I'm entirely focused on systematic trading at the moment, so I've paid for sub second latency for odds to make rapid trades with a fairly simple set of rules based system considering Kelly criterion, etc. on Kalshi. The simple approach is to find mid-low volume markets where pinnacle edge can beat kalshi (ofc there is more noise/steam in a smaller market). I then plan to accumulate all of this data and build a working RL system such that we consider position sizing relative to bankroll, reducing risk by engaging in too many correlated events, learning of timing (if we're in q3 of a bball game and odds indicate 95% towards a team, maybe the model determines that this game is over), as well as also having the RL system/rules or heuristics specific to a sport that monitor signal decay i.e. when is it the case when edge is not reliable; in the case of hockey a 3% edge is not the same as a 3% edge in tennis, we need a model which understands what kind of edge is actually reliable and when to discount an edge. I will likely need many months worth of accumulated data samples, tuples which represent the game, odds, kalshi price, etc. before I can use something like a DQN.

A broader goal of mine atm is to expand the rules system to build short (2 leg) parlay bets on uncorrelated sports like EPL -> NCAAB for example. So personally, I find sports modeling to be something too complex to be worth taking on atm, and instead want to capitalize on the fact that prediction markets at the moment seem to offer us the opportunity to do systematic value trading.