Journals record your trades. NoxarQuant checks what the record can and can't support.
Most trading tools are recorders. They log what happened and score how you've done. That is useful. It is a different job from checking which of those results had enough trades behind them to mean anything.
NoxarQuant enriches every trade with the market condition it was taken in, clusters by condition rather than outcome, scores each cluster with a sample penalty, and runs bootstrap Monte Carlo on the chronological out-of-sample segment. A journal tells you your win rate is 43%. It doesn't tell you how many trades are behind that number, or how far it moves when the same trades come in a different order. NoxarQuant does.
Keep your own tags. NoxarQuant adds the ones you can't see by eye: the session, the volatility, the RSI band and the trend at the minute you traded, read off price data and attached for you. That works from the first trade, before there are enough trades for any verdict.
And it works on your real fills, the trades you actually took, with real slippage and the decisions you actually made, not a hypothetical backtest of a strategy that never met a live order book. It reads crypto price action natively, so you don't need to code a strategy or wire up a backtester. Upload what you did and see which setups made money, which lost it, and which don't have enough trades yet to say.
Built for systematic and data-driven traders. Open to everyone else.
NoxarQuant is strongest for systematic, algorithmic, and data-driven discretionary traders. People auditing whether a setup carries statistical edge. Researchers exporting clean cluster data into Python or TradingView. Traders with a routine they want checked against their own numbers.
Discretionary traders fit too. The journal layer auto-enriches every trade with the market conditions it was taken in, and the groups that lost money show up from the start, however you trade. Where NoxarQuant is most differentiated is on the statistical-audit side. If tick-by-tick replay or daily emotional notes are your priorities, treat NoxarQuant as a layer alongside your existing journal rather than instead of it.
A different premise, not a longer feature list.
We respect these tools. Here's the one structural difference.
Each of these tools has genuine strengths we won't pretend away. The difference is not polish or feature count. It's the question the maths is built to answer.
A genuinely slick product with a strong community. The Zella Score blends win rate and expectancy into one clean number that summarises how a strategy has performed.
NoxarQuant asks a narrower question: how much of that result could have come from the order the trades fell in. It labels your groups on earlier trades, resamples the later ones it did not see, and shows the spread of results rather than one score.
A respected, analytically deep tool. Its Edge Finder lets you slice your history by tag, time, and symbol to look for what is working.
NoxarQuant groups trades by the market conditions at the minute they were taken and penalises small samples, so a slice that looks good by chance is marked as too thin to judge rather than treated as an edge.
Comprehensive coverage: 600+ statistics across instruments. If you want every computable number, it delivers breadth.
NoxarQuant deliberately does fewer things: tag each trade with its market conditions, score each group with a sample penalty, and test by resampling. Fewer numbers, each one doing a job.
A clean product whose Cypher AI surfaces patterns in your tagged trades, for example how you perform at different times of day.
That works on how you trade. NoxarQuant looks at the setup itself: whether its results held up on trades it had not seen, and whether there were enough of them to say.
1,000 simulations. Not a prediction.
Chronological 70/30 split. Bootstrap resampling with replacement on the out-of-sample segment. The output is a distribution, not a point estimate. On Pro and above, condition the run on a single market regime, resample only your NY AM trades, or only London, to see how your past trades in that session behaved, not just the average across every session.
The bootstrap simulation runs on the out-of-sample segment. Switch to the Out-of-Sample tab to see the distribution.
Markets have moods, and NY AM trades nothing like Asia. Most “this strategy is profitable” claims are averages across regimes you will never see again. Re-run the bootstrap on one session only and the same edge can land at a +$312 median in NY AM and a −$48 median in Asia: same trader, same setup, opposite verdicts.
SIMULATED RESULTS · PAST PERFORMANCE IS NOT INDICATIVE OF FUTURE RESULTS · NOT FINANCIAL ADVICE
Based on real NoxarQuant enriched trades.
For informational purposes only. Not financial advice. All analysis is derived from your own historical data.
HYPOTHETICAL PERFORMANCE RESULTS HAVE INHERENT LIMITATIONS. PAST PERFORMANCE IS NOT INDICATIVE OF FUTURE RESULTS. NO REPRESENTATION IS MADE THAT ANY ACCOUNT WILL ACHIEVE SIMILAR RESULTS.
Trading with an AI on crypto? Plug in our MCP.
Connect NoxarQuant to your assistant once and it can import a strategy's trades, see which edges survive out-of-sample, stress-test them, and write a plan back, without you copy-pasting anything. It becomes the verification layer your bot runs through.
On Pro and Elite (the free trial included), Settings → Account → Connect an assistant gives you a token. Paste it into your MCP client, Claude or Cursor or anything else that speaks the protocol. It can read your analysis, import trades and save a plan, and nothing else: it cannot place a trade or reach an exchange.
Prefer to wire it yourself? The same endpoints are a raw REST API on Pro and Elite, with the same token. The tools, endpoints, limits and errors are in the docs.
Example session. You asked: verify my BTC breakout strategy on noxarquant. It replied: imported 412 trades → 9 condition clusters. 3 hold edge out-of-sample · 2 are draining capital · 4 need more sample. bootstrapped the survivors on the held-out 30%: median +$8.40 per trade. saved a do · caution · review plan back to your account. You asked: what would a $50k challenge cost me to pass?. It replied: 1,000 attempts through a 6% static floor at $165 a go. 12.4% passed · 74.9% blown · the drawdown floor ended 58.2% of them. expected fees to pass $1,155, though half get there inside 5 attempts. You asked: where did last quarter actually go?. It replied: Asia gave back $18,300 across 3,031 trades. NY AM is the worst per trade at −$9, over 1,424 trades. London is the only session above water per trade.
Most journals record execution. NoxarQuant adds a layer that checks the record: sample-penalised scoring, an earlier and later split of your own trades, and resampling. That combination in one journal is what we haven't found elsewhere at a retail price.
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For informational purposes only. Not financial advice. All analysis is derived from your own historical trade data. Brand names are the property of their respective owners and are referenced for factual comparison only.