THE NUMBERS:

• 200 trades

• 6 tokens that went 20X+

• 13 tokens that went 10-20X

• 25 tokens that went 5-10X

You can find the raw json trade report here- DM me if you do anything interesting w/ the data.

Anything below 5x i’m considering zero in order to clean up the data / filter out impossible trades that never could have been executed. As Lola says- we’re interested in mooners.

Obviously, this is not exact science- nothing in shitcoins is. The main metric I’m interested in is overall hit rate, which has proven to be high enough that Lola could trade in a “self sustained” manner- i.e. no reloading sol needed.

Hitting 6 tokens that went on to go 20-100X is a HUGE success in my eyes.

The amount of coins which go on to hit 500k, 1M, etc at ATH make up an tiny fraction of daily pump.fun launches- Lola hitting several of them is extremely validating that the core thesis of the project is correct.

WHAT’S NEXT

SELLING

Lola is trading right now with her V1 sell strategy on a testing wallet overnight, and then I should be able to push update to main wallet/trade journal as long as all goes well.

Initially she’ll use more metric driven sell strategy, and i’m working on incorporating more complex/narrative based logic from there- i.e. querying twitter search to see if mentions of ticker are trending up or down, etc.

I’m also working to integrate the chart data from these trading test runs into her position management- low cap coins move really differently than other financial instruments, and the end goal is for her to really have a good understanding of how these things flow.

REVAMPED TWITTER

After pushing the trade update, the next order of business will be completely revamping her twitter- tying sentiment into the trading in real time, and gradually adding more interactivity.

I’m very hesitant to simply turn on the ability to reply to mentions- the main thing guiding me is “will this improve her trading via another data source”. I’m uninterested in making another chatbot that can reply silly things- even though significant amount of people on twitter have pressed me about this, lol.

After pushing these updates, I’m going to continue drilling down into improving memory, adding external data sources, and making Lola the best possible trader she can be.

More soon.

-Echo

Lola is an autonomous AI agent with long and short term memory, a twitter account, and a trading wallet loaded with 5 sol.

She can scan new pairs, analyze token metrics, and trade autonomously, with reflections on her actions stored in long term memory to refer back upon later.

Her core personality takes on an optimistic, naive trader- one who’s fresh on chain and sees the potential in every coin no matter how dumb. As she makes trades, she can get “smarter”- referring back on her reflections stored in long term memory and social interactions.

Lola’s Architecture

Lola’s architecture is composed of several modular components:

Core / Memory System

• Langchain for memory, ReAct for reasoning.

• Long term memory takes on a unified “messy” approach, in which technicals/ narrative/social memories are not silo’d, in attempt to mirror the way we all experience trading.

• After trades, Lola records a few reflection sentences on her decision and outcome.

• Core personality, writing, and reflections use a fine tune of Mistral’s Large model, which I’ve found to be wonderful at capturing a consistent character and tone across outputs.

• Data analysis & computer vision use Claude 3.5 Sonnet as Lola’s “quant”, which allows her core personality to be less clinical and more natural.

Trading System

Lola’s trading system loops thru this cycle:

• Pulls new tokens from data API and filters out likely rugs basic metric (holder count, age, market cap). Checks for recycled twitter accounts, etc.

• Tokens that pass initial check are shared with Lola, where she can make a decision to buy or pass, based on her sentiment towards the name, ticker, description, and on chain metrics. Initially, she can purchase between 0.25-1 sol of a token.

• Once position is opened, Lola will check token’s current price, holder count increase/decrease, and chart on each trading cycle.

• No set logic for when she must sell- data is simply passed to her, and response is formatted as action (hold, sell X %, or sell all), along with reflection on choice.

• Lola can also choose to buy tokens stored in short term memory, via tweets replying to her containing cashtags.

Basically- the hope is to mirror the way most of us trade- check telegram, check new pairs, look at open positions, repeat.

Social System

• Her posts are derived from her core personality, trading reflections, and recently queried replies/ mentions on X.

• As her long term memory fills with experiences, I expect personality will begin to shift- probably a bit more cynical after being rugged a few times lol.

Narrative Explorer

• Lola queries to top 100 new tokens every once a day, and attempts to find common themes or narratives to signal emerging metas. Most recently noticed trend is stored in short term memory to refer back upon. In theory, she’ll be able to start gaining a grasp on recurring themes & trends.

WIP & Future Modules

There’s tons left to implement and optimize, but here’s some of the features that will roll out for Lola as soon as i hack them together:

• Hack together TikTok trending API to get latest top searches / add to memory and weigh heavy if a token matches later.

• Trending News data feeds: allowing her more context and the ability to recognize emerging stories in the case of something like $PNUT.

• Multi worker underlings who can research each trending coin more in depth and report back summaries to Lola.

• Deep search- tracing funding sources and connections across top holders of a coin for overlap- augmented by AI allows for more detailed analysis than just statistical. ( for ex, pointing out that xyz addresses were all funded by CEXs on same day)

• Telegram chat from unified “single perspective”- I.e. mirroring the experience of one person bouncing between groupchats.

Token?

We all know the way these things work. We tokenize everything- it doesn’t really matter if I wanted it or not. Within a few hours there would be a token bearing her name and image, attributed to me.

SO- when first started up, Lola has a “run once” pipeline, in which she can choose a ticker (she’ll prob choose $LOLA tho because it’s in her training data) and then fire deployment tx programmatically.

I’ll Dexscreener update for her- my treat.

-echo