io.github.mnemox-ai/tradememory-protocol MCP Server

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

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Project status (August 2026): Feature-complete, in maintenance mode — bug and security reports are still reviewed; no new features or hosted service are planned. For paid work, see Trading Record Analysis.

Your trading AI has amnesia. And regulators are starting to notice.

It makes the same mistakes every session. It can't explain why it traded. It forgets everything when the context window ends. Meanwhile, MiFID II is raising the bar for algorithmic decision documentation (Article 17). The EU AI Act demands systematic logging of AI actions (Article 14). Your competitors' agents are learning from every trade.

The AI trading stack is missing a layer. Every MCP server handles execution — placing orders, fetching prices, reading charts. None handle memory.

Your agent can buy 100 shares of AAPL but can't answer: "What happened last time I bought AAPL in this condition?"

TradeMemory is the memory layer. One pip install, and your AI agent remembers every trade, every outcome, every mistake — with a SHA-256 tamper-evident audit trail.

Used in production by traders running pre-flight checklists before every position, and by EA systems logging thousands of decisions daily.

What it does

• Before trading: ask your memory — what happened last time in this market condition? How did it end?
• After trading: one call records everything — five memory layers update automatically
• Safety rails: confidence tracking, drawdown alerts, losing streak detection — the system tells you when to stop

Works with any market (stocks, forex, crypto, futures), any broker, any AI platform. TradeMemory doesn't execute trades or touch your money — it only records and recalls.

Quick Start

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."

<details> <summary>Claude Code / Cursor / Docker</summary>

# 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

</details>

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

Who uses TradeMemory

	US Equity Trader	Forex EA System	Compliance Team
Market	Stocks (AAPL, TSLA, ...)	XAUUSD (Gold)	Multi-asset
How	Pre-flight checklist before every trade	Automated sync from MT5	Full decision audit trail
Key value	Discipline system — memory before every decision	Record why signals were blocked, not just executed	SHA-256 tamper-evident records for regulators
Details	Read more →	Read more →	Read more →

How it works

<p align="center"> <img src="assets/owm-factors.png" alt="OWM 5 Factors" width="900"> </p>

1. Recall — Before trading, retrieve past trades weighted by outcome quality, context similarity, recency, confidence, and emotional state (OWM Framework)
2. Record — After trading, one call to remember_trade writes to five memory layers: episodic, semantic, procedural, affective, and trade records
3. Reflect — Daily/weekly/monthly reviews detect behavioral drift, strategy decay, and trading mistakes
4. Audit — Every decision is SHA-256 hashed at creation. Export anytime for review or regulatory submission

MCP Tools

Category	Tools	Description
Memory	remember_trade · recall_memories	Record and recall trades with outcome-weighted scoring
State	get_agent_state · get_behavioral_analysis	Confidence, drawdown, streaks, behavioral patterns
Planning	create_trading_plan · check_active_plans	Prospective plans with conditional triggers
Risk	check_trade_legitimacy	5-factor pre-trade gate (full / reduced / skip)
Audit	export_audit_trail · verify_audit_hash	SHA-256 tamper detection + bulk export

<details> <summary>All 20 MCP tools + REST API</summary>

Category	Tools
Core Memory	get_strategy_performance · get_trade_reflection
OWM Cognitive	remember_trade · recall_memories · get_behavioral_analysis · get_agent_state · create_trading_plan · check_active_plans
Risk & Governance	check_trade_legitimacy · validate_strategy · compute_dqs
Evolution	evolution_fetch_market_data · evolution_discover_patterns · evolution_run_backtest · evolution_evolve_strategy · evolution_get_log
Audit	export_audit_trail · verify_audit_hash · verify_audit_chain · get_daily_root

REST API: 35+ endpoints for trade recording, reflections, risk, MT5 sync, OWM, evolution, and audit. Full reference →

</details>

Trading Record Analysis

TradeMemory itself is free and self-hosted. What the maintainer offers as a paid service is statistical analysis of your own trading records: export your MT4/MT5 history and get a descriptive-statistics report — where your losses concentrate, how your position sizing changes after losses, forced-liquidation structure, and the actual risk you took per trade — followed by a walkthrough call.

Descriptive statistics of past trades only: no trade signals, no investment advice, no performance promises. Your files are deleted after delivery.

dev@mnemox.ai | Book a call

Enterprise & Compliance

Every trading decision your agent makes — including decisions not to trade — is recorded as a Trading Decision Record (TDR). Per-record SHA-256 content hashes are linked into a forward-chained audit ledger; every UTC day is summarised by a Merkle root which itself chains across days. Tampering with any historical record invalidates every subsequent link.

Regulation	Requirement	TradeMemory Coverage
MiFID II Article 17	Record every algorithmic trading decision factor	Full decision chain: conditions, filters, indicators, execution
EU AI Act Article 14	Human oversight of high-risk AI systems	Explainable reasoning + memory context for every decision
EU AI Act Article 12	Automatic, tamper-resistant logs over system lifetime	Linked SHA-256 chain + daily Merkle roots (RFC 3161 TSA in Phase 1.5)

# Verify a single record hasn't been tampered with
verify_audit_hash(trade_id="MT5-7047640363")
# → {"verified": true, "chain_entry": {"sequence_num": 42, ...}}

# Walk the entire chain (or a slice) end-to-end
verify_audit_chain(from_seq=1, to_seq=None)
# → {"verified": true, "checked_count": 1284, "first_break_at": null}

# Daily Merkle root — single 32-byte anchor over every TDR for that day
get_daily_root(date="2026-05-14")
# → {"verified": true, "root_hash": "a05544...", "record_count": 18}

# Bulk export for regulatory submission
GET /audit/export?strategy=VolBreakout&start=2026-03-01&format=jsonl

See LIMITATIONS.md for the full audit-chain maturity statement, including what's not in v0.5.2 yet (TSA timestamping, external anchoring, zkML proof of inference).

Need a custom deployment for your fund? → dev@mnemox.ai

Security

• Never touches API keys. TradeMemory does not execute trades, move funds, or access wallets.
• Read and record only. Your agent passes decision context to TradeMemory. It stores it. That's it.
• Local-first. The only outbound call is RFC 3161 trusted timestamping of daily audit roots — a 32-byte hash, no trade data (on by default; disable with TRADEMEMORY_TSA=off). Nothing else leaves your machine.
• SHA-256 chained audit ledger. Every record is hashed at creation and linked to the previous record. Daily Merkle roots anchor the chain. Verify integrity at the record, slice, or day level. Tampering is detectable at every level; external anchoring (TSA by default) is on the roadmap.
• 1,400+ tests passing. Full test suite with CI.

Research Status

TradeMemory's OWM framework is grounded in cognitive science (Tulving 1972) and reinforcement learning (Schaul et al. 2015). Current status:

• OWM five-factor scoring: implemented, tested (1,400+ tests)
• Statistical validation: DSR, MBL implemented (Bailey-de Prado 2014)
• Audit trail: SHA-256 tamper-evident TDR
• Evolution engine: research phase (strategy generation works, statistical gate pass rate under optimization)
• Hybrid recall: OWM-only mode active, vector fusion available when embeddings configured
• Empirical validation: ongoing (n=40 trades, target n>=100 for statistical significance)

Documentation

Doc	Description
Getting Started	Install → first trade → pre-flight checklist
Use Cases	3 real-world production scenarios
API Reference	All REST endpoints
OWM Framework	Outcome-Weighted Memory theory
Architecture	System design & layer separation
Tutorial	Detailed walkthrough
MT5 Setup	MetaTrader 5 integration
Research Log	Evolution experiments & data
Failure Taxonomy	11 trading AI failure modes
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.

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