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simulate_scenario

Evaluate hypothetical market scenarios for your portfolio. Input equity shocks or ticker-specific events to see dollar impact per holding and total portfolio, based on beta exposures.

Instructions

Call this for any short-horizon outlook question (1 day to 1 week). Trigger phrases: "내일", "tomorrow", "이번 주", "next week", "화요일", "수요일", "this Thursday", "how might X day look", "what if [macro event] happens", "FOMC 영향", "earnings 영향", "포트 어떻게 될까", "내 포트는 어떨까". DO NOT answer these from the brief alone — the brief is diagnostic only. This tool turns the brief's measured data into a quant answer. Thought experiment: deterministic conditional analysis. Given hypothetical market shocks, returns per-holding and portfolio P&L impact based on beta. Unlike project_net_worth (stochastic forward distribution over months/years), this is a point estimate per scenario for the immediate future — "if S&P moves −2% and your TSLA beta is 1.6, your TSLA P&L is impact_usd". Workflow: (1) call get_market_brief, (2) read holdings[].fundamentals.beta_5y into beta_overrides, (3) read risk_summary.daily_vol_pct into daily_vol_pct, (4) construct 2-3 scenarios (e.g. bear / base / bull at equity_pct -0.02 / 0 / +0.02; or hawkish / dovish for a Fed event). Caller MUST supply the shocks (the assumption) and disclose them in the answer. Without beta_overrides each ticker defaults to 1.0; without daily_vol_pct the one-sigma range is null. Use for next-day outlook ("if FOMC is hawkish?"), idiosyncratic event sizing ("my biggest position reports tomorrow, what's the dollar range?"), specific-day outlook ("화요일 시장 어떨까"), and rebalance impact ("if I trim TSLA and rotate to JNJ?"). For long-horizon projection (months/years, FIRE planning) use project_net_worth instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
display_currencyNoDisplay currency for output amounts (USD/KRW/EUR/JPY/CNY/GBP/HKD/INR/TWD).USD
scenariosYesOne to eight shock scenarios to evaluate in one call. Each is independent; results are returned in input order. Conventional patterns: directional triplet (bear/base/bull), conditional pair (event happens / does not), idiosyncratic single (one ticker stress).
beta_overridesNoPer-ticker beta overrides keyed by ticker. Pull from `get_market_brief` → `holdings[].fundamentals.beta_5y` for the user's actual exposure. Tickers with no override and no beta data default to 1.0.
daily_vol_pctNoPortfolio's measured 1-day stddev as a percentage (from `get_market_brief` → `risk_summary.daily_vol_pct`). Used to compute `one_sigma_range_usd` so each scenario can be sized against the portfolio's natural daily volatility.
Behavior5/5

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

No annotations are provided, so the description fully bears the burden. It transparently explains the deterministic conditional analysis approach, how beta_overrides and daily_vol_pct affect results, and what defaults occur (beta=1.0, one_sigma_range null). This gives a clear mental model of the tool's behavior.

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

Conciseness4/5

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

The description is thorough but slightly lengthy. However, it is well-structured with bold key terms, workflow steps, and examples. Every sentence serves a purpose, earning its place. Minor shortening could be possible without losing clarity.

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 the tool's complexity (4 parameters, nested objects) and the absence of an output schema, the description provides complete context: workflow, sourcing parameters, constructing scenarios, and distinguishing from sibling tools. No gaps remain for an AI agent to infer.

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?

Schema coverage is 100%, but the description adds significant value beyond the schema: it explains how to structure scenarios (bear/base/bull, conditional pairs), how beta_overrides are sourced from `get_market_brief`, and the conventional patterns. This greatly aids correct usage.

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?

The description precisely states the tool's purpose: answering short-horizon outlook questions (1 day to 1 week) by converting market brief data into quantitative scenario analysis. It clearly distinguishes from the sibling tool `project_net_worth` which handles long-horizon projections.

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 explicit usage guidance: trigger phrases, workflow steps (call get_market_brief first, then construct scenarios), and when NOT to use (long-horizon questions). It also specifies that the caller must supply shocks and disclose them, and directs to `project_net_worth` for months/years projections.

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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