Arkada

Loving that this comes out of a market news pipeline. We have something similar but smaller scale in our trading bot app where we are trying to score incoming crypto/macroeconomic news based on fixed questions like importance, direction, and coins affected.

Doing that via standard chat API is just a bit too much if all you need is quick yes/no or scoring. Adding `floor_p` for the user to see the baseline bias of the model when given nothing is such a great feature as well.

How difficult would it be to calibrate the custom prompt pack on my own data set?

This very thing. "Shipment of 40% more bugs and fixing only in response to user complaints" could be done in social streams or advertising tech, but not in finance and trading where margin for errors is close to zero.

A small bug implemented by an agent in the way R-multiples, stop loss distance, or P&L heatmaps are computed will never be complained about by the user—either he will trade incorrectly and lose his money, or see that single off-by-one difference compared to exchange ledger and churn forever.

Most ideal equities markets operating model would be a short auction period at the open and the close

This is almost exactly aligned with real life trading experience with retail traders. Retail trader P&L map by weekday and time of day shows that the overwhelming majority of retail losses fall into one of two categories - either choppy hours during the day where liquidity is low (noise from overtrading) or 15 minutes after the NY open where the volatility spike happens and stop hunts take place.

Extending equities to 24/7 with tokenization only increases the amount of low liquidity chop, which is good for exchange fees, but not for retail expectancy.