tradememory-protocol 0.5.1

MCP memory system for AI trading agents. Store, recall, and learn from past trades.

Tutorial | API Reference | OWM Framework | 中文版

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TradeMemory Protocol is an MCP server that gives AI trading agents persistent memory across sessions. Instead of forgetting every trade after each conversation, your agent stores decisions, discovers patterns, and adjusts strategy automatically — using a three-layer architecture inspired by ACT-R cognitive science.

When to use this: You're building an AI agent that trades forex, crypto, or equities via MT5, Binance, Alpaca, or any platform — and you want it to remember what worked, what didn't, and why.

How it works

1. Store — Your agent records trades with context (strategy, confidence, market regime) via MCP tools
2. Recall — Before the next trade, the agent retrieves similar past trades weighted by outcome (Outcome-Weighted Memory)
3. Evolve — The Evolution Engine discovers patterns across trades and generates new strategy hypotheses, validated with Deflated Sharpe Ratio

When to use TradeMemory vs alternatives

	TradeMemory	Raw Mem0/Qdrant	LangChain Memory	Custom SQLite
Trade-specific schema	✅ L1→L2→L3 pipeline	❌ Generic vectors	❌ Chat-oriented	❌ DIY everything
Outcome weighting	✅ Kelly + ACT-R	❌ Cosine only	❌ Recency only	❌ Manual
Strategy evolution	✅ Built-in engine	❌ Not included	❌ Not included	❌ Not included
MCP native	✅ 15 tools	❌ Custom wrapper	❌ Custom wrapper	❌ Custom wrapper
Statistical validation	✅ DSR + walk-forward	❌ None	❌ None	❌ None

News

• [2026-03] Onboarding CLI — tradememory setup wizard, doctor health check, 8-platform config generator
• [2026-03] v0.5.0 — Evolution Engine + OWM 5 memory types. 1,087 tests. Release Notes
• [2026-03] Statistical Validation — Strategy E passes P100% random baseline, Sharpe 3.24 walk-forward
• [2026-03] Live Paper Trading — Strategy E running on Binance via GitHub Actions (hourly)
• [2026-02] v0.4.0 — OWM Framework, 15 MCP tools, Smithery + Glama listed

Architecture

Three-Layer Memory

Quick Start

pip install tradememory-protocol

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "tradememory": {
      "command": "uvx",
      "args": ["tradememory-protocol"]
    }
  }
}

Then tell Claude: "Record my BTCUSDT long at 71,000 — momentum breakout, high confidence."

Claude Code / Cursor / Docker

# Claude Code
claude mcp add tradememory -- uvx tradememory-protocol

# From source
git clone https://github.com/mnemox-ai/tradememory-protocol.git
cd tradememory-protocol && pip install -e . && python -m tradememory

# Docker
docker compose up -d

Setup & Configuration

First-time guided setup:

tradememory setup

This walks you through:

1. Terms acceptance — trading disclaimer and data storage policy
2. Platform detection — auto-detects Claude Desktop, Claude Code, Cursor, Windsurf, Cline
3. Config generation — prints the exact JSON snippet for your platform
4. Health check — verifies database, MCP tools, and core functionality

Platform Configs

Generate config for any supported platform:

tradememory config              # interactive menu
tradememory config claude_code  # direct: auto-installs via CLI
tradememory config cursor       # prints .cursor/mcp.json snippet
tradememory config windsurf     # prints Windsurf config
tradememory config raw_json     # generic MCP JSON

Supported: Claude Desktop · Claude Code · Cursor · Windsurf · Cline · Smithery · Docker

Health Check

tradememory doctor        # core checks (~3s)
tradememory doctor --full # + REST API, MT5, Anthropic API

MCP Tools (15)

Category	Tools
Core Memory	store_trade_memory · recall_similar_trades · get_strategy_performance · get_trade_reflection
OWM Cognitive	remember_trade · recall_memories · get_behavioral_analysis · get_agent_state · create_trading_plan · check_active_plans
Evolution	evolution_run · evolution_status · evolution_results · evolution_compare · evolution_config

REST API (30+ endpoints)

Trade recording, outcome logging, history, reflections, risk constraints, MT5 sync, OWM, evolution.

Full reference: docs/API.md

OWM — Outcome-Weighted Memory

Full theoretical foundation: OWM Framework

Evolution Engine

Methodology & data: Research Log

Documentation

Doc	Description
Architecture	System design & layer separation
OWM Framework	Full theoretical foundation
Tutorial	Install → first trade → memory recall
API Reference	All REST endpoints
MT5 Setup	MetaTrader 5 integration
Research Log	11 evolution experiments
Roadmap	Development roadmap
中文版	Traditional Chinese

Contributing

See Contributing Guide · Security Policy

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MIT — see LICENSE. For educational/research purposes only. Not financial advice.

Built by Mnemox