Backtest a strategy across multiple symbols (portfolio). Args: symbols: List of stock symbols strategy: Strategy type to apply start_date: Start date (YYYY-MM-DD) end_date: End date (YYYY-MM-DD) initial_capital: Starting capital position_size: Position size per symbol (0.1 = 10%) Strategy-specifi...
AI agents invoke backtest_portfolio to trigger actions in MaverickMCP. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call — builds kicked off, notifications sent, workflows started.
Backtesting executes a strategy simulation engine using real financial parameters (initial_capital, position_size, date ranges). While it doesn't move real money, it triggers external computation and produces results that could directly influence real trading decisions. It falls under Execute as it runs a strategy simulation whose effects depend heavily on the arguments provided.
From the tool's definition 'Backtest a strategy across multiple symbols (portfolio)' — runs a computational simulation/execution of trading strategies against historical data with capital parameters (initial_capital, position_size)
Documented attack patterns abuse exactly the kind of access backtest_portfolio gives an agent:
PolicyLayer is an MCP gateway — it sits between your AI agents and MaverickMCP, and nothing reaches the server without passing your rules. This is the rule we recommend for backtest_portfolio:
{
"version": "1",
"default": "deny",
"tools": {
"backtest_portfolio": {
"limits": [
{
"counter": "backtest_portfolio_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}backtest_portfolio stays usable, but rate-capped — a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
Free to start. No card required.
Backtest a strategy across multiple symbols (portfolio). Args: symbols: List of stock symbols strategy: Strategy type to apply start_date: Start date (YYYY-MM-DD) end_date: End date (YYYY-MM-DD) initial_capital: Starting capital position_size: Position size per symbol (0.1 = 10%) Strategy-specific parameters as individual arguments Returns: Portfolio backtest results with aggregate metrics. It is categorised as a Execute tool in the MaverickMCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Maverick MCP server in PolicyLayer and add a rule for backtest_portfolio: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches MaverickMCP. Nothing to install.
backtest_portfolio is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the backtest_portfolio rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for backtest_portfolio. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
backtest_portfolio is provided by the Maverick MCP server (wshobson/maverick-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 54 MaverickMCP tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
54 MaverickMCP tools catalogued and risk-classified — across an index of 42,500+ MCP servers.