A thread on figuring out how much these dirty perp DEX points are actually worth 🧵
Been having some fun lately so I’m launching https://t.co/xlMEyDwnuA to value points for my fellow point farmers.
Goal: estimate how much $ those points might translate to.
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Looking to chat with people working on small capacity alphas. Currently in the trenches on the shittiest perp Dexs on earth.
Hit me up if you wanna chat/collab
For my friends who are still using UV and might be a little weary about recent compromises to PyPi packages, stick this in your pyproject.toml.
You can let all of those pip users find and report the compromises...
10% discount on vHYPE if you’re willing to take the risk of getting stuck with it.
The risk is that the team doesn’t wind down, or no new stakers come in, so you end up stuck with it.
I’m happy to hold these until they shut down or some fresh HYPE comes in. Bought some at 0.85.
for it's first weekend, @tradexyz's S&P500 did pretty well volume-wise compared to other markets' first weekends, becoming the biggest equity launch weekend ever on hip-3
@ScottPh77711570@turbofish_pk Volume and OI post TGE for many of these projects is going at least -90%.
FDV for projects already airdropped is sitting at 3/5x the OI. If you do the math the points are not worth a lot.
Better have alpha in determining which project will keep their metrics up
@klikkiklakki@goatgoatgo97862 I don’t account for everything that happens before I receive the ws message so true latency is higher.
Parsing is done with serde_json and communication through lockfree::channel.
I have pinned threads for logging, market data feed and prediction computations.
@goatgoatgo97862 sadly the rewrite is not finished yet. It's my first time writing rust so not moving really fast here.
Biggest latency will probably be due to order signing.
But for parsing + formulating a simple prediction I have:
- P50: 4us
- P99: 14us
- P99.9: 57us
@fearthasea read somewhere (think that it was written by Jeff) that with the taker bump you will trade when your price model disagrees with the MM one.
I think it' s a good mental model.
Back to modeling.
Instead of downloaded data I now use locally timestamped data parsed and stored using the same infra as production system.
This is a ridge regression only on book features (Binance & Hyperliquid feeds).
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@monkbloc@__qisoka I serialize everything for logging (have a look at msgspec) so types are converted.
You can also implement a log data class with a serialize/format method if you need to handle specific things.
Started running the system with small size.
Signing on HyperLiquid with Python is expensive.
Tick to Prediction is ~600us then smash more then 5ms on top of it just to sign the order.