TradeMemory — Decision Audit Trail for AI Trading Agents

Every Binance skill executes trades. None of them record why.

TradeMemory is the compliance layer. When your AI agent opens a position using the Spot or Futures skill, TradeMemory captures the full decision context: what conditions triggered the signal, which filters passed or blocked, the market indicators at that moment, risk state, and execution details. Every record is SHA-256 hashed for tamper detection.

This matters because regulators now require it. MiFID II Article 17 mandates algorithmic trading audit trails. The EU AI Act (August 2025) requires high-risk AI systems to maintain systematic logging of every action and decision path. ESMA's February 2026 supervisory briefing specifically targets AI-driven trading. Non-compliance fines reach up to 15M EUR or 3% of global turnover.

What TradeMemory Records

For every trading decision your agent makes:

Real Decision Event

This is a real decision event from a XAUUSD trading system running three automated strategies. The AI agent detected a SHORT breakout signal but the sell_allowed filter blocked execution:

{
  "ts": "2026-03-26 07:55:00",
  "strategy": "VolBreakout",
  "decision": "FILTERED",
  "signal_triggered": true,
  "signal_direction": "SHORT",
  "conditions_json": {
    "conditions": [
      {"name": "breakout_high", "passed": false, "current_value": 4462.58, "threshold": 4569.75, "operator": ">"},
      {"name": "breakout_low", "passed": true, "current_value": 4462.58, "threshold": 4463.11, "operator": "<"}
    ]
  },
  "filters_json": {
    "filters": [
      {"name": "spread_gate", "passed": true, "blocked": false, "current_value": 12.0, "threshold": 0.0},
      {"name": "sell_allowed", "passed": false, "blocked": true, "current_value": 0.0, "threshold": 0.0},
      {"name": "account_risk", "passed": true, "blocked": false, "current_value": 0.0, "threshold": 0.0},
      {"name": "regime_gate", "passed": true, "blocked": false, "current_value": 0.0, "threshold": 0.0}
    ]
  },
  "indicators_json": {
    "atr_d1": 171.16,
    "atr_m5": 8.53,
    "asia_high": 4544.08,
    "asia_low": 4488.78,
    "asia_range": 55.30
  },
  "regime": "TRENDING",
  "regime_ratio": 0.335,
  "consec_losses": 0,
  "cooldown_active": false,
  "risk_daily_pct": 0.0
}

A regulator or risk manager can read this and immediately understand: the agent saw a valid breakout, but policy blocked the SHORT direction. No guessing, no black box.

How It Works with Binance Skills

Your AI Agent
    |
    |--- [1] Binance Spot Skill: execute BUY 0.01 XAUUSD
    |
    |--- [2] TradeMemory Skill: record WHY this trade was made
    |         - conditions that triggered the signal
    |         - filters that passed/blocked
    |         - market indicators at decision time
    |         - risk state and regime context
    |         - SHA-256 hash for tamper detection
    |
    |--- [3] Later: query /audit/verify/{trade_id} to prove the record hasn't been altered

Installation

pip install tradememory-protocol

Start the server:

python -m tradememory
# Server running at http://localhost:8000

API Endpoints

Record a Decision

POST /trade/record_decision
Content-Type: application/json

{
  "trade_id": "VB_20260326_0755",
  "symbol": "XAUUSD",
  "direction": "short",
  "strategy": "VolBreakout",
  "confidence": 0.75,
  "reasoning": "SHORT breakout detected. Price 4462.58 < asia_low 4488.78 - buffer. Blocked by sell_allowed filter.",
  "market_context": {
    "price": 4462.58,
    "session": "london",
    "regime": {"regime": "TRENDING", "atr_h1": 26.66, "atr_d1": 171.16},
    "decision_data": {
      "indicators": {"atr_d1": 171.16, "atr_m5": 8.53, "spread_pts": 12}
    }
  }
}

Audit: Get Decision Record

GET /audit/decision-record/{trade_id}

Returns a complete Trading Decision Record (TDR) with:

• Decision context (who, what, when, why)
• Memory context (similar past trades recalled)
• Market snapshot (indicators, regime, risk)
• SHA-256 data hash

Audit: Verify Integrity

GET /audit/verify/{trade_id}

{
  "trade_id": "VB_20260326_0755",
  "verified": true,
  "stored_hash": "a3f8c9...",
  "computed_hash": "a3f8c9...",
  "match": true
}

Recomputes SHA-256 from stored inputs and compares. If any field was tampered with after recording, match will be false.

Audit: Bulk Export

GET /audit/export?strategy=VolBreakout&start=2026-03-01&end=2026-03-31&format=jsonl

Export all TDRs as JSON or JSONL for regulatory submission.

Security

• TradeMemory never touches API keys. It does not execute trades, move funds, or access wallets.
• Read and record only. The agent calls TradeMemory after making a decision, passing the context. TradeMemory stores it.
• No external network calls. The server runs locally. No data is sent to third parties.
• SHA-256 tamper detection. Every record is hashed at creation time. Verify integrity at any point with /audit/verify.
• 1,234 tests passing. Full test suite with CI.
• Scale: 17 MCP tools, 35 REST endpoints, 5-layer memory architecture (episodic, semantic, procedural, affective, prospective).

Regulatory Alignment

MCP Integration

TradeMemory also runs as an MCP server with 17 tools:

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

Key MCP tools: store_trade, recall_trades, get_performance, daily_reflection, audit_decision_record, audit_verify.

Links

• PyPI: tradememory-protocol
• GitHub: mnemox-ai/tradememory-protocol (1,234 tests, MIT license)
• Author: mnemox-ai