The Quant Space Research Corpus

Aggregate questions about the corpus (how many papers disclose a Sharpe ratio / report a backtest / have code, the highest or lowest reported Sharpe, annual return or drawdown, averages, top-N, optionally in an asset class, since a year, net of costs) are answered exactly from the database with the papers linked (engine: sql). Otherwise a retrieval-augmented answer: the question is searched semantically over every paper page, guide and tool, and a language model answers ONLY from the retrieved pages, citing them as markdown links. Returns the answer text plus a sources array of {title, url, kind}. Scores quoted in the answer come from the page text; verify with get_paper. Metered (retrieval + generation): 5 calls per minute and a daily budget; when exhausted, use semantic_search / search_papers.

Side-by-side comparison of 2 to 10 corpus papers on methodology, datasets, transaction-cost treatment, out-of-sample validation, reproducibility and reported performance. Each cell is labelled author-reported or platform-classified and unknown values are explicit. Paid tool: $0.10 USDC per call via x402 v2 (solana). A call without payment returns a payment-required result.

Totals: papers, with code, average scores, counts by quadrant, by year (with average rigor) and by paper type, and the last update date.

Typed neighbourhood of a topic, method, paper, book or author in the quant knowledge graph, with stable ids, deterministic ordering, per-edge provenance and an explicit verified or inferred flag. Depth up to 3, up to 100 nodes. Paid tool: $0.02 USDC per call via x402 v2 (solana). A call without payment returns a payment-required result.

Source-grounded literature review for a quantitative-finance research question: ranked relevant papers, optional books, methodological comparison, supporting and contradictory evidence, limitations and open questions. Every claim cites corpus sources. Paid tool: $0.25 USDC per call via x402 v2 (solana). A call without payment returns a payment-required result.

Full Markdown text of one guide or learning path by slug (from list_guides), e.g. deflated-sharpe-ratio, evaluate-ml-trading-paper, score-guide.

Full record for one paper by arXiv id (e.g. 2409.01234), SSRN id (ssrn-1234567) or arXiv URL: abstract, summary, the written 'why this score' rationale, the Mermaid research flowchart, all labels, and five related papers.

Titles, slugs and one-line descriptions of the ~70 guides on evaluating, reproducing and operationalizing quant research, plus the learning paths (sequenced reading orders). Use get_guide for the text.

The 14 method hubs (econometrics-time-series, stochastic-calculus, deep-learning, reinforcement-learning, ...) with description, counts, rigor share and the slugs of the guides that teach each method's evaluation pitfalls.

The 17 topic hubs with slug, description, paper count, share of papers with rigor >= 7, and URLs. Use the slug as the topic filter in search_papers.

Search and filter the 5,000+ scored quant-finance papers. Returns compact records (scores, quadrant, topics, methods, authors, summary, paper_url, page_url) plus the total match count. Combine filters; use min_rigor 7 for credible empirical work; set has_code true for reproducible papers. Reported performance (sharpe, annual_return_pct, max_drawdown_pct, sample_start/end, transaction_costs, out_of_sample) is included when the paper discloses a backtest: filter with has_sharpe / min_sharpe and sort by sharpe or annual_return to answer 'how many papers disclose a Sharpe' or 'highest reported return' questions (use total for counts). Execution-realism flags (tcost_modeled, oos_method, data_resolution, evaluation_type) say whether trading results are net of costs and validated out of sample; empirical_rigor is capped at 6.0 when they are not (empirical_rigor_raw keeps the uncapped score). Follow up with get_paper for the abstract, score rationale and flowchart.

Meaning-based (vector + keyword hybrid) search over the full text of every paper page, practitioner guide, learning path, calculator and post, via Cloudflare AI Search. Use it for natural-language questions ("does momentum survive transaction costs in crypto?") or when search_papers' exact keyword matching finds nothing. Returns pages with a relevance score, a snippet of the best-matching chunk and, for papers, the scores and labels. No filters beyond kind; for structured filtering use search_papers. Metered upstream: at most 25 results per call, and a daily budget; when it is exhausted the tool says so and search_papers keeps working.