HPSILab - Quant Finance MCP Server for Stock Analysis and Options Analytics
The hpsilab-mcp-server provides 8 institutional-grade quantitative finance tools for analyzing stocks via MCP or REST. All tools accept a single symbol parameter (e.g., "NVDA", "AAPL", "SPY").
analyze_stock— Aggregates AI prediction, IV radar, options pressure, Monte Carlo simulation, and backtesting into a single bull/bear/neutral verdict with a confidence score (0–100) and supporting evidence.get_ai_prediction— Ensemble AI directional prediction (gradient-boosted trees + LSTM + quantum VQC) for a stock's next-session move, including up/down probability, model votes, market regime, and signal strength.get_iv_radar— Returns ATM IV, IV rank/percentile, risk reversal direction, and volatility regime (Low / Normal / Elevated / Extreme) to assess whether options are cheap or expensive.get_option_pressure— Identifies options-market positioning data: max pain price, gamma walls, expected move range, squeeze targets, and significant pressure zones near expiration.get_monte_carlo— Runs 10,000-path GBM Monte Carlo simulation over a 30-day horizon, providing probabilistic price ranges (90% and 68% confidence intervals) and downside risk probabilities.get_equity_curves— Backtested equity curves and risk-adjusted performance metrics (Sharpe, Sortino, max drawdown, win rate) for standard quant strategies on a ticker.generate_stock_research_report— Generates a structured markdown research report synthesizing all signals (AI, IV, options, Monte Carlo, backtesting), suitable for sharing with investors.generate_stock_images— Returns public URLs for three charts: a candlestick price chart, a 3-D IV surface, and an options flow heatmap (URLs expire after 24 hours).
Enables quantitative analysis of US stocks and options within GitHub Copilot, offering tools for stock signals, volatility analysis, options positioning, scenario simulations, risk checks, and structured research reports.
HPSILab Quant Finance MCP Server for Stock & Options Analytics
HPSILab is an open-source Python quantitative finance MCP server for research on US equities, ETFs, and supported options. It brings stock signals, implied volatility, options analytics, Monte Carlo simulation, AI prediction, backtesting, and risk analysis into ChatGPT, Claude, Cursor, VS Code, and other MCP clients. Connect once, ask in natural language, and receive structured results that an assistant can compare and explain.
Research and educational use only. HPSILab does not provide investment advice and does not execute trades.
Get a Free API Key · Pricing · Tool reference · Client setup · Python SDK
Registry name |
|
Version | 0.10.0 — a source checkout reports |
Transports | Streamable HTTP (hosted) · stdio (PyPI package) |
Remote endpoint |
|
Package |
|
Authentication | Bearer API key, or |
Tools | 10 — nine financial research tools plus |
Connect: hosted Streamable HTTP
Recommended, and requires no local installation.
Register a free account, sign in, and generate an API key from Settings.
Add the server to your client's private configuration, replacing
hpsi_your_key. Never commit a real key or paste one into chat.
The example below is Claude Code's .mcp.json; other clients use different
configuration schemas, all covered in
client setup.
{
"mcpServers": {
"hpsilab": {
"type": "http",
"url": "https://hpsilab.com/mcp",
"headers": {
"Authorization": "Bearer hpsi_your_key"
}
}
}
}Verify the connection:
Use HPSILab to analyze AAPL. Separate observed metrics from interpretation,
identify conflicting signals, and finish with a concise risk summary.All financial research tools require a valid API key. See authentication for key handling and rotation.
Related MCP server: yahoo-finance-mcp-server
Connect: local stdio
For clients that require a local process:
pip install -U hpsilab-quant-finance-mcpThis example uses the mcpServers schema supported by Claude and Cursor; VS
Code and GitHub Copilot use a servers schema instead.
{
"mcpServers": {
"hpsilab": {
"command": "hpsilab-quant-finance-mcp",
"env": {
"HPSILAB_API_KEY": "hpsi_your_key"
}
}
}
}Then verify it through the MCP client:
Use HPSILab to get the AI prediction for NVDA and summarize the model consensus.The client discovers tools with MCP tools/list and invokes them with
tools/call. See
local setup
and
Python usage,
which also covers calling the tool functions directly from Python.
Tools
Nine financial research tools, plus register_account. Tool names and parameter
meanings are part of the public compatibility contract.
Tool | What it returns | Behavior |
| Aggregate directional and quantitative stock analysis | Read-only |
| Next-session prediction, confidence, and model consensus | Read-only |
| IV level, rank, percentile, skew, and regime | Read-only |
| Max pain, gamma walls, expected move, and pressure zones | Read-only |
| 30-day simulated distribution and probabilities | Read-only |
| Strategy backtests and risk-adjusted performance | Read-only |
| Position, exposure, correlation, and risk checks | Read-only |
| Hosted stock and options chart artifacts | Creates an artifact; not idempotent |
| Structured Markdown research report and timestamp | Creates an artifact; not idempotent |
| Account credentials for the authenticated caller | Creates an account and sends email; not idempotent |
Research tools accept one exchange ticker such as NVDA, SPY, or BRK.B;
company names are not accepted. Live results can change between calls. Artifact
tools can consume quota and should not be retried automatically.
Full inputs, outputs, side effects, and tool-selection guidance are in docs/tools.md.
Monte Carlo research example

Example visualization of scenario-based Monte Carlo research output. Results
depend on the selected inputs and model assumptions. See
get_monte_carlo
for tool details.
Copy-ready prompts
Claude
Use HPSILab to analyze NVDA. Summarize the directional signal, AI model
consensus, IV regime, options pressure, 30-day Monte Carlo range, and the
three most important risks. Distinguish tool data from interpretation.Cursor
Use HPSILab's IV radar and option-pressure tools for SPY. Compare IV rank,
percentile, skew, expected move, max pain, gamma wall, and pressure zones.
Return a compact table and do not recommend a trade.ChatGPT
Run the HPSILab pre-trade risk scan for TSLA. Explain every warning or failed
check, preserve unavailable fields as unavailable, and quote the returned
reason instead of guessing. Do not execute or recommend a trade.Setup guidance covers ChatGPT, Claude, Cursor, VS Code, GitHub Copilot, Continue, and Kimi. See the client setup guide for each client's transport and configuration format.
Errors, retries, and limits
Every failure is a structured object with a stable error_code, never prose an
agent has to pattern-match. Five refusals matter, because each has a different
remedy:
| Meaning | What resolves it |
| No key is configured | Registering. Nothing is sent downstream |
| Calling too fast (429) | Waiting — |
| The Credit balance is empty (402) | Adding Credits, or registering for trial Credits |
| The free evaluation ceiling is spent (402) | Registering, or verifying an email. Money does not lift it |
| A payment whose outcome is unconfirmed | Reconciliation. Do not retry it and do not pay again |
Without a key the package stops locally, before constructing the downstream client or sending a request:
{
"error": "api_key_required",
"message": "A free API key is required.",
"register_url": "https://hpsilab.com/register",
"docs_url": "https://hpsilab.com/developer/v2"
}401 and 402 responses are never retried. A 429 is retried only when it carries a
valid Retry-After. Read-only calls use a finite retry budget for timeouts and
recoverable 500/502/503/504 responses; artifact-producing calls are not retried
automatically. The package also applies one process-local safeguard of 10
requests per rolling minute per API key — burst protection, not a quota, since
only the hosted service knows the balance and the plan.
Field-by-field payloads, the Credits circuit breaker, and the reasoning behind each remedy are in docs/authentication.md and docs/python-sdk.md.
Why HPSILab
HPSILab gives assistants typed inputs, structured outputs, ticker validation, machine-readable errors, and dedicated tools instead of invented metrics. It supports US-listed equities, ETFs, and supported options data; coverage and limits depend on the hosted service and plan.
Safety and license
HPSILab is for research and education only. Outputs may be incomplete, delayed, or wrong and are not investment, financial, or trading advice. The MCP server has no brokerage connectivity, order entry, or trade-execution capability.
Licensed under the MIT License. Contributions are welcome; read AGENTS.md and CONTRIBUTING.md before proposing public schema changes.
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