StoQuant — Quantitative Stock Research

io.github.ohadf2015Published by GitHub account ohadf2015v0.6.0Updated Nov 1, 2026

StoQuant: 61 tools — Q-Score, ML, screeners, alt-data, portfolio, backtest, strategy.

VerifiedSTDIODesktop onlyFinanceData & Analytics

Overview

AI-generated overview

StoQuant MCP gives an assistant read-only access to StoQuant's quantitative stock research: Q-Scores, ML predictions, screeners, ownership, macro and backtests.

What you can do
  • Fetch quotes, extended quotes, sparklines, benchmarks and sector performers
  • Research Q-Scores, signals, ML predictions and their explanations, strategic edges and evidence
  • Run prebuilt and custom screeners, and surface top-conviction, hidden-gem and multibagger candidates
  • Read insider trades and clusters, institutional holders, analyst estimates and targets, short interest, SEC filings and news
  • Pull social sentiment and trends, catalysts, alt-data, unusual options flow and macro indicators
  • Evaluate portfolios, run and review backtests, and inspect strategy presets and performance
When to use it
Useful when you want an assistant to research US equities with quantitative signals, screen a universe, check insider or institutional activity, or run and review backtests inside a chat, without leaving the conversation to look up data.
Requirements
Runs locally over stdio via npx (Node.js). Requires a StoQuant API key in STOQUANT_API_KEY; a free key covers core tools at 100 requests/day, and premium tools need a paid tier. Internet access to the StoQuant API is required. Optional variables set the base URL, rate limit, timeout and dev mode.
Before you install
The API key is a secret stored in the client config; revoke it from the StoQuant account page if exposed. Data comes from a third-party service and is sent to it on every call. Backtests persist simulation runs on the service. Signals and ML outputs are research inputs, not investment advice, and the README warns not to treat hit rate as skill.

Installation

In SourceWeft

  1. Open StoQuant — Quantitative Stock Research in the dashboard and add it to a workspace.
  2. Enable the server for the chats that should use its tools.

Desktop only via STDIO. STDIO servers start a local process, so they need the SourceWeft desktop host.

Other MCP clients

Follow the launch instructions in the repository.

README

stoquant-mcp

[npm version] [MCP Registry] [License: MIT] [Tools]

Model Context Protocol (MCP) server for StoQuant — gives Claude (and any MCP-compatible agent) direct, read-only access to StoQuant's quant signals, ML predictions, screening, ownership, and macro data.

Free to start. A free StoQuant API key runs the core tools (quotes, Q-Score, screeners, market regime) at 100 requests/day. Power removes the cap and unlocks the premium tools (ML, alt-data, insider clustering). Get a key at https://stoquant.com/account/api-keys.

Source & listings: GitHub · npm · Official MCP Registry (io.github.ohadf2015/stoquant-mcp) · Landing page

One-command install

bash
npx -y stoquant-mcp install

You'll be prompted for your API key. The installer writes the right claude_desktop_config.json for your OS, backs up any existing config, then prints the next step.

After install, fully quit and relaunch Claude Desktop. Type list available tools to confirm the stoquant_* tools are loaded.

Manual install (alternative)

Add this to your claude_desktop_config.json:

json
{  "mcpServers": {    "stoquant": {      "command": "npx",      "args": ["-y", "stoquant-mcp", "serve"],      "env": {        "STOQUANT_API_KEY": "sk_live_…"      }    }  }}

Config locations:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
  • Windows: %APPDATA%/Claude/claude_desktop_config.json
  • Linux: ~/.config/Claude/claude_desktop_config.json

Tools (61)

CategoryTools
Pricesget_quote, get_quotes_batch, get_extended_quote, get_sparklines, get_benchmarks, get_sector_performers
Researchget_qscore, get_signals_v2, get_ml_prediction, explain_ml_prediction, get_strategic_edges, get_quant_analytics, get_evidence
Signalsget_market_regime, get_signal_accuracy, get_accuracy_summary
Discoveryget_top_conviction, get_hidden_gems, get_multibagger_candidates, get_related_stocks, get_signal_accuracy_heatmap
Screenerlist_prebuilt_screens, run_prebuilt_screen, run_screener
Ownershipget_insider_trades, get_insider_cluster, get_big_insider_buys, get_institutional_holders, get_institutional_ratings, get_analyst_estimates, get_analyst_price_targets, get_analyst_changes, get_short_interest
Filings & Newsget_sec_filings, get_news, get_news_events
Socialget_social_sentiment, get_social_trend, get_trending_social, get_social_momentum, get_source_credibility
Catalystsget_catalysts, get_catalyst_watch
Alt-dataget_alt_signals (FDA, CPSC, SEC suite, clinical trials, GitHub, App Store, Hacker News, news tone — one call), get_alt_data (Google Trends, Wikipedia views, options IV)
Options flowget_unusual_options
Portfolioget_auto_portfolio_status, get_auto_portfolio_history, get_portfolio_health, optimize_portfolio
Backtestlist_backtest_templates, run_backtest, get_backtest, get_backtest_history
Strategyget_strategy_performance, list_strategy_presets, get_strategy_preset_candidates
Macroget_macro_indicator, get_macro_dashboard, get_macro_calendar, get_macro_themes

Most tools are read-only fetches. run_backtest persists a simulation run (no live or paper orders). There is no order placement, cash move, or holdings mutation. Each tool is annotated readOnlyHint so agents know it is safe to call.

Accuracy and the no-edge gate

get_accuracy_summary hits the public /public/accuracy/summary scorecard. Read excessSpreadPp (alpha spread) and dataSufficient — do not treat hitRate as skill. When dataSufficient && excessSpreadPp < 0, auto-portfolio buys are suppressed (gated:no_edge). Call this before any buy-oriented portfolio read.

Portfolio/backtest/strategy tools are evaluate/read/backtest/optimize. This server never places orders. POST /auto-portfolio/evaluate executes paper trades and is not exposed.

Two things to know

  • The Q-Score (get_qscore) is the headline 0-100 composite, computed on sector-residualized returns (sector beta removed). Start here for "is this a good stock".
  • The ML prediction (get_ml_prediction) returns the probability of outperforming the S&P 500 over a horizon (classification: bull|bear) — it is not a buy/hold/sell rating.

Resources

The server exposes reference docs an agent can read on demand:

  • stoquant://glossary — what Q-Score, ML outperformance, regimes, and signal accuracy mean, plus unit conventions.
  • stoquant://methodology — sector residualization, IC-weighting, ML health gating, and screener field semantics.

Prompts

Pre-built workflows that chain the tools:

  • analyze_ticker(ticker) — full single-stock deep dive (Q-Score + ML + ownership + edges → verdict).
  • find_opportunities(style) — generate and vet a shortlist for a style (value, momentum, growth, hidden gems).
  • market_briefing — top-down snapshot (regime + macro + benchmarks + conviction).

Using the screener

run_screener filters on exact camelCase field names. An unknown field is treated as null server-side and silently excludes every stock, so use the documented names:

trailingPE, priceToBook, pegRatio, marketCap, debtToEquity, currentRatio, grossMargins, operatingMargins, revenueGrowth, earningsGrowth, rsi14, fiftyDayAverageChangePercent, twoHundredDayAverageChangePercent, fiftyTwoWeekHighChangePercent, shortPercentOfFloat, numberOfAnalystOpinions, sector, regularMarketPrice, regularMarketVolume, regularMarketChangePercent.

Margins/growth are decimals (0.25 = 25%); marketCap is raw USD (250000000 = 250M). Operators: gt, gte, lt, lte, eq, between (value: [low, high]), in (value: [string, …]).

The screener reads from StoQuant's cached daily fundamentals (fast, refreshed daily). Prefer run_prebuilt_screen (pre-tuned) or run_screener with universe: "sp500" and a few filters.

Example prompts

  • "Get the Q-Score for NVDA and explain the top features driving the ML call."
  • "Show me the top 10 conviction stocks and their current ML outperformance odds."
  • "What's the current market regime, and which sectors are leading?"
  • "Run the undervalued-gems prebuilt screen on the sp500 universe."
  • "Screen sp500 for trailingPE under 15 and debtToEquity under 0.5."
  • "Get insider activity for PLTR over the last quarter."
  • "What is the live accuracy grade and excessSpreadPp? Do not buy if the tail is inverted."
  • "Show auto-portfolio last eval and holdings. If gated:no_edge, do not recommend buys."
  • "List backtest templates and run momentum on sp500 for 2023."

Security

  • Server requires STOQUANT_API_KEY — fails fast on boot if missing or malformed.
  • All requests use HTTPS. Bearer token in Authorization header. Never logged.
  • Tickers are validated (^[A-Za-z0-9.\-^]{1,10}$) before any external use.
  • Client-side rate limit: 200 req/min (configurable via STOQUANT_RATE_LIMIT_RPM).
  • 30s per-request timeout (configurable via STOQUANT_TIMEOUT_MS).
  • Server-side: API key is hashed (SHA-256) at rest, prefix-indexed for fast lookup, timing-safe-compared on verify.
  • Revoke any key from https://stoquant.com/account/api-keys at any time.

Environment variables

VarDefaultDescription
STOQUANT_API_KEY— (required)Your StoQuant API key (free or Power tier)
STOQUANT_BASE_URLhttps://stoquant.com/apiAPI base URL
STOQUANT_RATE_LIMIT_RPM200Client-side rate limit (req/min)
STOQUANT_TIMEOUT_MS30000Per-request timeout
STOQUANT_DEV0Set 1 to allow http:// base URLs (local dev only)

License

MIT

Source: README.md at commit b780334

Tools

0
SourceWeft has not started this package in its sandbox yet, so its tools are not known.

Version history

1
  1. v0.6.0LatestOct 18, 2026

Score

50% of the applicable checks met

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  • Reached in the last 30 days25 pts

    Keep the endpoint answering the MCP handshake; SourceWeft probes it every 30 days.

  • Auth declared10 pts

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  • Tools listed15 pts

    Answer tools/list without a credential, or ship a package that starts in a sandbox without one.

  • DescriptionRequired15 pts
  • Logo10 pts
  • Documentation15 pts
  • License10 pts