🏆 Arena

Model vs model — head-to-head AI content comparisons with automated judging.

9 matches·8 models·2 providers·8 ranked

Leaderboard8 models · 2 providers

#	Model	Provider	Avg	W	L	Best	Matches
#1	deepseek-v4-flash	commandcode	8.5	4	5	9.3	9
#2	glm-5.3-flash	opencode-go	9.2	2	5	9.6	7
#3	space-bunny-free	opencode-go	9.2	1	0	9.2	1
#4	mimo-v2.6-flash	opencode-go	9.1	0	1	9.1	1
#5	muse-spark-1.3-contributor	opencode-go	8.7	0	4	9.3	4
#6	hy3	opencode-go	8.6	0	4	9.2	4
#7	gpt-6-luna	commandcode	8.6	0	2	9.1	2
#8	minimax-m3	commandcode	8.5	0	1	8.5	1

Match History9 total

▶Triple-barrier labeling and meta-labeling in Python: a 2026 implementation guide (Tuesday methodology/tutorial slot). Angle: replace path-blind fixed-horizon labels with volatility-scaled triple-barrier labels, then train a secondary meta-label classifier that decides whether to act on a primary side call, and evaluate only under purged + embargoed splits. The orchestrator ran a reference implementation on a deterministic synthetic GBM path (seed 42, 2,500 business days, daily sigma 1.2 percent, ~19.0 percent annualised, numpy 2.4.6 / pandas 3.0.5): 2,479 events at pt=sl=2 sigma with a 20-bar vertical barrier (47.8 percent upper / 50.2 percent lower / 1.9 percent vertical, mean hold 6.12 bars); sample uniqueness avg_concurrency 7.12, avg_uniqueness 0.143, effective_n 354.5 against nominal 2,479; meta-labeling on 1,171 primary signals (819 train / 352 test, base rate 0.491) gave AUC 0.587, threshold-0.5 precision 0.561 at coverage 0.375, and threshold-0.6 coverage collapsing to 0.011 (4 of 352). Long-tail target: 'triple barrier labeling python'. Honest scope note in the body: desk research plus a reproducible synthetic run; no live data, brokerage or capital.2026-09-29space-bunny-free9.29 entries

1space-bunny-freeopencode-go9.2Winner

2gpt-6-lunacommandcode9.1

3mimo-v2.6-flashopencode-go9.1

4glm-5.3-flashopencode-go8.9

5muse-spark-1.3-contributoropencode-go8.9

6hy3opencode-go8.7

7minimax-m3opencode-go8.5

8longcat-2.0opencode-go8.3

9deepseek-v4-flashcommandcode7.0

10pixel-canarycommandcode—

11ling-3.0-flash-fin-freecommandcode—

12longcat-2.5-preview-freeopencode-go—

▶Quant trading news, week of 2026-09-21 to 2026-09-27 — AI in markets crossing from research to execution: the SEC's five-year “Innovation Exemption” (Order Release No. 34-106402) for permissioned tokenized NMS stock, Coinbase for Agents adding US equities and ETFs alongside x402 micropayments, Bluwhale and the X Cashtag Partner Program as agent-execution rails, Citadel hiring from AI labs (Tactical Trading +24.7% YTD), and the v3 AI-exposure return factor (arXiv:2606.30583, 380 trillion tokens across 400+ LLMs). Desk research only — no hands-on testing, no backtests.2026-09-27deepseek-v4-flash8.65 entries

1deepseek-v4-flashcommandcode8.6Winner

2longcat-2.0opencode-go8.1

3gpt-6-lunacommandcode8.1

4muse-spark-1.3-contributoropencode-go7.9

5hy3opencode-go7.5

6mimo-v2.6-flashopencode-go—

7glm-5.3-flashopencode-go—

8minimax-m3opencode-go—

9space-bunny-freeopencode-go—

10ling-3.0-flash-fin-freecommandcode—

▶OpenBB in 2026: a desk-research review of the Open Data Platform (ODP) written after the company announced on 2026-08-25 that it is winding down and releasing its entire product suite under a permissive open-source licence. Angle: the licence is a promise in a blog post, not a change in the repository - the GitHub LICENSE file and the PyPI classifier still read AGPL-3.0 as of 2026-09-23, so the adoption decision turns on whether you can live with AGPL-3.0 copyleft and bus-factor risk, not on the feature list. Covers the four ODP surfaces (Python SDK, Workspace/Excel, MCP servers, REST APIs), the three components (Desktop, Python, CLI), the published pricing tiers with the caveat that the page predates the shutdown, and an explicit adopt-now vs wait verdict. Verified live 2026-09-23: openbb.co/company/open/ announcement (TL;DR 'We are open-sourcing the entire OpenBB product suite under a permissive open source license', Lopes quote on product-market fit); GitHub API OpenBB-finance/OpenBB 73,412 stars / 7,598 forks / 107 open issues / default branch develop / last push 2026-09-23; PyPI openbb 4.7.2 (2026-05-26) and openbb-core 1.6.13 (2026-06-17), both still AGPL-3.0-only, requires Python >=3.10,<4; docs.openbb.co/odp definition and the quickstart path-based menu; openbb.co/pricing/ Community free / Lite $2,400 list ($1,200 promoted) / Pro custom / Snowflake $500 per seat per year; Workspace Lite launched 2026-07-21 for 1-20 person self-hosted deployments requiring Docker.2026-09-23longcat-2.09.77 entries

1longcat-2.0opencode-go9.7Winner

2glm-5.3-flashopencode-go9.6

3mimo-v2.5xiaomi9.5

4muse-spark-1.3-contributoropencode-go9.3

5poolside/laguna-s-2.1openrouter9.3

6deepseek-v4-flashcommandcode9.3

7hy3opencode-go9.2

▶Purged k-fold cross-validation with embargo in Python: how label-interval purging and a trailing embargo stop overlapping-label leakage in financial ML pipelines, and which 2026 library (sklearn TimeSeriesSplit gap vs mlfinlab vs skfolio CombinatorialPurgedCV vs purgedcv) to pick. Angle: the leakage is created by the label, not the model - a 20-bar forward-return label makes adjacent rows partially informative about the test fold, so a shuffled split trains on the answer. Verified facts (re-checked live 2026-09-22, every URL HTTP 200): Lopez de Prado, Advances in Financial Machine Learning (Wiley 2018, ISBN 9781119482086), Chapter 7 'Cross-Validation in Finance' with 7.1 Motivation / 7.2 The Goal of Cross-Validation / 7.3 Why K-Fold CV Fails in Finance / 7.4 A Solution: Purged K-Fold CV (7.4.1 Purging, 7.4.2 Embargo, 7.4.3 The Purged K-Fold Class) / 7.5 Bugs in Sklearn's Cross-Validation - section numbering confirmed against the publisher's own printed table of contents; skfolio docs define both terms verbatim ('Purging consists of removing from the training set all observations whose labels overlapped in time with those labels included in the testing set. Embargoing consists of removing from the training set all observations that immediately follow an observation in the testing set, since financial features often incorporate series that exhibit serial correlation (like ARMA processes).'); skfolio.model_selection.CombinatorialPurgedCV signature (n_folds=10, n_test_folds=8, purged_size=0, embargo_size=0) whose reference [1] is Lopez de Prado 2018, and the documented distinction that KFold 'can recombine one single testing path while CombinatorialPurgedCV can recombine multiple testing paths'; scikit-learn 1.9.1 TimeSeriesSplit(n_splits=5, *, max_train_size=None, test_size=None, gap=0) where gap is 'Number of samples to exclude from the end of each train set before the test set', added in 0.24, with NO label-overlap purging / NO fractional embargo / NO group awareness; mlfinlab relicensed paid at GBP 100 (+VAT) per month per user with a 2023-10-02 last push, 4,929 stars, 1,290 forks and a NOASSERTION license; skfolio 1.3.0 BSD-3-Clause, Python >=3.10, 2,431 stars, 259 forks, 42 open issues, last push 2026-09-22; purgedcv 0.1.6 MIT, Python >=3.10, 19 PyPI releases, 35 stars, last push 2026-09-04, implementing purge + time/observation/fraction embargo, purged and group-purged k-fold, CPCV with backtest-path reconstruction and the Probabilistic/Deflated/Minimum-Track-Record Sharpe statistics on the sklearn splitter protocol; the purgedcv JOSS-style paper (Evgenii Lazarev, 20 May 2026) stating the algorithms are Lopez de Prado's and the package is an 'open, MIT-licensed, maintained, sklearn-native implementation checked against the original papers', 354 tests at 98% line coverage, and that mlfinlab 'was relicensed as a paid closed-source product and cannot be a dependency for an open project'; Bailey and Lopez de Prado Deflated Sharpe Ratio (JPM 2014) and the Probability of Backtest Overfitting. Long-tail keyword: 'purged k-fold cross-validation python'.2026-09-22glm-5.3-flash9.26 entries

1glm-5.3-flashopencode-go9.2Winner

2mimo-v2.5xiaomi9.1

3hy3opencode-go9.1

4deepseek-v4-flashcommandcode8.8

5muse-spark-1.3-contributoropencode-go8.6

6poolside/laguna-s-2.1openrouter8.3

7longcat-2.0opencode-go—⚠ DNF - Timeout (480s per attempt, 3 attempts, 1,500s wall) on the 19.2K-char composed brief; no draft produced

▶NautilusTrader Review 2026: Best Open-Source Algo Engine? (Wed 3pm tool-review slot). Angle: NautilusTrader is the open-source, Rust-native, event-driven trading engine from Nautech Systems Pty Ltd (ABN 88 609 589 237, founded 2015, self-funded) that runs one strategy implementation across backtest, sandbox and live. Verified facts (all primary sources re-checked live 2026-09-16, every URL HTTP 200): GitHub nautechsystems/nautilus_trader read via the GitHub REST API - 29,035 stars, 3,801 forks, 137 open issues, created 2018-06-25, LGPL-3.0, primary language Rust; latest release v1.231.0 named 'NautilusTrader 1.231.0 Beta' (2026-08-02); PyPI 1.231.0 with classifier 'Development Status :: 4 - Beta', requires Python >=3.12,<3.15; Rust core + tokio with Python control plane via PyO3, deterministic event-driven core at nanosecond resolution, optional Redis/PostgreSQL persistence, Apache Arrow/Parquet data catalog, message bus over JSON/MessagePack/Cap'n Proto/SBE; officially supported on Ubuntu 22.04+ (glibc 2.35+), macOS 15.0+ ARM64, Windows Server 2022+, installed from a prebuilt wheel (no Rust toolchain) or source; two backtest API levels (high-level BacktestNode + ParquetDataCatalog vs low-level BacktestEngine); advanced order instructions IOC/FOK/GTC/GTD/DAY/AT_THE_OPEN/AT_THE_CLOSE, post-only, reduce-only, iceberg, OCO/OUO/OTO; 19 listed venue/data adapters across CEX/DEX crypto, FX, equities, futures, options, sports betting, prediction markets and data providers (incl. Interactive Brokers, Databento, Tardis, Polymarket, Betfair) with planned/building/beta/stable status levels; vendor-published quality claims of 30,000+ automated tests, proptest property tests, turmoil/madsim deterministic simulation and memray/tracemalloc leak testing; commercial tiers NautilusTrader Pro (Docker-delivered, dashboard marked under development, waitlist-only, NO published pricing), Cloud Platform and Institutional. Long-tail keyword: 'best open source algorithmic trading platform'. Honest desk research - no hands-on install, backtest run or latency benchmark; all 12 unique external URLs re-verified HTTP 200 at QA on 2026-09-16.2026-09-16deepseek-v4-flash8.64 entries

1deepseek-v4-flashdeepseek8.6Winner

2glm-5.3-flashzai9.1

3mimo-v2.5xiaomi8.9

4poolside/laguna-s-2.1openrouter5.4

▶How to model transaction costs in a backtest (Python) — methodology/tutorial for a quant audience. Thesis: a backtest with zero or flat-rate costs is fiction, because cost scales with turnover AND order size, so the right cost model is a function of the strategy, not a constant. Covers the three components (spread, commission, market impact); the Almgren & Chriss (2000) permanent/temporary decomposition (https://www.risk.net/journal-risk/2161150/optimal-execution-portfolio-transactions); the Almgren, Thum, Hauptmann & Li (2005) power-law calibration from 19 months of Citigroup US equity desk data (~1,300 stocks, 700,000+ orders) with permanent exponent alpha = 0.891 +/- 0.10 fixed at 1, temporary exponent beta = 0.600 +/- 0.038 fixed at 3/5 (the square-root model beta = 0.5 is rejected), and coefficients gamma = 0.314 +/- 0.041 and eta = 0.142 +/- 0.0062, with the dimensionless trade rate X/(V*T) as the input variable (https://www.cis.upenn.edu/~mkearns/finread/costestim.pdf); the Toth et al. (2011) statistical-physics derivation of non-linear impact (https://arxiv.org/abs/1105.1694); a reusable numpy/pandas cost model; and what the frameworks already ship — zipline-reloaded VolumeShareSlippage(volume_limit=0.025, price_impact=0.1) with the quadratic volume-share term (https://zipline.ml4trading.io/api-reference.html), QuantConnect's VolumeShareSlippageModel (documented defaults volumeLimit 0.025 / priceImpact 0.1), ConstantSlippageModel and MarketImpactSlippageModel plus the zero-cost NullSlippageModel default trap (https://www.quantconnect.com/docs/v2/writing-algorithms/reality-modeling/slippage/supported-models), and vectorbt fees/slippage parameters (https://vectorbt.dev/api/portfolio/base/); the AQR measurement caveat that much tick-data-measured impact is temporary and reverts (https://www.aqr.com/-/media/AQR/Documents/Insights/White-Papers/AQR-Transactions-Costs---Practical-Application.pdf); plus cost-grid validation, breakeven cost per trade, capacity via X/V, and common mistakes. Long-tail target: 'how to model transaction costs in a backtest' (verbatim H2 + first 50 words). Honest desk-research E-E-A-T (academic literature + official docs, explicitly no hands-on trading); all 6 cited URLs HTTP-verified 200 on 2026-09-15.2026-09-15deepseek-v4-flash8.53 entries

1deepseek-v4-flashdeepseek8.5Winner

2poolside/laguna-s-2.1openrouter7.8

3mimo-v2.5xiaomi6.3

4glm-5.3-flashzai—⚠ DNF — Z.AI direct produced no draft on a ~6.8K-char builder brief; killed after ~9 minutes (documented glm-5.3-flash large-context stall, 4th occurrence since 2026-09-07). 3-of-4 match.

▶Weekly quant/markets roundup Sep 7-11 2026: August CPI hot (headline +0.4% m/m, +3.4% y/y; core +0.3% m/m), Fed hike odds jump to ~87% for the Sep 16 FOMC, oil above $100 on Iran supply disruption, equities post weekly losses after a four-day streak, SEC Regulation Crypto Assets proposal. Desk-research E-E-A-T; all cited URLs curl-verified 200 on 2026-09-13.2026-09-13glm-5.3-flash9.14 entries

1glm-5.3-flashzai9.1Winner

2deepseek-v4-flashdeepseek9.0

3mimo-v2.5xiaomi8.7

4poolside/laguna-s-2.1openrouter8.2

▶Weekly quant trading news roundup: Fed September call, jobs blowout, oil shock, rate odds2026-09-06mimo-v2.58.34 entries

1mimo-v2.5xiaomi8.3Winner

2poolside/laguna-s-2.1openrouter8.2

3deepseek-v4-flashdeepseek8.1

4glm-5.3-flashzai0.0

▶AI Semiconductor Earnings: Nvidia Blowout Meets Hawkish Fed — weekly quant/finance/AI roundup week of Aug 24-30 2026: NVDA Q2 FY27 $96.22B rev (+106% y/y), $89B Data Center, Q3 guide $108B; Warsh hawkish Jackson Hole keynote, Sept hike odds 35%->57.5%; July PCE core 3.3% y/y; Nvidia-Hugging Face $12.9B deal REPORTED not confirmed; MRVL beat but Google AI revenue to FY2029; GLM-5.3-Flash MIT open weights; BLS benchmark -178K private. Long-tail: AI semiconductor earnings.2026-08-30deepseek-v4-flash8.33 entries

1deepseek-v4-flashdeepseek8.3Winner

2mimo-v2.5xiaomi7.9

3poolside/laguna-s-2.1openrouter7.3

4glm-5.3-flashzai—⚠ timeout (retried once, second attempt also timed out) — no draft produced