generate_stock_research_report
Generate a comprehensive stock research report with AI predictions, volatility analysis, options positioning, Monte Carlo outlook, and strategy backtests in institutional markdown format.
Instructions
Generate a structured, institutional-style markdown research report for a single stock, covering all major quantitative signal sources.
The report is divided into six sections:
Executive Summary — bull/bear verdict, confidence score, one-line thesis
AI Prediction — ensemble model votes, up-probability, regime
Volatility Analysis — ATM IV, IV rank, vol regime, risk reversal
Options Positioning — max pain, gamma wall, expected move, squeeze targets
Monte Carlo Outlook — 30-day price distribution, 90 %/68 % confidence ranges
Strategy Backtests — Sharpe, max drawdown, win rate across quant strategies
Output is a complete markdown string (~800–1200 words) ready to render or share. Response latency is ~10–20 s due to full multi-model data aggregation.
Use this tool when:
A user asks for a "report", "write-up", "research note", or "deep dive".
You want a pre-formatted narrative combining all signal sources in one document.
You need output suitable for archiving, PDF export, or investor communication.
Do NOT use this tool when:
You only need a quick directional verdict → use analyze_stock instead.
You need a specific data dimension (IV, Monte Carlo, etc.) → use the dedicated sub-tool (get_iv_radar, get_monte_carlo, etc.) for lower latency.
Parameters
symbol : str Exchange ticker in uppercase, e.g. "NVDA", "TSLA", "SPY". Do NOT pass company names — use official tickers only.
Returns
dict with keys: symbol : str — normalized ticker report : str — full markdown report (~800–1200 words, 6 sections) generated_at : str — ISO 8601 generation timestamp
Notes
Requires a valid HPSILAB_API_KEY.
Free-tier keys are limited to a predefined ticker set.
For programmatic use, prefer analyze_stock which returns structured JSON.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes |