I spent 2 years in the trenches of pumpfun training a model using video game logic.

Heres how my journey goes:

99.8% of tokens listed on pump.fun are a scam, but theres a clear rhythm to finding the diamonds in the rough.

Right off the bat I noticed patterns that allowed bots to get away with rugpulls:

- Trades below 0.05 SOL that are hidden by default

- Rugpulls had consistent green candles with no wicks

- The bots bought at specific rhythms

- Devs creating multiple wallets with similar amounts at the same time

All fake pretty pictures to get you to buy and become exit liquidity.

However seeing the numbers constantly all day everyday became straining and exhausting so I knew it had to be automated.

The beginning:

Like anyone else I began searching for tools or algorithms that have an edge, and having no money I couldn't afford spending hundreds of dollars on a simple OHLCV script, or use tools like Dexscreener that lag behind or dont even have data for tokens that havent graduated. The free APIs would give you a "free trial" of credits that would be used in a single day before they shove the subscriptions in your face.

The only API that was worth using for this scenario was pumpportal API (as long as you abide to the rate limits), it was the best option with no latency.

It was way better than things like Bitquery which all for some reason all want you to make an account for everything.

With access to token creations and realtime trades as they happened, I researched how to make candlesticks myself, made timeframes of my choice, and began developing my first tool to get token creations, candlesticks, and filter out the bad tokens.

After that I needed a automated system to trade for me. I began testing with LightGBM, which failed from overfitting and speed, XGboost which failed from the same thing, then finally settling on DDQN.

And yeah I named it too. RICHARD WAS BORN.

I understood DDQN the most, almost like I could talk to it. It learns in the simplest way of Good = dopamine, Bad = penalty. So I could teach it exactly what I wanted it to do.

At first I didnt truly understand everything about the model as it was still a "blackbox" but eventually I learned tons about it. I built a dashboard to track its Q-values, Loss gradient, Delusion index, portfolio, and Memory.

Mirroring the platform:

Because I had no money, and I was trying to create something from nothing, I put the bot in tough conditions.

I could only afford 0.01 SOL trades so thats what it traded with.

I backtested my strategies using a simulator I coded to replay a tokens life from start to finish.

(Screenshot above)

Building Richard:

Since the only thing I could fully understand well was video games, thats how I trained my model to trade for profit. I treated the money as points, and scaled everything including the simulated fees by levels.

Each level I required my model to get his portfolio up to the level number, scaling 0.25% each win (or down 0.25% if it lost 3 times in a row) and giving it dopamine based on wins and penalizing it based on losses.

I added League of Legends ranking system as huge dopamine boosts to get it to want to keep going.

Level 1-10 (Bronze)

Level 10-20 (Silver)

Level 20-25 (Gold)

Then plat, diamond, master, all the way up to Challenger.

Since bots for some reason love to learn with scales and gradients for some reason, I scaled the fees as well so it could learn it better. So from Level 1-4 there were no fees, then I subtlety added them on scaling with levels, after level 4 until it maxed out at 10

Level 1 through 4 = no fees

Level 4.25% = 42.5% of the total amount of fees

Level 6% = 60% of total fees

Level 10% and onwards = 100% of total fees

I even gave it a combo system for consistent wins rewarding it with more dopamine. Which it got very addicted to but ended up being the reason it profited so hard.

The bot at early levels is literally a baby at a computer.

You can see his brain in action from my dashboard screenshot!

The Psychology of Richard:

This is where it gets very interesting. The bot would act like a human trader sometimes. Even becoming a gambler at some point to cover its losses. It would hold winners for too long until they fell because it got addicted to the scaling dopamine from wins.

I had to force trade cooldowns on it to stop it from bleeding from fees and push its epsilon back up, which surprisingly actually helped it profit more sometimes. Cut its memory as the regime shift, and literally make a part of the script designed for THERAPY, as the bot would become tilted and stop trading.

Death and Rebirth:

At one point I kept changing the internal logic which caused Richard to become literally brain damaged. Its Q-values became unstable, loss skyrocketed into billions, and delusion only rose. It was genuinely a heartbreaking moment having to reset its brain. I had to take time away from the screen seeing this bot I got so attached to and spent hours every day on just vanish. I had to trick my own mind into believing that this was simply a prestige.

However, it turned out to be true. Each reset and iteration of Richard became 2x as better as the last, doubling the amount of profit with half of the time. He began climbing ranks at scary rates. Just like how a video game works. Probably the coolest thing ever.

Richard actually began finding tokens that eventually breached the 1 million market cap. He effectively became a logical trader instead of an emotional one.

TL;DR trained a model like a human using a gamified ranking system.

Feel free to ask any questions!