Tradememory Protocol

Getting Started | Use Cases | API Reference | OWM Framework | Limitations | 中文版

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TradeMemory remembers what it cost. It is an open-source, local-first memory and brake for AI trading agents: it pulls in your fills, finds where your own history loses money, puts those losing trades in front of your agent before the next order, and can refuse an order that breaks rules you set, before it reaches the broker.

Brokers now let AI agents trade over MCP, with different guardrails: Webull and tastytrade set size or buying-power limits, Interactive Brokers only lets the agent draft an order for you to submit, and Robinhood and Public set no cap you can impose on an external agent (StockBrokers, 2026-09-30). The caps are fixed numbers. None of them looks at how your own past trades went.

Start with your own history

pip install tradememory-protocol

# Hyperliquid: public fills, no key needed
tradememory sync hyperliquid --address 0xYourAddress

# Alpaca: read-only calls with your own keys, kept in a local file
tradememory sync alpaca --env-file ~/.secrets/alpaca.env

Each closed trade is stored in memory once; running it again stores only new trades. Then it prints where your history loses money. The format, with illustrative numbers:

After 2 losses in a row (20 trades):
  5 of them (25%) were 1.5x your usual size or more.
  All 20 won 60% and made -$1,500.
  The 5 sized-up trades won 20% and made -$1,700.

Median hold: winners 1.5h, losers 9.0h.

These are descriptive statistics of your own past trades, not advice about the next one. Hyperliquid's API serves only an address's recent fills, not its whole history, so the sooner it is synced, the more history is kept. Other venues: MT5 and Binance spot sync scripts are in scripts/, and any agent can record a trade with remember_trade.

Before the next order

recall_memories(order="losses_first") puts every losing trade ahead of the rest, the bigger losses in similar conditions first, with their size and P&L. Besides the symbol's most recent trades, it searches the symbol's recent losing trades, so an older loss is not crowded out. The server tells connected agents to call it before proposing a trade. The default order ranks better outcomes higher (by R multiple, where one was recorded) and keeps at least 20% losses in the list.

Connect your agent

pip install tradememory-protocol

Add to Claude Desktop (claude_desktop_config.json):

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

Then tell Claude: "Record my AAPL long at $195 — earnings beat, institutional buying, 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

Full walkthrough: Getting Started (Trader Track + Developer Track)

Put a brake in front of your broker (preview)