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bkuri
by bkuri

backtest_monte_carlo

Analyze trading strategy robustness using Monte Carlo simulation to test outcome variations, assess risk, and calculate confidence intervals for informed decision-making.

Instructions

Perform Monte Carlo simulation for strategy robustness analysis.

Tests many outcome variations to assess robustness.

Args: strategy_name: Strategy to analyze pair: Trading pair

Returns: Monte Carlo analysis with confidence intervals and risk assessment

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
strategy_nameYes
pairYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Without annotations, the description carries the full burden and explains the simulation methodology ('tests many outcome variations') and return format ('confidence intervals'). However, it omits operational details like whether results are persisted, execution duration, or if this is a read-only computation versus a job submission.

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

Conciseness3/5

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

The description uses a docstring format with Args/Returns sections that efficiently organizes information, but contains redundancy between 'Perform Monte Carlo simulation for strategy robustness analysis' and 'Tests many outcome variations to assess robustness.'

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

Completeness3/5

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

Given the simple 2-parameter schema and presence of an output schema, the description provides sufficient context for basic invocation. However, it misses constraints on input values (e.g., strategy name format, pair syntax) and operational context that would be expected given the lack of annotations.

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

Parameters3/5

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

With 0% schema description coverage, the description provides basic semantic labels ('Strategy to analyze', 'Trading pair') that add meaning beyond the raw parameter names. However, it lacks format specifications, examples, or constraint details that would fully compensate for the schema deficiency.

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

Purpose4/5

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

The description clearly states the tool performs Monte Carlo simulation specifically for strategy robustness analysis, using specific verbs and identifying the domain. While it doesn't explicitly contrast with sibling tool risk_monte_carlo, the 'backtest' prefix and 'strategy' focus provide adequate differentiation.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives like backtest_comprehensive or risk_monte_carlo, nor does it mention prerequisites such as requiring an existing strategy or historical data availability.

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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