Let's say we are given 10 days of pricing data, and the work of technical analysis is to draw the lines on the right to make sense of the trend in order to generate the next day's pricing for the 11th day. It is quite obvious to find that it is indeed what a convolutional neural network could tackle.
Knowing that, practically, the time unit we are looking at could be per 100 ms or 10 ms instead of 1 day, but the principle will be the same:
Let's continue with the Duke Energy example. In this hypothetical case, we assume that we are the treasurer running the pension fund plan of Duke Energy with a total asset size of 15 billion USD with a defined contribution plan. Presumably, we know what our IPS is in digital format:
- Target return = 5% of real return (that means deducting the inflation of goods)
- Risk = return volatility equals 10% ...