QuantDinger Jev Filter Backtest: 0 of 1,324

Does QuantDinger's Jev filter help? In my replay it never got the chance. With the platform's default settings, Jev's answers didn't clear the confidence bar once in 1,324 requests, so every trade went through exactly as if the filter were off. When I removed the thresholds and let Jev's own answers decide, it did no better than blocking trades at random.

Why I ran this

In part 4 I read QuantDinger's Jev gate and ended on a complaint: it only runs on live strategies, so nobody can backtest it. That wasn't quite true. The gate only needs a JSON state and six answers from Jev, and the Polymarket memory test showed Jev doesn't recall how past events turned out. So I rebuilt the state for every historical signal, asked Jev, and applied QuantDinger's own rule to the answers.

How I rebuilt the gate

I didn't retype anything I could import. From QuantDinger at commit 96cec2d:

Piece	Where it came from
The six questions and their choices	parsed straight out of ai_decision_filter.py
RSI, MACD, ATR, moving averages, support/resistance	QuantDinger's own technical_indicators.py, imported
Order budget	their order_budget.py, imported
The state sent to Jev	a line-by-line copy of ai_decision_context.py; the only change is that "now" is the signal's time, not today
Answer checks and the pass rule	a line-by-line copy of _evaluate_jev()

The request body is the same one the platform sends (model, state, questions) to the same endpoint, with jev-latest, which answered as jev-1.14.0.

The strategy is QuantDinger's official example, strategy_v2_dual_ema_long.py: on every bar close, if the 20-bar average is above the 50-bar average, hold 95% of equity; if not, hold nothing; 5% stop loss. While it's flat in an uptrend it asks to enter on every bar, so a rejected entry gets asked again an hour later. Exits never go through the gate, same as the platform.

	Data	Period	Trades without the gate	Return	Max drawdown
BTC 1h	Exness BTCUSD ticks, shown to Jev as BTC/USDT	30 Apr 2024 – 11 Oct 2026	246	−20.7%	−53.3%
Gold 1h	Exness XAUUSD ticks	17 Jun 2020 – 5 Oct 2026	416	+96.2%	−19.0%

Buys fill at the next bar's open plus the real spread, with 0.05% fees per side on BTC and 0.01% on gold. One losing strategy and one winning one, on purpose: a useful filter should cut the first's losses without wrecking the second.

Before running the gated version I wrote down what would count as "the filter helps": beat the unfiltered return and beat at least 95% of random gates that block the same share of requests.

What happens with QuantDinger's defaults?

Nothing. The filter never makes a decision.

QuantDinger only trusts Jev's answer when three of the six questions (entry_decision, risk_check, execution_quality) each come back with a confidence of 0.55 or more. Below that it treats Jev as failed and hands the decision to an ordinary LLM. With no LLM configured, the trade goes through.

For the 1,324 trades in the chart below (both markets, at two position sizes), all three cleared 0.55 zero times, and execution_quality never went above 0.49. Across every response I collected for this post, 41,693 of them, all three never cleared together once.

Part of this is how the API answers. Jev returns a probability for each choice and a separate confidence field, and they're not the same number. On one BTC entry it chose pass with probability 0.60 and confidence 0.20. QuantDinger reads confidence.

So in this replay the filter changed nothing: the same 246 BTC trades at −20.7%, the same 416 gold trades at +96.2%. If you've switched it on without an LLM, my numbers say it isn't filtering. If you have an LLM configured, that LLM is the one making the calls, not Jev. You pay one credit per check either way.

What does Jev actually think?

To see Jev's own opinion, I took the thresholds out and applied the rule to its chosen answers. That's not how QuantDinger behaves; it's the most generous reading of Jev.

It blocked almost everything: 99.85% of entry requests on BTC (10,396 asked) and 99.69% on gold (17,512 asked). The reason was nearly always risk_check = block, on 232 of 246 BTC entries. Jev objects to putting 95% of equity into one position, which is a fair point about QuantDinger's own example, but it's a point about sizing, not about the market.

Blocking that much changes the results, so the test is whether Jev picked which trades to keep better than chance. I ran 1,000 random gates that block the same share of requests:

	No filter	Jev decides	Random gates, median (5th–95th pct)	Random gates as good or better
BTC 1h	−20.7%, 246 trades	−9.1%, 16 trades, PF 0.55	−1.6% (−14.7% to +20.2%)	80%
Gold 1h	+96.2%, 416 trades	+4.3%, 55 trades, PF 1.17	+4.0% (−10.7% to +21.9%)	48%

On BTC, Jev lost less than the unfiltered strategy only because it was hardly ever in the market, and most coin flips did that better. On gold it threw away almost all of a +96% run and landed right on the random median. Neither result meets the bar I set beforehand.

With a smaller position, can it tell good trades from bad?

The sizing objection was drowning everything else out, so I cut the position to 10% of equity and asked Jev about every trade of the unfiltered strategy once. Now it blocks about one entry in five, mostly for signal_conflict, which is the market reading I actually wanted to test.

10% size	Trades passed	Trades blocked	Passed minus blocked, avg per trade (95% CI)	AUC of P(pass) vs winning trade
BTC 1h	197, avg −0.04%	49, avg −0.07%	+0.02 pts (−0.72 to +0.79)	0.50
Gold 1h	329, avg +0.20%	87, avg +0.11%	+0.09 pts (−0.25 to +0.41)	0.53

The trades it blocked weren't worse than the ones it let through. Every interval crosses zero, and an AUC of 0.50–0.53 is a coin flip. I'd set 0.55 in advance as the minimum to say Jev separates good entries from bad. It didn't get there, and its market answers (aligned, conflict, favorable, adverse) stayed between 0.47 and 0.55.

That matches part 5, where Jev read clean indicators accurately but couldn't say where Bitcoin was going next.

What would I do with this?

If you run QuantDinger: don't treat the Jev filter as a safety layer. With the defaults it's off in practice, and with the thresholds removed it's a coin flip that mostly says no. The parts of the platform I'd keep relying on are the plain-code ones from part 2: the order budget and the rule that exits never wait on an AI.

And for any AI gate, mine included: replay it before it touches money. This whole test cost about $5 in Jev credits.

What I didn't check

• The LLM fallback. With an LLM configured, every decision in this replay would have gone to it. I didn't test any LLM.
• One strategy. It's QuantDinger's own example, on two markets, 1h only. A strategy with smaller positions and different signals could get different answers.
• The 10% size as a live gate. I asked about each 10% trade once, but the bar-by-bar run at 10% stopped when my Jev credits ran out.
• Dates in the state. QuantDinger sends bar timestamps, so Jev could see the date. The Polymarket test found no memory of outcomes, but I didn't test memory of price paths.
• Data details. Volume is Exness tick count, not exchange volume. Only completed bars go into the state; the live platform may include the forming bar. No swap on gold.
• Versions. QuantDinger at commit 96cec2d, Jev 1.14.0 in early November 2026. The hosted version may be configured differently, and either side may change.

In this replay, QuantDinger's Jev filter either did nothing or did no better than chance.