"author:arhancanli" matching MCP servers:

A full finance desk for your AI agent: 355 tools in one free server with no API key, reading SEC filings, earnings guidance, insider trades, options, rates and macro data from the source, plus quant analytics and backtest validation. The agent sees 5 tools and searches the rest, so it stays light on context.

Enables verifying citations in reference lists and BibTeX by detecting fabricated or mismatched references and retractions, and returning corrected BibTeX.

Check a backtest before trusting it: deflated Sharpe, CSCV overfitting, minimum track record length, breadth ceiling and paper-evidence checks. Deflated Sharpe and track record are tested in CI against the papers' worked examples, CSCV against CRAN pbo. Runs hosted with no signup, or fully on your machine in private local mode. Also reads SEC-reported company financials by ticker.

Enables agents and users to explore 117 open benchmark problems, check candidate certificates against exact offline sandboxed checkers, compare values to best-known records, and retrieve verified results, replays, and citations. Every check yields a verdict and exact value, never an estimate.

Enables AI assistants to search and read an open quant research record: papers, topics, killed candidates, trial counts, live paper-trading sleeves, and a tamper-evident chain head. It provides six read-only tools over public CDN files, returning each source's stated limits alongside paper-execution-only caveats.

Enables AI agents to retrieve and cross-check public market, company, filing, economic, and alternative data from primary sources, including SEC filings, financial statements, screening, insider activity, options, prediction markets, DeFi metrics, and central-bank and macroeconomic series. Results include source URLs and evidence so answers can be cited and verified.

Enables local, keyless quantitative finance analysis across performance and risk, options and exotics, fixed income, portfolio construction, econometrics, technical indicators, strategy backtests, and data/ML labeling on user-supplied data without network calls.

Enables AI agents to build point-in-time factors from SEC filings and run cross-sectional signal backtests on locally supplied prices, with lookahead blocked in code, costs and market impact charged on real trades, crisis and out-of-sample checks, and every variant logged so the best run is judged against the whole search.