I am interest in trading by optimally exploiting a directional forecast given by an oracle.
The oracle predicts directionally the price of an asset (higher or lower than at the moment of forecast delivery) in a forecast horizon H, say 180 minutes. The forecast is delivered several times within a single forecast horizon: it is delivered every D minutes, say, 20.
It is known that:
- The oracle is correct a certain fraction of the time, say 60%.
Furthermore, let's make the simplifying hypothesis that:
- The distribution of the absolute value of the realized price change is the same for all 4 pairs: [predicted up - right, predicted up - wrong, predicted down - right, predicted down - wrong]
A trivial strategy to exploit this oracle would be to treat every single forecast independently, and execute a trade in the direction of the forecast at every delivery, closing each order after its forecast horizon has expired.
Nevertheless, the forecasts predict quantities that are all but independent of one another (consecutive price points of the same asset at a frequency of 1/D). The forecast formulated at time T-D, for example, is relevant information when deciding what to do at time T, because it informs about the price at T+H-D, which in turn is very much predictive of the price at T+H.
How can I exploit completely all the relevant information available? I tried looking into reinforcement learning techniques such as Temporal Difference Learning, but I can't find the perfect fit. Where should I look?