HPSILab Quant Finance

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. 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. - API access, quota, and ticker coverage are governed by the HPSILab account. - Response latency is ~5–15 s due to multi-model aggregation.

Generate chart image URLs for a stock: price chart, IV surface, and options flow heatmap. Use this tool when: - A user explicitly asks to "see", "show", or "visualize" a chart. - You want to accompany a written analysis with supporting visuals. - You need to share chart links in a report or message. Parameters ---------- force: Regenerate images instead of reusing cached artifacts. Defaults to True. types: Optional subset of chart types. Allowed values: ai_prediction, iv_radar, option_pressure, monte_carlo, equity_curves. Omit to generate every chart type. Note: Images are served as public URLs. They expire after 24 hours. If images do not render in your client, copy the URL and open it in a browser directly. Returns ------- dict with keys: symbol : str — normalized ticker price_chart_url : str — URL to candlestick + volume chart (PNG) iv_surface_url : str — URL to 3-D IV surface chart (PNG) options_flow_url: str — URL to options flow heatmap (PNG) expires_at : str — ISO 8601 expiry timestamp for the URLs

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: 1. Executive Summary — bull/bear verdict, confidence score, one-line thesis 2. AI Prediction — ensemble model votes, up-probability, regime 3. Volatility Analysis — ATM IV, IV rank, vol regime, risk reversal 4. Options Positioning — max pain, gamma wall, expected move, squeeze targets 5. Monte Carlo Outlook — 30-day price distribution, 90 %/68 % confidence ranges 6. 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. 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. - API access, quota, and ticker coverage are governed by the HPSILab account. - For programmatic use, prefer analyze_stock which returns structured JSON.

Get an AI/ML directional prediction for a stock's next-session move. Use this tool when: - You want a data-driven probability estimate for the next trading day's direction (up vs. down). - You need the individual model votes (ensemble breakdown) to assess consensus strength. - You want to compare model confidence against current IV pricing. The prediction engine uses an ensemble of gradient-boosted trees, an LSTM, and a VQC (quantum-classical hybrid) model. Features include VIX, relative strength, Treasury rates, and options flow signals. Returns ------- dict with keys: symbol : str — normalized ticker prediction : str — "Up" | "Down" | "Neutral" up_probability : float — 0.0–1.0 probability of upward close confidence : float — 0.0–1.0 ensemble agreement score model_votes : dict — per-model predictions and probabilities regime : str — "Bull" | "Bear" | "Chop" market regime signal_strength : str — "Strong" | "Moderate" | "Weak"

Retrieve backtested equity curves and performance metrics for standard quantitative strategies applied to a single stock. Use this tool when: - You want to evaluate how well rule-based strategies (momentum, mean- reversion, vol-targeting) have performed on this specific ticker. - You need risk-adjusted return metrics (Sharpe, Sortino, max drawdown) to compare strategy quality. - You are building a multi-leg options strategy and want historical context for the underlying's trending vs. mean-reverting behavior. Returns ------- dict with keys: symbol : str — normalized ticker strategies : list — each item is a dict with: name : str — strategy name total_return : float — cumulative return (e.g., 0.45 = +45 %) sharpe_ratio : float — annualized Sharpe ratio sortino_ratio : float — annualized Sortino ratio max_drawdown : float — maximum peak-to-trough loss (negative) win_rate : float — fraction of winning trades (0–1) pl_ratio : float — average win / average loss equity_curve : list — daily portfolio value series

Retrieve implied-volatility (IV) metrics for a single stock. Use this tool when: - You need to assess whether options are cheap or expensive relative to historical norms (IV rank / IV percentile). - You want the current volatility regime ("Low", "Normal", "Elevated", "Extreme") to frame risk sizing or strategy selection. - You are analyzing skew or risk-reversal direction (put-heavy vs call-heavy market). Do NOT use this tool if you already called analyze_stock — the IV data is included in that response. Returns ------- dict with keys: symbol : str — normalized ticker atm_iv : float — at-the-money implied volatility (annualized %) iv_rank : float — 0–100; ≥80 = expensive, ≤20 = cheap iv_percentile : float — historical percentile (0–100) risk_reversal : float — 25-delta risk reversal (positive = call-skew) volatility_regime: str — "Low" | "Normal" | "Elevated" | "Extreme"

Run a Monte Carlo price-path simulation for a stock over a 30-day horizon. Use this tool when: - You need a probabilistic price range rather than a single point estimate. - You want to quantify downside risk (e.g., probability of a 10 % drawdown). - You are sizing a position using a volatility-adjusted scenario. The simulation uses a GBM (Geometric Brownian Motion) model calibrated with the stock's realized volatility and current IV. 10,000 paths are run by default. Returns ------- dict with keys: symbol : str — normalized ticker current_price : float — spot price at simulation start mean_price : float — expected price at horizon range_90 : dict — {"lower": float, "upper": float} 90 % CI range_68 : dict — {"lower": float, "upper": float} 68 % CI prob_above_spot: float — probability (0–1) price is above current spot prob_10pct_drop: float — probability (0–1) of ≥10 % decline distribution : dict — histogram data: {"bins": list, "frequencies": list, "kde_x": list, "kde_y": list}

Retrieve options-market positioning and dealer-hedging pressure zones. Use this tool when: - You want to identify max-pain price (where option sellers face least loss at expiry) as a gravitational target near expiration. - You need to locate gamma walls (strike clusters with large open interest) that act as price magnets or resistance/support levels. - You want the expected-move range implied by the options market for the current weekly/monthly expiry cycle. Returns ------- dict with keys: symbol : str — normalized ticker max_pain : float — max-pain strike price gamma_wall : float — largest gamma concentration strike expected_move : float — ±expected move in dollars for nearest expiry squeeze_target: float — upside squeeze price target expiry_date : str — target expiry date (YYYY-MM-DD) pressure_zones: list — list of significant strike/OI concentration dicts

Run a pre-trade risk scan for adding a single stock to the user's tracked portfolio, covering volatility/beta/VaR/drawdown deltas, market regime, a forward return distribution, position-sizing checks, sector/symbol exposure impact, and correlation against existing holdings. Use this tool when: - You need a risk-first check before evaluating or placing a trade. - You want position-sizing guardrails (volatility, drawdown, beta, liquidity) evaluated against warn/fail thresholds, not just raw numbers. - You need to see how adding this symbol would shift sector or per-symbol concentration in the existing portfolio. - You want the new symbol's correlation to current holdings, to judge diversification benefit vs. redundant exposure. Do NOT use this tool for: - A standalone price-distribution simulation with no portfolio context → use get_monte_carlo instead. - A general bullish/bearish read on the stock → use analyze_stock. Example ------- get_pretrade_risk_scan("NVDA") Returns ------- dict with keys: symbol : str — normalized ticker asOf : str — ISO 8601 date the scan was computed regime : str — "bull" | "bear" | "chop" market regime regimeConfidence: float — 0–1 confidence in the regime classification riskDeltas : list — before/after risk metrics from adding the position, each item a dict with: label : str — e.g. "Annualized Volatility", "Beta (vs SPY)", "1-Day VaR (95%)", "Max Drawdown (1Y)" beforeValue : float — metric value for current portfolio afterValue : float — metric value after adding the position unit : str — "%" or "" (unitless, e.g. beta) higherIsRiskier : bool — whether an increase in this metric is worse distribution : dict — forward return distribution: {"bins": list, "frequencies": list,…

Reissue account credentials for an already authenticated HPSILab account. Use this tool when: A valid HPSILAB_API_KEY is required. Without one, this tool returns the same `api_key_required` registration prompt as every other SDK call and sends no request. New users register at https://hpsilab.com/register. After it succeeds, set HPSILAB_API_KEY to the returned `api_key` so the other tools authenticate as this account. The account is also bound to this caller server-side, so calls from here are recognised even before the environment variable is updated. The account is created **unverified**, which keeps the anonymous daily allowance until the emailed link is confirmed; confirming it unlocks the full Free plan. Tell the operator to click that link. Calling again returns the same account and a fresh key rather than creating a second one, so it is safe to retry if a key was lost. An address that already belongs to a different account is refused — you cannot attach yourself to someone else's account. Returns ------- dict email : str — the registered address tier : str — plan name, "free" on registration email_verified : bool — false until the emailed link is clicked api_key : str — set this as HPSILAB_API_KEY already_registered: bool — true when this caller already had an account message : str — next step, in plain language