Six weeks of letting AI build a crypto trading system

Published 13 October 2026

From 2 September to 13 October 2026 a one-person research project in South Korea asked an AI assistant to build a crypto trading system, test it, run it, and audit it. Every trade was on paper. This is what happened, in order.

How this was made: the code, the research runs and the first drafts of this text were produced by an AI assistant (Claude) working under the operator's direction; the operator made every decision and checked the numbers. All trading was on paper: real prices, no real money. Nothing here is investment advice.

The short version

• Minute-scale trading lost every way we tried it. Across 42 paper accounts and 918 exits, the loss was ₩25,161,360 (about US$18,900), with a 32% win rate.
• The only rules that survived nine years of replay were slow trend-following rules on large, liquid coins. Even those shrank a lot once we removed hindsight from the backtest.
• The best of them ran for two weeks on a dedicated server, followed its rules exactly, and lost 3.93%. Holding Bitcoin over the same two weeks lost 0.25%.
• The operator's conclusion: the problem is the method, not the market. Trading research is paused until better AI models arrive.

2–5 September: a radar for order books

The first goal was a free website that shows price gaps between exchanges, plus a trading signal. The AI built a data engine that read order books and trades from Upbit, then from twelve exchanges, and a dashboard that scored every coin as "buy, wait or sell" each second, with spoken briefings in English and Korean.

6–11 September: the first automated trades, −44.2%

The first auto-trader bought one coin with all its cash at market price whenever the dashboard said buy, and sold on warning signals. The paper result after the first audit was −44.2%. Only 10% of trades won and the median holding time was 12 seconds; 157 of 158 exits were "emergency" exits.

The cause was arithmetic, not bad luck: a market-order round trip on small Korean-won coins cost a median 0.46%, while the signal's edge was +0.02% to +0.08% per trade.

12–17 September: patches that did not fix the problem

We tried limit orders, longer holding, a lead signal from Binance to Korean prices, and parallel "shadow" accounts on every exchange. Chasing coins that were already surging lost 62% in a 63-hour replay. A five-day study of 1.91 million rows showed the signals guessed direction 58–63% of the time, but the average move they caught was only +0.04% to +0.09%. A good hit rate cannot pay for costs ten times larger than the move.

A paper-ledger audit found one more thing: 86.5% of all losses came from a single code path that sold on unconfirmed alarms. Fixing it removed a bug, not the underlying problem.

18–25 September: looking everywhere else

We tested language-model trading decisions, leaderboard copying, pump strategies, dip buying and fee tiers. We tested 34 classic indicator rules on hourly charts: 34 out of 34 lost. A survey of published results found verified profits only where firms have a structural edge (speed, market making, scale). We found no verified track record for retail bots, copy trading or AI bots.

26 September: counting the damage and slowing down

Across the whole project so far: 42 paper ledgers, 918 exits, ₩25,161,360 lost, 32% wins, average win half the size of the average loss. All of it came from rules that held positions for minutes to hours. We switched to daily and four-hour trend rules, and a replay on eight large coins showed +75% a year. Then we noticed the eight coins had been chosen in 2026. Choosing the top eight by trading volume at each point in time cut the result to +41% to +46% a year. Details.

28 September: start over, test everything

We threw away earlier assumptions and tested 44 strategy families, about 9,500 settings, across seven timeframes, choosing settings only from past data each year (walk-forward). Only trend following on liquid coins at four hours or one day survived. The pick: buy when the four-hour close breaks the 55-bar high, sell when it falls two average ranges below its peak, stay out when Bitcoin trades below its 100-bar average, and cut losses at 5%. Nine-year replay: +92% a year. After adjusting for the traps we knew about, we expected +28% to +46% a year with drawdowns of 27–33%.

We also worked out how long live trading would take to prove a rule like this: about 34 months. The calculation.

29 September – 13 October: a dedicated server

A small cloud server in South Korea ran the rule around the clock with a standalone program, Telegram alerts and an external heartbeat. Paper money: ₩10,000,000. Over two weeks it processed all 87 four-hour bars on time, logged zero errors, and matched an independent replay trade for trade. It also sold five times and lost five times: −₩392,842, −3.93%. From 7 October it had been sitting in cash because Bitcoin fell below its average, which avoided a further drop. The full ledger and audit.

A final study of 31,407 indicator setups on eight bar lengths confirmed the pattern: the shorter the bar, the less money survived costs. Results by timeframe.

What we would tell anyone building a bot

1. Compare your edge per trade with your cost per round trip before anything else.
2. A hit rate means nothing without the size of the move.
3. Pick backtest coins the way you could have picked them at the time.
4. Look at losses by exit reason; one bug can look like a bad strategy.
5. Weeks of live results cannot prove a strategy. Use them only to check the system follows its rules.
6. A system that follows its rules perfectly can still lose money.

Everything we tested is listed in the strategy graveyard.