMAX models, and other models of that ilk. I want to construct strategies based my forecasts, not on whether I have bounced off a 20-period moving average.

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  • $\begingroup$ Hi: I follow some of what you said but the part that confused me was : "each time a period goes by, I then do a new forecast with an additional periods data and get all the same estimates. ". I'm picturing that, at the beginning of the day, for each stock and each day, you have A) the high and low predictions for the respective stock, B) the variance of the high prediction and the variance of the high prediction and C) the covariance of the high prediction with that of the other stocks and the covariance of the low prediction with that of the other stocks. But that may not be correct ? $\endgroup$
    – mark leeds
    Nov 24, 2023 at 9:10
  • $\begingroup$ So, my understanding is that new estimates could only be created after another one day has passed ( since, the prediction is daily ? ). And why would they stay the same ? $\endgroup$
    – mark leeds
    Nov 24, 2023 at 9:14

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