tradememory-protocol
Active·★ 1.0k·MIT·Updated 2026-05-28
★ Memory & Context★ Multi-Agent
Memory layer for AI trading agents. Outcome-weighted recall, autonomous strategy evolution, 15 MCP tools.
TradeMemory Protocol provides a persistent memory layer for trading agents, enabling them to recall past trades with context, discover patterns, and evolve strategies autonomously. It features outcome-weighted recall, cognitive memory architecture, and an evolution engine for strategy discovery. Available as an MCP server for integration with Claude and other agents.
#ai-agents#claude#crypto#evolution-engine#forex#mcp#mcp-server#mcp-servers
01
Features
01Persistent memory with full context for every trade
02Outcome-weighted recall (OWM) for adaptive learning
03Evolution Engine for autonomous strategy discovery
0415 MCP tools for memory, cognitive, and evolution operations
05Multi-platform support (crypto, forex, MT5, etc.)
02
Compatibility
Claude Desktop
MCP client
Verified via docs
Claude Code
CLI agent
Verified via docs
Python API
Python SDK
Verified via docs
Docker
Container
Verified via docs
03
Quick start
1
$ pip install tradememory-protocol
04
Use cases
↳Crypto trading: Feed BTC/ETH trades and discover timing patterns automatically
↳Forex + MT5: Auto-sync trades and leverage persistent memory for adaptive strategies
↳Developers: Build memory-aware trading agents with 15 MCP tools and 30+ REST endpoints
05
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Comments
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- yang xiaoMay 26, 2026
Interesting concept — giving trading agents persistent memory is a game changer for strategy continuity. Still early but the protocol design looks solid.
- onedayMay 25, 2026
文档有点薄，需要自己看源码才能搞清楚 memory schema 怎么定义。不过核心思路是对的，持久化交易记忆确实是 Agent 交易系统缺的一环。
