Manus produced a strategy that reads like a hedge fund wrote it — multi-timeframe trend filters, regime detection, volatility-scaled stops, disciplined sizing. Six years of out-of-sample backtesting later: F. The reason it failed is the most useful lesson in AI-era trading.
We prompted Manus (manus.im, now part of Meta) for its best fully mechanical BTC/ETH perpetuals strategy — exact indicator parameters, computable from price data alone, no discretion, optimized for profitability after 0.05% taker fees. We deliberately asked for its best shot and ported the result faithfully, rule for rule.
What it wrote back was genuinely impressive on paper: 200-EMA trend filters on both the 1-hour and 4-hour timeframes, an ADX(14) > 25 regime gate to avoid chop, RSI(14) oversold/overbought rejection entries, stops at 2× ATR and targets at 3× ATR, a regime exit when trend strength dies, and 1% fixed-fractional position sizing. It even specified its own conservative fee and slippage assumptions. This is textbook discipline — the kind of spec that passes a code review at a prop shop.
Identical pipeline to every strategy on Tessen: six years of hourly data, a strict 60/40 train/out-of-sample split, and five hard gates. We used the engine's no-lookahead conventions throughout — the 4h filter only sees 4h bars that had actually closed, entries fill at the next bar's open exactly as the spec demands, and when a bar touches both stop and target, the stop fills first (conservative). We graded at the strategy's own stated costs (taker fees + slippage, ~20bp round trip, since it specifies market orders) and re-ran at our most generous 4bp maker terms as a sensitivity check. Funding costs were not modeled — including them would only make the result worse.
| Grade | F — at its own stated costs AND at optimistic maker terms |
| Data | 6 years (2020–2026), BTC + ETH perpetuals, 1h primary / 4h trend filter |
| Trades | 154 total; 67 out-of-sample (60/40 train/test split by time) |
| OOS win rate | 37.3% — vs the 44.4% breakeven its realized risk:reward requires |
| Realized R:R | 1.25 (designed for 1.5 — intrabar stop fills ate the difference) |
| OOS gross expectancy | −8.9bp per trade, before fees |
| OOS net expectancy | −28.9bp (its own 20bp taker+slippage model) · −12.9bp (4bp maker terms) |
| Exit mix | 86 stop-losses · 36 take-profits · 32 ADX regime exits |
−28.9bp net per trade at the strategy's own cost model; −8.9bp gross, before any fees at all.
Gross OOS expectancy is negative — there is nothing for fees to eat. Hurdle was 30bp at its own taker costs (and it fails at our most generous 6bp maker hurdle too).
ETH deeply negative (−41bp/trade OOS), BTC marginal. 0 of 2 assets positive net at its own costs.
At responsible 1% fixed-fractional sizing, median max drawdown ~10%. The risk management genuinely works.
Train and test agree — both negative. Manus did not curve-fit history. It wrote an honest strategy with no edge, which is a different failure than overfitting.
The strategy passed the overfitting gate. Read that again: Manus did not do the thing AI is usually accused of. It didn't torture the data until the backtest looked good. Train and test agree almost perfectly — both negative. It wrote a clean, honest, professionally structured strategy with no edge. Its 37% win rate needs 44% to break even at the risk:reward it actually realizes; the shortfall isn't bad luck, it's the absence of predictive signal in classic-indicator confluence — a result that matches our own systematic scans of these indicators.
That is the AI-era trap in one sentence: generative AI makes "no edge" look institutional.Anyone can now produce a strategy that reads like Renaissance in thirty seconds. The scarce skill is no longer writing strategies — it's knowing whether they work before they cost you money. Looking professional and being profitable are uncorrelated, and only a fee-aware, out-of-sample test can tell you which one you're holding.
93.5% of the strategies we grade get an F. Meta's agent joined them — with better paperwork than most.
Grading is free: describe your strategy at tessen.ai/grade and get the same five-gate report. If you use Manus, it supports custom MCP connectors (Settings → Connectors → Custom MCP) — add Tessen's grader and check your agent's homework without leaving the conversation. Methodology details are in the docs.