H|ψ⟩ Quantum Finance
Stock & Options Analytics for AI Agents
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HPSILab Quant Finance MCP Server for Stock & Options Analytics

Stock & options risk research.For you and your AI agent.

Understand market direction, options positioning and possible downside in one research workflow. Explore it on the web or connect through MCP.

Try NVDA analysis Connect your Agent

Create a free accountWatch 3 stocks · No credit card required

Explore the output

One ticker. A clearer picture of risk.

Start with NVDA. These are the latest available API snapshots, with dates and source details kept visible.

  1. 01

    Direction, with context

    Read the next-day probability alongside its data date, then open the full analysis for the model evidence.

  2. 02

    A range of outcomes

    Use the 10-day Monte Carlo range to explore possible price scenarios.

  3. 03

    Options beneath the price

    Review the volatility regime and gamma wall together with the options expiry.

Open full NVDA analysis →
Research another stock or ETF

Find HPSILab Across the AI Ecosystem

Explore all listings →
HPSILab in the Claude DirectoryListed on MCP ServersHPSILab Quant Finance MCP on MCPtryHPSILab Quant Research skill on AgensiHPSILab Quant Finance MCP on Plug and Ship

Your next step

Make the research part of your workflow.

Create a free account to follow 3 stocks and review risk changes. Building with an agent? Start with the connection guide.

Connect your AI agent →

MCP connection guide · REST API & Python documentation

or

Free plan: 3 stocks, daily risk alerts. Web use costs no Credits.

Paid features · free samples

Real outputs on fixed demo data, refreshed after each US close. A paid plan runs the same checks on your own watchlist.

Pre-Trade Check

Compare NVDA's own risk with the SPY benchmark, then see position sizing checks, plus sector exposure and correlation in a demo portfolio.

See the NVDA sample

Decision Intelligence

The risk scan and the full stock analysis combined into one risk summary for a proposed position size.

See the decision card

Grover Picks

Quantum search for the best 1–3 stock portfolio in a watchlist, checked against the classical best.

See the demo watchlist result
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What Agents Get

One quant finance engine, multiple decision lenses.

Generic feeds tell an AI agent what happened. HPSILab gives it probability, options structure, simulation range, and model evidence so the answer is easier to verify.

analyze_stockCredits
Aggregates every quant tool into one JSON stock analysis: direction signal, direction score, bullish/bearish factors, plain-English summary.
get_ai_predictionCredits
Next-day probability the stock closes up, a model direction signal (bullish / neutral / bearish) for research and paper trading only, not investment advice or a trade instruction, and how strongly the underlying models agree.
get_iv_radarCredits
Implied-volatility structure: how expensive options are, volatility squeezes, call/put skew.
get_monte_carloCredits
Monte Carlo price simulation for the next ~10 trading days: likely range and odds of finishing higher.
get_option_pressureCredits
Option-chain pressure map for the nearest expiry: Max Pain, dealer Gamma Wall, likely weekly high, extreme squeeze target.
get_equity_curveCredits
Backtest performance of the quant strategy across a watchlist: Sharpe ratio, max drawdown, win rate, total return.
generate_stock_imagesCredits
Generates stock-report chart images (PNG) and returns their URLs.
Pro
get_pretrade_risk_scanPro
Full pre-trade risk scan for a stock before entering a position.
generate_stock_research_reportPro
Presentation-ready markdown research report combining analysis and charts for a stock.
Install

Give your AI agent a market research layer.

Use MCP for agents and REST for your own backend, scripts, dashboards, and alert pipelines. Developer unlocks API keys, higher limits, and production integration support.

Install
pip install -U hpsilab-mcp
HPSILab in the Claude DirectoryPyPI versionGlamaListed on mcpservers.orgHPSILab Quant Finance MCP on Glama
Quick start
from hpsilab_mcp import HpsiMcpClient, HpsiMcpError try: client = HpsiMcpClient( api_key="YOUR_API_KEY", base_url="https://hpsilab.com", ) result = client.get_ai_prediction("TSLA") print(result) except HpsiMcpError as exc: print(f"Prediction request failed: {exc}")
View Developer setupGet API Key
Trust Boundary

Built for research, not blind trading.

Informational use only

HPSILab returns research context for agents and analysts, not buy/sell instructions.

Structured outputs

Responses are JSON-ready for agent workflows, dashboards, alerts, and review logs.

Review before action

Agents can draft watchlists and research reports, while humans remain in the loop.

Create a free accountSign in
HPSILab Quant Finance MCP Server for Stock & Options Analytics