| name | tradememory |
| slug | tradememory |
| version | 0.5.1 |
| description | AI trading memory with outcome-weighted recall and autonomous strategy evolution. 17 MCP tools, 1,233 tests, works with any trading platform. |
| source | https://github.com/mnemox-ai/tradememory-protocol |
| repository | https://github.com/mnemox-ai/tradememory-protocol |
| homepage | https://github.com/mnemox-ai/tradememory-protocol |
| metadata | {"openclaw":{"emoji":"📊","category":"finance","requires":{"bins":"[Truncated]","env":"[Truncated]"},"os":["linux","darwin","win32"],"homepage":"https://github.com/mnemox-ai/tradememory-protocol"}} |
TradeMemory Protocol
Give your AI agent persistent trading memory. TradeMemory records every trade, recalls past decisions weighted by outcome quality, discovers behavioral patterns, and autonomously evolves new strategies from raw price data.
Outcome-Weighted Memory (OWM) — 5 memory types (episodic, semantic, procedural, affective, prospective) that score recall by P&L outcome, context similarity, recency, and confidence. Winning trades surface first.
Evolution Engine — LLM-powered strategy discovery. Feed it OHLCV data from any exchange, it generates candidate patterns, backtests them vectorized, validates out-of-sample, and graduates survivors. No manual rule writing.
Platform-agnostic — works with MT5, Binance, Alpaca, or any broker that outputs trade data. 1,233 tests passing. MIT licensed.
Installation
pip install tradememory-protocol
Verify:
python -c "import tradememory; print('TradeMemory ready')"
Setup
Claude Desktop (via uvx)
Add to your Claude Desktop MCP config:
{
"mcpServers": {
"tradememory": {
"command": "uvx",
"args": ["tradememory-protocol"]
}
}
}
Claude Code
claude mcp add tradememory -- uvx tradememory-protocol
Manual (local server)
python -m tradememory
Runs the MCP server on stdio. For the REST API server:
python -m tradememory.server
MCP Tools Reference
Core Memory (2 tools)
