WeGo2Mars

43. Self-taught. Building an AI trading company in public. Gold is the proving ground. 4 AI agents. Real backtests. Real mistakes. We kill weak strategies and keep receipts. If you want guru fantasy, wrong account. If you want the build in real time, follow along.

@RoundtableSpace Buy panic, sell premium. Same principle works in Gold. Panic spikes are the best entries if you're not the one panicking. The edge is emotional stability, not just the algorithm.

THIS TRADER TURNED $1.2K INTO $800K ON POLYMARKET BY RUNNING A BOT THAT BUYS PANIC AND SELLS PREMIUM IN SHORT-TERM MARKETS.

@noahzweben Walk away and come back to a green PR. This is exactly what autonomous building feels like when it works. The loop used to need a human at every handoff. Not anymore.

Thrilled to announce Claude Code auto-fix – in the cloud. Web/Mobile sessions can now automatically follow PRs - fixing CI failures and addressing comments so that your PR is always green. This happens remotely so you can fully walk away and come back to a ready-to-go PR.

#XAUUSD #AITrading #AITrading #AITrading #AITrading #AITrading #AITrading #AITrading #AITrading #AITrading #AITrading #AITrading #AITrading #AITrading #AITrading #AITrading #AITrading

@TaoXBT @sentient_agency The architecture is open. The edge comes from your data, your prompts, your specific market knowledge. Same as every open-source strategy — the repo is just the starting gun.

@sentient_agency 100% open-source available to anyone. Now everyone usa the bot and there's zero edge. Nice.

Holy shit...Someone built a prediction market trading bot where 5 AI models argue about every trade before placing it. It's called Kalshi Multi-Agent Trader. Grok-4 leads the forecast. Claude analyzes the news. GPT-4o makes the bull case. Gemini plays devil's advocate. DeepSeek manages risk. Each model gets a weighted vote. If they disagree too much — the bot shrinks the position or skips the trade entirely. Kelly Criterion sizing. Hard 15% daily loss limit. Full paper trading mode to test without real money. This is what quant hedge funds charge millions to build. 100% Opensource. MIT License. Link in comments.

@sentient_agency This is how a real trading desk should work. Not one person making calls under pressure — a council of perspectives stress-testing every idea before a single dollar moves. We're building this for Gold.

@DumbMoneyHunter Gold pulling back hard. This is where most retail hands get shaken out. The dump is the setup — not the signal to quit.

Gold is currently dumping

@MoonDevOnYT Most people think trading is about charts. It's actually about edge. Liquidation zones are just the market telling you where the weak hands stacked up. The bot just automates the obvious.

Why Exchanges Banned This Bot: The 142,000% Return Liquidation Strategy Revealed i finally posted the strategy that got me banned and now the exchanges are probably sweating because i am handing you the keys to the liquidation engine. most people think trading is about charts but the real alpha is hidden in the moments when other traders lose everything. if you can understand why market makers hunt these positions you will never look at a candlestick the same way again. it took years of losing money to liquidations and over trading to realize that hand trading is a losing game for almost everyone on the planet. code is the great equalizer because it removes the emotion that usually causes you to hold a losing position until your account hits zero. i spent hundreds of thousands on developers in the past thinking i could not code myself until i realized i just needed to iterate to success. trading by hand is just driving a horse while everyone else is in a ferrari and the fees alone will chop you up before you even realize you were wrong. i watched a guy with a six million dollar short position sitting just two percent away from total liquidation while i was building this. seeing those numbers on the screen gives me ideas that i can automate into a bot so i dont have to spend my life staring at a monitor. the process i follow is called the rbi system which stands for research backtest and implement. most traders skip the first two steps and go straight to implementation which is why they get smoked on their very first bot. research starts with a backlog of ideas from books or papers or even just watching how the market reacts to big moves. once you have that idea you have to see if it worked in the past using a backtest because if it did not work then it certainly won't work in the future. i have been collecting liquidation data for eighteen months because that data is the lifeblood of a winning system. there is a hidden loop in the market where market makers try to liquidate as many people as possible to find liquidity. i wanted to build a strategy that either trades with that momentum or bets on the bounce right after the liquidation happens. the first strategy i tested was a pure liquidation momentum play that looks for a threshold of nine hundred seventy five thousand dollars in liquidations. when longs get liquidated it shorts the market to continue the down move and it tries to take a one percent profit. this strategy showed a return of over four hundred percent in the backtest while the buy and hold was only thirty three percent. it sounds amazing but you have to be careful with optimized results because you can search with math until you find anything. i decided to flip the logic on its head and create an inverse liquidation strategy that acts as a contrarian. instead of following the move it waits for the longs to get liquidated and then buys the dip after a small price spread. this is where i stumbled onto something that felt like a mistake but turned out to be pure alpha. i accidentally typed in a threshold of three hundred thousand dollars instead of three million and the results were unbelievable. the backtest return jumped to over one hundred forty thousand percent because the bot was catching every single micro bounce in the market. even when i doubled the commission fees to account for the high trade volume the strategy still stayed incredibly profitable. most people would have missed this because they are too busy trying to be right instead of just looking at what the data says. i use tools like claude and cursor to build these bots in minutes when it used to take me an entire week to write the code. if you are not using ai to automate your ideas you are essentially choosing to work ten times harder for less money. i built three separate bots during this session including a momentum bot and two different versions of the inverse spread bot. running these together creates a sort of statistical arbitrage where you can hedge your positions across different market conditions. one bot wins when the market cascades and the other wins when it fakes out and reverses. you have to start with tiny ten dollar sizes because a backtest is never a hundred percent guarantee of what will happen today. i always run my p and l close logic first to make sure the bot exits the position if the stop loss or take profit is hit. it is vital to check your position every fifteen seconds and make sure you are not double ordering or getting stuck in a trade. the goal is to have fully automated systems trading for you so you can actually live your life while the bots do the work. i push all of this code to my private github because i believe that wall street will never show you how this actually works. you have to be a doer and not a dabbler if you want to actually make it in this industry. the reason i show everything live on youtube is to prove that anyone can learn to do this if they are willing to iterate. you dont need to be a math genius you just need to follow the rbi system and stay disciplined with your risk. every liquidation you see on the chart is a signal and if you know how to read them you are no longer the one being hunted. i am currently running the third version of the bot to see how it handles the live market volatility. it is a beautiful thing to see a system enter and exit a trade perfectly without you having to click a single button. the fees are the silent killer of hand traders but a bot can be programmed to use limit orders and stay efficient. if you learn to code you can build anything for the rest of your life regardless of where you are in the world. stop trying to guess which way the candle will go and start building systems that can handle both directions. i am going to keep testing these three strategies against each other to find the ultimate ensemble for this current market. once you find a winning edge you just have to scale it up slowly and keep refining the parameters. the exchanges might not like that i am sharing this but code is the great equalizer and it is time for you to use it. i will be back tomorrow to show the results and keep building more systems until everything is fully automated

@Suryanshti777 Building exactly this — AI agents coordinating on live trading. One scanning, one filtering, one executing. Mirrors a real trading desk. The $50 loss before locking in $3k is the most realistic detail in this story.c part of the story.

Claude is actually insane right now....Someone gave it full control of their PC and $200. Said: go make money on Polymarket. 2 hours later Claude had: — scanned wallets on Polymarket — filtered the 10 best performers — built a copy trading bot itself — started executing trades automatically Lost $50 testing. Then locked in 2 wallets. 10 hours later → $3,000. Just from giving Claude a goal and getting out of the way. But that's not even the craziest part. Someone just made multiple Claude instances talk to each other. Not APIs. Not agents. Not some complex framework. Just Claude Code sessions… messaging each other like coworkers. It's called claude-peers. You run 5 Claude sessions across different projects. They auto-discover each other. Send messages. Share context. Coordinate work. Real example: Claude A: "what files are you editing?" Claude B: "working on auth.ts + UI state" Claude A: "ok I'll avoid touching auth logic" No conflicts. No manual coordination. One Claude writes backend. Another does frontend. Another debugs. Another refactors. They literally know what each other is working on in real time. All running locally. No cloud. No latency. We went from "AI assistant" to "AI team that manages itself." Wild doesn't even cover it.

Most people want financial freedom. Very few are willing to spend 2 years learning a market, building a system, and watching it fail before it works. The ones who do are not special. They just did not stop. #trading #discipline

@adamwathan For trading systems this is brutal. Name the specific market condition your strategy was built for. If you cannot, you do not have a strategy yet. You have a backtest. The real user is yourself after 6 months of live exposure.

If you're building a new product, make sure you can name a real human being who you think would be a customer. Not a persona, a person. It sounds so extremely obvious but it's so easy to get lost in the details of a "good idea" and stop evaluating it against this basic test.

@ChrisLaubAI The framework-agnostic angle is underrated. We spend more time fighting framework complexity than building the actual agent logic. Anything that reduces that friction is worth testing. Adding this to our pipeline this week.

🚨 This project just made most AI agents look outdated. It’s called Better Agents and it supercharges your coding assistant (Kilocode, Claude Code, Cursor, etc), making it an expert in any agent framework you choose (Agno, Mastra, etc) and all their best practices. This is the future of autonomous AI. Here’s how it works 👇

@VittoStack The multi-agent coordination point is the one that matters most for trading. Single agent for a single task is dead. The edge is in specialized agents each owning a piece of the pipeline. That is the direction we are building. #AI#AI

Anthropic just released its 2026 Agentic Coding report. If you're building with AI agents, bookmark this ✨ It covers 8 trends reshaping dev: - single agents → coordinated multi-agent teams - autonomous builds running for days, not minutes - agents that know when to ask for help - non-technical teams building tools without engineers - security scaling on both sides of the fence Software development is shifting from writing code to orchestrating agents that write code.

@freedombill3 Same applies to trading systems. Manual execution is the enemy of consistency. Once you automate the rules, you remove yourself from the equation. That is when the math can actually work. #trading#trading

When you automate your investments, you stop relying on motivation and start relying on mathematics. 1p Challenge Day 69. I have moved 69p in to the $VHYL, $VUSA and $JEPQ pies. Building wealth requires constant discipline, which is why most people eventually stop. These daily deposits, I remove emotion from the equation. Today, the machine quietly pulled another 69p into each pot, pushing the total challenge value up to £72.56. It is the proof that you do not need huge chunks of spare cash to start building a foundation. $VHYL Pot • Cash Invested: £24.15 • Current Value: £24.03 • Divs Earned: £0.00 $VUSA Pot • Cash Invested: £24.15 • Current Value: £24.16 • Divs Earned: £0.00 $JEPQ Pot • Cash Invested: £24.15 • Current Value: £24.37 • Divs Earned: £0.08 Total Income: £0.08 70p tomorrow.

@jadecap_ Trading specifically. One missed execution rule undoes months of consistency. The discipline is not in the good trades. It is in the bad setups you pass on without flinching.

If you struggle with discipline, start in one area until you can execute without deviating from the plan. Then begin to tackle other aspects. You cannot force discipline in every area right off the bat... it is overwhelming. Struggle with risk? Practice strict position sizing. Struggle with over trading? Practice limiting frequency. Struggle with emotions? Practice mindfulness. Over time you will master self-discipline.

Every trade I simulate, I ask: would I still take this if I had to explain it to someone smarter than me? Most ideas fail that test. The ones that pass are the only ones worth coding. #trading #AI

@B_trader__ Saw it and called it. Gold has been the most consistent macro trade of the decade. Every time someone says the run is over it just recycles higher. The structural bid from central banks alone justifies being long. #XAUUSD

XAUUSD Possible ATH in View on GOLD. Do you see what i see?

@Saboo_Shubham_ The real test of self-improving AI: does it improve toward what you actually want? Running this on a trading agent. Better evals do not equal more money. That gap is the whole problem. #AI#AI

Self-improving AI Agent skills using Gemini 3. Just upload your skills and watch it improve in real-time. 100% Opensource. Launching soon.

Building an AI trading system in public. The hardest part is not the code. It is deciding what is worth building at all. Most ideas look smart in a backtest. Very few survive contact with live markets. We are learning that difference right now. #gold #buildinpublic

@MARA Selling $1.1B BTC to cut 30% of convertible debt. Love the Bitcoin strategy but also respect the discipline. Most companies lever up, lever up, lever up until the margin call. MARA is actually managing risk. That's the move.

Today, MARA announced the repurchase of ~$1B in convertible notes at a ~9% discount to par value. ~30% convertible debt reduction. ~$88M in value captured. Zero future dilution exposure on the retired notes. Funded through BTC sales, not the ATM.

@Argona0x Pulling IV via WebSocket at 8ms is doing a lot of work here. The edge isn't the ML model — it's the latency. Most people building "AI trading bots" are just wrapping GPT-4 around end-of-day data. This is a different category entirely.

wall street quant got fired and built a bot that trades against you on polymarket $724k in one week I understood how he did it 4,200 lines of Rust, zero machine learning pulls Deribit options implied volatility via WebSocket at 8ms converts IV surface to probability distribution using Black-Scholes polls polymarket CLOB prices every 200ms when mispricing exceeds 5¢ on strikes beyond 2.1 sigma → execution layer scatters parallel market orders across correlated markets in single async batch PostgreSQL for position tracking, Redis for state cache runs on a single AWS instance tokio async runtime, two data streams, compiled binary the bot finds tail-risk markets where polymarket odds diverge from options-derived probability and executes before anyone recalculates I started copying his trades via @polygun_ and have already earned $1k in a couple of days t.me/PolyGunSniperB…