My AI strategy gave back half its gains in a chop. So I rewrote its rules — in plain English.
Third in a series on building honest crypto quant. Post 1: what didn't work. · Post 2: up 15.7%, and why I didn't trust it.
What happened: a good week, then two weeks of getting nibbled to death
One of my arenas — a committee of LLM agents paper-trading BTC perpetuals on 30-minute bars — had a great run and hit +17%. Then Bitcoin stopped trending and started chopping: two weeks of range-bound, back-and-forth price. The equity slid from that peak down to +8%.
This is the classic failure mode of an active strategy in a sideways market: death by a thousand cuts. A committee that looks for a setup every bar keeps opening positions that go a little in its favor, reverse, and stop out. No single loss is big. But with a 47% win rate and no trend to let the winners run, the small losses and costs quietly give back the gains you made when the market was actually moving.
The diagnosis: the problem was when it traded, not how
The committee's judgment wasn't broken. The problem was that it traded at all during the chop. What it needed wasn't smarter agents — it was trade selection: a filter that says "sit on your hands unless this is a high-quality, trend-aligned setup."
On most platforms, that means editing code and redeploying. On this one, it's a sentence.
The fix: I rewrote the entry rule in plain English
Every arena has a mechanical-rule box. You type a rule in natural language; it's parsed into deterministic indicators and evaluated with no lookahead. I replaced the free-running committee entry with this:
In an uptrend (price above the 200-EMA) with ADX above 20, buy a pullback within 2% of the recent support; in a downtrend, short a bounce within 2% of the recent resistance.
Three things happen automatically from that one sentence:
- It becomes a two-layer pyramid. The rule opens the first tranche at support; a scale-in layer adds up to two more tranches if price drops further into the level, averaging the entry down. To give the ladder room to fill, the tight stop is swapped for a wide disaster stop — so per-trade drawdowns are larger by design, the trade-off every pyramid strategy makes.
- The exit style is auto-matched. An exit agent reads the rule, recognizes it as trend-following, and configures a trailing stop that lets winners run — instead of guessing a mean-reversion exit that would cut the trend short.
- It trades far less. Requiring trend + structure + proximity means it stays flat through the chop and only fires on the setups it's designed for. Fewer trades, less whipsaw grinding.
The bet is simple: the trades it now skips are the ones that were losing money in the range. That's a hypothesis, not a fact — which is why I'm testing it in the open rather than declaring victory.
The control experiment: 12 arenas, one rule each
To find out whether any particular entry rule actually carries an edge — and to stop myself from cherry-picking one lucky strategy — I spun up twelve arenas on the same market (BTC, 30-minute), same capital, same committee. The only thing that differs is the injected rule. Ten are distinct styles, one is a fast-timeframe scalper, and one is a control with no rule at all (pure committee) so I can see whether any rule even beats the free-running baseline.
| Arena | Entry rule (plain English) | Hypothesis / support |
|---|---|---|
| Trend breakout | Uptrend + strong ADX + volume breakout of the recent high (mirror for shorts) | Momentum continuation |
| Trend pullback | Uptrend + ADX + buy a pullback to support (the pyramid above) | Buy dips in trends |
| Bollinger reversion | Buy the lower band with RSI oversold; short the upper band overbought | Mean reversion |
| Crowding reversal ★ | Short when the retail long/short ratio is extreme long; buy when extreme short | The one edge that survived a strict holdout in my earlier research |
| Smart-money follow | Follow the top-trader positioning ratio, trend-aligned | Institutions lead |
| Order-flow confirm | Aggressive-buy dominance + rising open interest (mirror for shorts) | Flow + OI confirmation |
| Volatility squeeze | Low ATR compression, then breakout of the recent extreme | Low vol precedes expansion |
| Moving-average cross | EMA20 crosses EMA60 (golden/death cross) | Classic trend baseline |
| RSI extreme | Buy deep oversold, short deep overbought | Pure contrarian |
| Multi-factor ★ | Trend + not-crowded + structural entry, all at once | Confluence of the best signals |
| Scalper (5m) | Bollinger + RSI mean-reversion on 5-minute bars | Fast in/out |
| Control | No rule — the free-running committee | The baseline every rule must beat |
Each arena's exit style is auto-matched to its entry style: trend rules get trailing exits, mean-reversion and scalper rules get quick "revert to the mean" exits, and the crowding rules exit when the crowd unwinds. Entry style and exit style are finally consistent — no more entering mechanically and exiting on a guess.
What I actually expect (the honest part)
If you've read the first post, you know the prior: most price-only rules do not beat buy-and-hold, and few carry a stable edge. I expect the majority of these twelve to be noise. The crowding-reversal arenas (marked ★) have the most support because crowding reversal is the single cross-sectional edge that survived a strict coin-and-time holdout in my research; the rest are hypotheses I'm testing precisely because I don't know.
The point of running them isn't to find a money printer. It's to answer a clean question — does trade selection via a mechanical rule beat a free-running committee, and which rule, if any — with a real, forward, no-lookahead experiment where you can watch every entry, every exit, and every honest scoreboard update. Some of these will be flat or down. Telling you that is the product.
You can watch all of it
Every arena above is live and public: the committee's reasoning, each trade, the auto-selected exit, and the scoreboard against buy-and-hold. If the rewritten pyramid strategy keeps giving back gains, you'll see that too. And if you want to test your own idea, the mechanical-rule box takes a sentence — bring an entry rule and watch it get stress-tested the same honest way.
FAQ
Why did the AI strategy give back its gains?
It peaked at +17% during a trending move, then gave back to +8% over two weeks of range-bound Bitcoin. A frequently-trading committee gets ground down by whipsaws in a sideways market — many small stop-outs, no trend to ride. With a 47% win rate and no trend to carry the winners, costs and reversals ate the edge.
What is a "mechanical rule" here?
A plain-English entry condition you inject into an arena. It's parsed into deterministic indicators — moving averages, RSI, support/resistance distance, ATR, Bollinger bands, volume, crowding ratios — and evaluated with no lookahead, so entries follow a repeatable rule rather than the committee's mood alone.
What is a two-layer pyramid (scale-in)?
It enters in tranches at progressively better prices: it opens on a trend-aligned pullback to support, then adds up to two more tranches if price moves further into the level, averaging the entry. To let the ladder fill, the tight stop is replaced by a wide disaster stop — so per-trade drawdowns are larger by design.
Does trading less actually improve results?
Only if the trades you skip were the low-quality ones. The bet is that a trend filter plus a structural entry removes the whipsaw trades a frequent strategy takes in a sideways market. Whether that improves risk-adjusted returns is exactly what the live experiment is meant to answer — it's a hypothesis, not a proven result.
Is this profitable, or should I copy it?
This is paper trading and research, not investment advice. The sample is tiny and the window is short. Past performance — especially a couple of weeks of it — is not indicative of anything. The platform's value is honest evaluation, not a guarantee of profit.
Research and paper-trading. Not investment advice. Past performance is not indicative of future results.