Backtest a candidate pattern against historical OHLCV data. Takes a pattern dict (from discover_patterns) and runs a vectorized backtest. Returns fitness metrics: Sharpe ratio, win rate, trade count, max drawdown, total PnL. Args: pattern_dict: CandidatePattern as dict (from discover_patterns out...
Part of the Pypi:tradememory Protocol server.
Free to start. No card required.
AI agents invoke evolution_run_backtest to trigger processes or run actions in Pypi:tradememory Protocol. Execute operations can have side effects beyond the immediate call -- triggering builds, sending notifications, or starting workflows. Rate limits and argument validation are essential to prevent runaway execution.
evolution_run_backtest can trigger processes with real-world consequences. An uncontrolled agent might start dozens of builds, send mass notifications, or kick off expensive compute jobs. PolicyLayer enforces rate limits and validates arguments to keep execution within safe bounds.
Execute tools trigger processes. Rate-limit and validate arguments to prevent unintended side effects.
{
"version": "1",
"default": "deny",
"tools": {
"evolution_run_backtest": {
"limits": [
{
"counter": "evolution_run_backtest_rate",
"window": "minute",
"max": 10,
"scope": "grant"
}
]
}
}
}See the full Pypi:tradememory Protocol policy for all 15 tools.
These attack patterns abuse exactly the kind of access evolution_run_backtest gives an agent. Each links to the full case and the policy that stops it:
Other execute tools across the catalogue. The same approach applies to each: rate-limit and validate the arguments.
Backtest a candidate pattern against historical OHLCV data. Takes a pattern dict (from discover_patterns) and runs a vectorized backtest. Returns fitness metrics: Sharpe ratio, win rate, trade count, max drawdown, total PnL. Args: pattern_dict: CandidatePattern as dict (from discover_patterns output) symbol: Trading pair (e.g. "BTCUSDT") timeframe: Bar timeframe — "5m", "15m", "1h", "4h", "1d" days: Days of history to backtest against (default 90). It is categorised as a Execute tool in the Pypi:tradememory Protocol MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
Register the Pypi:tradememory Protocol MCP server in PolicyLayer and add a rule for evolution_run_backtest: 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 Pypi:tradememory Protocol. Nothing to install.
evolution_run_backtest 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 evolution_run_backtest 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 evolution_run_backtest. 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.
evolution_run_backtest is provided by the Pypi:tradememory Protocol MCP server (pypi:tradememory-protocol). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Deterministic rules across all 15 Pypi:tradememory Protocol tools. Per-identity grants. Full audit log. Live in minutes. Nothing to install.
Free to start. No card required.
4,600+ MCP servers and 31,000+ tools scanned and risk-classified.