Honest receipts · real Binance data · after fees

We built two trading bots to beat crypto.
They couldn't.

Two AI-assisted bots ran on real market data for months — one on Ethereum, one on Bitcoin — trying every popular way to outsmart the market. Here's exactly what happened, with nothing hidden.

Both bots are switched off. Everything below is a frozen snapshot of real backtests — nothing is trading live.
Meet the bots

Two coins, one honest verdict

Same engine, same AI, pointed at the two biggest coins. Click either to explore its results below.

Ξ
Aether
Ethereum · ETH
Bought every dip and sold every bounce on Ethereum — its settings tuned in real time by a reinforcement-learning optimiser and a Claude AI advisor.
1 / 4 market conditions where it lost to simply holding
See Aether's last run, history & settings →
₿
BitGain
Bitcoin · BTC
The identical strategy and AI pointed at Bitcoin instead. Different coin, same outcome — a clean test that the result wasn't just one unlucky market.
2 / 4 market conditions where it lost to simply holding
See BitGain's last run, history & settings →

Nothing here is hidden. The full source for both bots — the trading engine, every strategy, the reinforcement-learning optimiser, the Claude AI advisor and the backtests behind these verdicts — is open on GitHub: https://github.com/joubja/PaperTradingBot. Read it, run it, or check our numbers yourself.

The receipts

Aether on ETH — strategy vs. just holding

We didn't just test one idea. Pick any popular strategy and see how it did across real crashes, slow bear markets, flat chop and bull runs — every trade charged a realistic fee and slippage.

Classic TA (EMA / RSI / MACD)
Trend-following (ride trends, dodge crashes)
Buy-the-dip / sell-the-bounce cycling
Crash circuit-breaker (de-risk on drops)

Each card shows the money you'd actually have after the run. Beating buy-and-hold in a downturn just means losing less — not making money.

Bull market
+11.3% you'd have made
-1.5% vs holding — worse than holding
holding +12.8% · 41 trades
Flat / ranging
-7.0% you'd have lost
+0.2% vs holding — still a loss, just smaller than holding's
holding -7.2% · 4 trades
Crash — FTX 2022
-23.6% you'd have lost
0.0% vs holding — it never traded — identical to just holding
holding -23.6% · 0 trades
Slow bear — 2022
-37.4% you'd have lost
+0.1% vs holding — still a loss, just smaller than holding's
holding -37.5% · 6 trades
Bull market — $100 invested
after a realistic 0.030 % slippage + 0.1% fee per trade
The part nobody admits

"But it has AI" — why that didn't help

These weren't dumb bots. Each one carried a self-learning optimiser (a multi-armed bandit that rewarded settings that made money) and a Claude AI advisor reading the market. People assume that's the missing ingredient. It isn't — and here's the honest reason why.

1
You can't optimise an edge that was never there. The AI tuned how the bot bought dips — how deep, when to sell. But buying dips on spot has roughly zero expected profit before costs, and a negative one after fees and slippage. Optimisation just finds the settings that lost the least on past data. That's curve-fitting to history, not a prediction that holds up next month.
2
The bandit assumes a stable game. Markets aren't. A bandit only converges when each choice has a roughly fixed payoff. Here the best setting in a calm month is the worst one in a crash — and which regime comes next is exactly the thing nobody can predict. So it kept confidently chasing the last regime's noise into the next one.
3
An LLM can't see tomorrow's price. The Claude advisor reads news and indicators, but it has no privileged information about where ETH or BTC goes next — nobody does. In practice it added confident-sounding, costly noise, and sometimes overrode the optimiser for the worse.
4
We fixed the bugs. It didn't matter. We found and corrected real wiring faults in the reward loop. The ceiling didn't move: a perfectly-tuned optimiser on top of a no-edge strategy still nets to about break-even before costs — and just holding wins after them.
Before you @-reply us

Is it all trading bots?

No — and we won't pretend otherwise. What loses is the popular kind: bots that try to predict short-term price direction. On crypto, and by the same logic on forex and stocks, those lose to simply holding once you pay real fees — which is why the large majority of retail algo and day traders end up underwater.

The strategies that genuinely survive the data don't predict at all. They're market-neutral — long-short and pairs trades that harvest the price gap between related instruments, carry that collects a structural yield instead of betting on direction, and true arbitrage and market-making. Those edges are real. They're also small, fiercely competed by funds with scale and near-zero costs, and at retail fees usually get eaten before you see them. So: not "bots can't work" — rather, the bots being sold to you, the ones that promise to call the market, are the ones that don't.

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