Skip to main content
Glama

get_equity_curves

Retrieve backtested equity curves and performance metrics for quantitative strategies (momentum, mean-reversion, vol-targeting) on a single stock. Evaluates Sharpe, Sortino, max drawdown to compare strategy quality.

Instructions

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.

Parameters

symbol : str Exchange ticker in uppercase, e.g. "NVDA", "AAPL", "SPY".

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It implies a read-only retrieval of historical backtested data, which is likely safe, but it does not explicitly disclose behavioral traits like side effects, authentication needs, or data freshness. The return structure is documented, but behavioral guarantees are absent.

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?

The description is well-structured with a clear opening sentence, bullet-point usage guidance, and a separate parameter/returns section. It is concise—no redundant sentences—and front-loaded with the main 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 the tool has one parameter, no output schema, and no annotations, the description documents the return structure in detail (dictionary with symbol, strategies list including fields like total_return, sharpe_ratio, etc.). This fully compensates for the missing structured metadata.

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?

The single parameter 'symbol' in the schema lacks description, but the tool description adds valuable context: 'Exchange ticker in uppercase, e.g. "NVDA", "AAPL", "SPY".' This goes beyond the schema's type and title, providing formatting and examples.

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 clearly states the tool retrieves backtested equity curves and performance metrics for standard quantitative strategies applied to a single stock. This specific verb-resource combination distinguishes it from sibling tools like analyze_stock or get_ai_prediction.

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

Usage Guidelines4/5

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

The description provides explicit 'Use this tool when:' bullet points detailing three scenarios (evaluating rule-based strategies, needing risk-adjusted metrics, building options strategies). It offers clear context but does not specify when not to use the tool or alternatives.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/haiyunsky/hpsilab-mcp-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server