DB

[CryptoAdam @_crypto_adam_]

R1CH $GRODT

6V4QLouv2HTbae34hBjLgW5Qc3kdquqFLBsDS3scR1CH

[Get Rich Or Die Trying @grodtsolana]

Crypto isn’t for the cautious.

It’s for the maniacs.

The ones who understand glory lives on the edge.

$GRODT isn’t safety.

It’s survival It’s the mark you carry when you bet it all.

Get Rich Or Die Trying.

[Get Rich Or Die Trying @grodtsolana]

$GRODT isn’t a token, it’s a creed.

Every chart is a battlefield.

Every bag is a weapon.

[DB @ceefax84]

@harambro6900 @TheCryptoDog @himgajria \

[harambro6900 @harambro6900]

@TheCryptoDog @himgajria $ASYM is that coin

In hindsight it will be obvious

[0nlyfibs @0nlyfibs]

@funghibull @shillrxyz @bernardo_cafe @ASYM41b07 @mungimungimungi @mungimungimungi has not stopped building/shipping since you hosted this 8 months ago. Things are getting exciting. Any interest in getting him back on to hear where things are at?

Those that are aligned know and have been accumulating as much as they can.

[DB @ceefax84]

@mungimungimungi \

[mungi @mungimungimungi]

i hypothesized that a new LLM pipeline
would make a better trading agent.

THE RESULTS ARE IN:

1. dramatically correct in some cases
2. dramatically incorrect in other cases

let's discuss

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The core change was moving from a single LLM call to generate a trading hypothesis to a multi-step LLM approach.

The assumed benefit is that we could apply more intelligence and parameterization to the hypothesis generation pipeline that @ASYM41b07 uses to ideate trading strategies in a way that would produce more profitable trading strategies.

This worked.
It also failed.

The multi-step process introduced new features/approaches to support the LLM calls:

🤠 alpha rotation (to force ideation diversity)
🧐 feature agnostic ideation w/ remapping
🤓 market-informed signal logic
🤪 market-informed risk parameters
🥱 strategy-aware rate limiting

Much of this was done in response to analysis of strategies produced from our first Benchmark (B1) where we requested ASYM to generate, backtest & eval 140 strategies.

After we made the changes to the LLM hypothesis-generating pipeline, we ran an additional Benchmark (B2) to compare the efficacy of our changes.

The good (as hypothesized):
- Avg PNL improved bigly: -3.2% -> -.62%
- Avg Win Rate UP bigly: 22.7% to 32.8%
- Drawdown down bigly: avg max DD 2.97% -> .55%
- Diversity Improved smoly: avg jaccard .082 -> .067

The bad (unexpected & undesired!!!):
- Strat prod funnel shrank: 70% success rate to 49%
- absolute PnL right tail clipped: 3.3 -> 0
- Sharpe mean fell: -.46 -> -.73

key takeaways:

a) we can definitively influence the trading capacities of the system, and on our first go, we've shown we can make strats move up and to the right. this is a big demonstration.

b) atomizing the hypothesis generation pipeline made the system less effective at producing strategies that are backtest-able/functional; in isolation, this isn’t a massive issue, mostly adds cost overhead, and should be reasonable to improve.

a) a real issue that we'll have to focus on: our changes capped any strategy’s capacity to produce profits. the new risk setting logic (re TP/SL/hold-time) made Strats safer but also capped winners. Win rate went up but profits never ran—— Benchmark 1 actually produced more profitable Strats than Benchmark 2!! Given we can produce n strategies on demand, we care more about producing any number of pnl positive strategies than we do the average pnl across generated strategies. more intelligent risk parameterization will be a focus moving forward.

Hypotheses are always just hypotheses, ynkuyk. That said, we saw some awesome things here. And some unexpected that help shed light into where to go next.

Some other smol bugs identified through this process to fix in the shorter term. Then we take some more swings; will share more on this soonly.

Progress every day.

ASYM41b07 continues to move up the curve.

gigabrain trading agent soon.

Yes, yit.

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[DB @ceefax84]

@mungimungimungi \

[aligned\ @logandockery03]

@imperooterxbt check out somenthing useful scooter
https://t.co/lWsgeS10cl

[DB @ceefax84]

@CrashDavis010 @mungimungimungi @ASYM41b07 \ aligned as always here for the journey

[DB @ceefax84]

@whalewatchalert @JewPeter33 \

[DB @ceefax84]

@mungimungimungi \

[DB @ceefax84]

@PenguinOffisial Agreed asym has alot of potential. Great dev involved and solid community. Updates are following and engagement is high ! $asym

[Penguin @PenguinOffisial]

$ASYM is a sleeper unicorn. I don't think this will go hidden for too long. Dev previously worked on $BOME.
Still only around $1m marketcap. 1000x+ potential.

[DB @ceefax84]

@mungimungimungi Mungi another awesome update \

[mungi @mungimungimungi]

WE ARE SO BACK.

ASYM AI can now generate AND backtest hyperliquid trading strategies… in 5-10 minutes.

The new backtesting engine:
- can analyze 1000+ trades in under 30 seconds
- provides penny-accurate P&L reconciliation
- complete audit trail for every trade decision
- w/ 100% deterministic results

Weeks to months of manual work for quant funds,
compressed into minutes.

————————————————————————
additional context & follow ups:

1.
For those just joining: this is a secondary part of a broader trading system: the broader system tracks tokens on Hyperliquid and leverages an LLM pipeline to hypothesize and develop trading strategies——— ;

A + B is much more interesting than B alone: we can generate infinite strategies & filter them down via our backtesting engine. Very cool!

2.
The backtesting engine had been causing a ton of issues; when I first started to develop the backtesting engine, I made 2 big architectural mistakes:

first, I mirrored the live trading system almost 1:1, including setting up mock broadcasting services and mock execution services as external services that a backtesting simulation could connect to in order to replicate the live trading system’s network constraints; this worked but backtests either took too long or ran into bottleneck issues via those exact network constraints; I decided then to move away from that approach but still built on top of a lot of the infra that I had set up at that point, which became very messy, tens of thousands of lines of unnecessary code, etc.

second, I had wanted the LLM pipeline to produce a single strategy class that was environment aware and capable of working within the backtesting environment or the live trading environment; maybe someone else will figure out how to do that, but I struggled, only able to get it to work properly in one or the other, I hypothesize because the environments were different enough that it was difficult for the LLM pipeline to build code that was environment aware, and paired with my then-disgusting-frankenstein backtesting environment, things were brutal to debug.

so I capitulated.

I took the learnings from the first pass at the backtesting engine and rebuilt it from scratch, creating a nice clean and easy to work with backtesting environment.

I built a secondary LLM pipeline that could reproduce the trading logic and constraints perfectly but that would produce code that could work in the backtesting environment.

and that’s what it took to get this working.

3.
Right now I can tell you with confidence: the system would not deploy many strategies, as most are definitively losing strategies atm, but a) we can filter those out, as that’s what the backtesting engine is for, and b) we can now measure the overall system’s ability to trade and we can see how that changes as we add new features and improve the broader system.

I will share more on this benchmarking process s00n along with other details.

mungi johnson

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[DB @ceefax84]

@mungimungimungi Log off time gets slightly closer

[mungi @mungimungimungi]

Bitcoin.
Fartcoin.
Ya mama's coin.

This AI system can LITERALLY create algorithmic trading strategies for any coin on Hyperliquid.

There were 3 things we had to nail to get AI strategy generation to work consistently:

1. Strategy Protocol
2. LLM Orchestration
3. A Strategy Runtime Environment

Each of the pillars cooperates with one another and has been built with the others in mind.

The Strategy Protocol:
> Exposes data streams to strategies
> provides a single, auditable-path to use an execution service
> enforces Take Profits, Stop Losses, and Time Walls for risk management
> reports heartbeats for liveness checks

LLM Orchestration
> Leverages the Strategy Protocol to inform LLMs of system requirements
> Enables causal reasoning for hypothesization
> Determines the best data features available given the hypothesis
> Sets Triple Barrier params that are enforced by the protocol
> Generates code that extends the Strategy Protocol
> Performs QA
> Iterates as needed

The Runtime Environment
> Takes in strategy code
> Builds a per-strategy Docker image, caps CPU/RAM, network-whitelists ancillary services for data and execution, and injects env details
> Runs the strategy code
> Has an external watchdog to kill & re-summon any container that misses heartbeats

Groundwork laid--------- we can programatically, consistently generate functioning algorithmic trading strategies for any coin on Hyperliquid.

Anyone else starting to feel a little bricked up about this AI shiii?

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[MOONDOGE @moondogecoin_]

@Vivek4real_ Mainstream moon mode 🐶🌙📢