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analyze_stock

Get a unified bull/bear signal for any stock by aggregating AI prediction, implied volatility, options pressure, Monte Carlo simulation, and backtesting.

Instructions

Run a full institutional-grade quantitative analysis for a single stock.

This is the primary tool for a complete market view. It aggregates results from AI prediction, implied-volatility radar, options-pressure map, Monte Carlo simulation, and strategy backtesting into one unified signal.

Use this tool when:

  • You need a holistic bull/bear verdict with supporting evidence.

  • You want to compare multiple signal sources in a single call.

  • A user asks for a "stock analysis", "market view", or "trading signal".

Prefer the dedicated sub-tools (get_iv_radar, get_monte_carlo, etc.) when you need only a specific data dimension, to reduce latency and token usage.

Parameters

symbol : str Exchange ticker in uppercase, e.g. "NVDA", "AAPL", "SPY", "QQQ". Do NOT pass company names ("Nvidia") — use official tickers only.

Returns

dict with keys: symbol : str — normalized ticker signal : str — "Bullish" | "Bearish" | "Neutral" confidence_score: int — 0–100 directional confidence bullish_factors : list — evidence supporting an upward move bearish_factors : list — evidence supporting a downward move summary : str — one-sentence synthesis

Notes

  • Requires a valid HPSILAB_API_KEY.

  • Free-tier keys are limited to a predefined ticker set.

  • Response latency is ~5–15 s due to multi-model aggregation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, but description discloses key behavioral traits: requires HPSILAB_API_KEY, free-tier limitations on tickers, and typical response latency of 5-15 seconds. Also describes aggregation logic.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured with sections for description, usage, parameters, returns, and notes. Every sentence adds value; no redundancy. Front-loaded with core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given one parameter, no output schema, and sibling tools, description is complete. It explains purpose, usage, parameter semantics, return values (with dict keys), and behavioral notes. All necessary context is covered.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Only one parameter (symbol) with 0% schema coverage, but description provides thorough guidance: uppercase ticker required, examples given, explicit warning against company names. This adds substantial meaning beyond the minimal schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it runs a full institutional-grade quantitative analysis for a single stock, aggregating multiple signal sources. It explicitly positions itself as the primary tool for a holistic market view, distinguishing from specialized sub-tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear when-to-use conditions (holistic verdict, compare signals, specific queries) and when-not-to-use (prefer sub-tools for specific dimensions). Includes prerequisites like API key and free-tier limitations.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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