Stat Arb

MFT & HFT | Views my own. Not financial advice.

[Stat Arb @quant_arb]

@noah_nccl Ik

[Stat Arb @quant_arb]

Almost everything has converged to AWS Tokyo. Even Okex moved from
Alibaba HK. Few outliers still. Save some time:

Binance : ap-northeast-1
Coinbase : us-east-1
Bybit : ap-southeast-1
OKX : ap-northeast-1
Kraken : Equinix London
Bitget : ap-northeast-1
KuCoin : ap-northeast-1
https://t.co/deC1QcLqBD : ap-northeast-1
HTX : ap-northeast-1
https://t.co/m3LOoucgWK : ap-northeast-1

[Lihong | Freeport @Lihong062]

>friend works on FX vol desk

>desk head is a boomer

>discovers Astra

>starts spending 8 hours a day talking to it

>develops terminal AI psychosis

>convinced he's discovered a generational arb in some absurdly exotic part of the FX vol curve

>apparently the entire market has been mispricing this for 2+ years

>friend points out that approximately 4 people on earth would make a market in this thing

>liquidity disappears the second you put real size on

>existing risk systems can't even capture half the exotic Greeks

>would need entirely new infra just to monitor the position

>boss doesn't care

>"AI has considered all of this"

>friend actually runs the backtest

>10 years of data

>t-stat ≈ 0.78

>not even remotely statistically significant

>presents results to boss

>boss goes absolutely ballistic

>starts screaming that friend doesn't understand the trade

>threatens to fire him if he can't produce a proper analysis

>friend realizes what needs to be don

>opens Astra

>"make this strategy look statistically compelling"

>AI tries 40 lookback windows, excludes crisis periods, optimizes parameters on full sample, ignores transaction costs

>Sharpe now 3.7

>beautiful charts

>23-page investment memo explaining once-in-a-generation structural inefficiency

>boss reads it

>"EXACTLY. THIS IS WHAT I'VE BEEN SAYING."

>tells friend he's finally learning how to think

>"now you get it"

>friend has an epiphany

>stops doing actual research

>starts asking Astra to generate exotic vol trades every morning

>forwards AI slop directly to PM

>increasingly elaborate narratives about structural dislocations and hidden convexity

>PM absolutely loves it

>thinks friend is a generational quant talent

>starts putting positions on in size

>entire investment process is now LLM generating trades

>desk currently down quarter yard

>boss still convinced thesis is right

>Astra says drawdown is a statistically rare but entirely expected manifestation of nonlinear payoff dynamics

>boss wants to double down

[Stat Arb @quant_arb]

@hyperfitted Post better

[Stat Arb @quant_arb]

How to make my PnL go up

[Stat Arb @quant_arb]

Sorry I thought this was Google

[Stat Arb @quant_arb]

@DataShill Sorry I don’t keep an up to date list of which D tier unis have a money printer degree

[Stat Arb @quant_arb]

“I couldn’t get in after bachelors so I did financial engineering at Ohio state to get recruiter to Optiver”

[Stat Arb @quant_arb]

@adaYen72 Judgement

[Stat Arb @quant_arb]

How to think about fitting and automation of my job:

Think about fitting as a budget. Unlike an actual budget there is not a hard limit and you can’t 100% know where it lies but you can get a very good estimate of where it roughly lies from testing OOS and comparing the drop off in performance.

The more data you have the larger the budget.

Every search method delivers a different level of reward (measured by OOS improvement in X metrics- ie R2) for a usage of your fitting budget. A cheap LLM with a bad framework for example might give a bad return, an expensive LLM prompted well gives a solid return and the best still remains the human mind.

Despite what people think, you can mass scale the human mind and this is what worldquant does (see WQ BRAIN) and why they have thousands of consultants in 3rd world countries who see the reward of *up to 120k* as an enormous sum of money (it’s nothing to scoff at for the average joe but given quant talent market rates it’s a great deal for WQ cost wise) and many researchers cranking out alphas on top of that. This is how they have millions of alphas. Other firms have this setup too - 2sigma being one.

Basically this is mass search using the currents best value for budget (which is human capital).

Value for budget is not always the entire equation. It’s also about what you can compute. Human compute is expensive and finding high quality humans is hard. Alpha in itself.

For MFT/LFT strategies, human approach is the optimal (except for maybe very high turnover MFT strategies, where it borders on HFT but I’d say that’s more hybrid). This is because the budget is small so the number of alphas you can search is within the bound of what’s reasonable for a human.

For HFT you often hit limits where even computing the alphas you come up with can get extremely expensive due to the huge amount of data. Fitting becomes less of an issue and we cannot really hope to achieve much success manually searching the space because assuming we can afford a lot of servers to let the LLMs test to their hearts contents we will be outcompeted by the LLMs despite producing better alphas on avg just through sheer volume.

With LLMs now existing, instead of needing to brute force solutions as we did in the past, we are now seeing the non-human side of things get very competitive and is now one of the go-to solutions for research on the HFT side (not near enough for MFT yet, not as long as humans create better alphas than ai on average).

Brute force search was already a big thing in HFT before LLMs but because they were less efficient than LLMs the need for compute to find a given level of alpha was much higher and hence much more expensive and unreachable. It was also less flexible and limited to the transforms given- not to mention there are still *some* overfitting risks in HFT. It’s much less significant but still you can hit it if you try hard enough and go heavy on a local regime.

Now with LLMs it’s becoming quite reachable compute cost wise for smaller firms who have 5 figs ish to spend on compute. So we will see from here what drives edge and I think it’ll be framework and such.

I will caveat this with the one weakness of LLMs, creativity. Human and machine are orthogonal in their discovery in many ways so optimal is likely both. Both search different spaces in my experience as someone who spends a lot of time doing this.

[Stat Arb @quant_arb]

@larpcapitalwc How soon

[Stat Arb @quant_arb]

Gm

https://t.co/Xvq6UVUrKg

[Stat Arb @quant_arb]

Just go to a great uni is the answer

[Stat Arb @quant_arb]

@iarbpairs Always larp

[Stat Arb @quant_arb]

@HYPER_STITION @aincomeinvestor Come to Zug habibi

[Stat Arb @quant_arb]

@MateriaWealth I trade on binance

[Stat Arb @quant_arb]

This github repo is FREE

it contains:

The top 10 alphas for Hyperliquid, Solana, and IBKR

And all the python code is FREE

it has 56k stars on github

Link below

[Stat Arb @quant_arb]

@pedma7 it’s FREE https://t.co/RKtVzE67g0.voucher

[Stat Arb @quant_arb]

Picked off by @HYPER_STITION

[Stat Arb @quant_arb]

If you’ve made it below you are going to get picked off