Discover trading patterns from market data using LLM analysis. Uses Claude to analyze OHLCV data and generate candidate trading patterns with entry/exit conditions. Each pattern can be backtested afterward. Args: symbol: Trading pair (e.g. "BTCUSDT") timeframe: Bar timeframe — "5m", "15m", "1h", ...
Part of the Pypi:tradememory Protocol server.
Free to start. No card required.
AI agents call evolution_discover_patterns to retrieve information from Pypi:tradememory Protocol without modifying any data. This is common in research, monitoring, and reporting workflows where the agent needs context before taking action. Because read operations don't change state, they are generally safe to allow without restrictions -- but you may still want rate limits to control API costs.
Even though evolution_discover_patterns only reads data, uncontrolled read access can leak sensitive information or rack up API costs. An agent caught in a retry loop could make thousands of calls per minute. A rate limit gives you a safety net without blocking legitimate use.
Read-only tools are safe to allow by default. No rate limit needed unless you want to control costs.
{
"version": "1",
"default": "deny",
"tools": {
"evolution_discover_patterns": {}
}
}See the full Pypi:tradememory Protocol policy for all 15 tools.
These attack patterns abuse exactly the kind of access evolution_discover_patterns gives an agent. Each links to the full case and the policy that stops it:
Other read tools across the catalogue. The same approach applies to each: allow, with a rate cap to control cost.
Discover trading patterns from market data using LLM analysis. Uses Claude to analyze OHLCV data and generate candidate trading patterns with entry/exit conditions. Each pattern can be backtested afterward. Args: symbol: Trading pair (e.g. "BTCUSDT") timeframe: Bar timeframe — "5m", "15m", "1h", "4h", "1d" count: Number of patterns to generate (default 5) temperature: LLM creativity 0-1 (default 0.7, higher = more diverse) days: Days of history to analyze (default 90). It is categorised as a Read tool in the Pypi:tradememory Protocol MCP Server, which means it retrieves data without modifying state.
Register the Pypi:tradememory Protocol MCP server in PolicyLayer and add a rule for evolution_discover_patterns: 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_discover_patterns is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the evolution_discover_patterns 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_discover_patterns. 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_discover_patterns 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.
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