Apologies if this is not the correct forum for this question.
Access to high frequency data (trade data, quote data, limit order book updates, etc.) is currently relatively easy through various public or minimally-private endpoints (Crypto Exchange APIs, Broker APIs, etc).
It seems like it would be informative to be able to utilize this information in trading strategies or analysis that act on or are constrained by longer timeframes without the potential loss of information or flexibility that downsampling, to say evenly spaced bars over time or volume, may incur.
With the widespread availability of real-time data of such granularity, I'm wanting to know if there is some common development or analysis framework that I'm not aware of that lends itself to analysis/making predictions/taking actions/modeling the impact of said actions/etc. based on said data.
As a more specific example, say we start with a snapshot of the limit order book for some liquid asset traded on a centralized exchange and receive real-time updates of changes to the LOB as they happen.
In this case, for whatever reason (lack of colocation, latency, infrastructure, etc.), we have a delay of $\tau$ between when the LOB update actually happens and when our decided upon action reaches the exchange where $\tau$ is notably larger than the time between updates.
In this situation, is there a way to leverage the information contained in the real-time updates in a way that accomodates being able to act with a delay of $\tau$ while still giving us the flexibility of not working exclusively with downsampled evenly-spaced and/or lower-frequency data?
I'm not sure if this dips over into the proprietary or if simplicity dictates just dealing with the benefits/constraints of evenly-spaced/lower-frequency data or constraining our approach to avoid actions that hinge upon acting with a lower delay than $\tau$, but if possible, any references would be greatly appreciated.