tradememory

name	tradememory
slug	tradememory
version	0.5.1
description	AI trading memory with outcome-weighted recall and autonomous strategy evolution. 17 MCP tools, 1,233 tests, works with any trading platform.
source	https://github.com/mnemox-ai/tradememory-protocol
repository	https://github.com/mnemox-ai/tradememory-protocol
homepage	https://github.com/mnemox-ai/tradememory-protocol
metadata	{"openclaw":{"emoji":"📊","category":"finance","requires":{"bins":["python3","pip"],"env":{"ANTHROPIC_API_KEY":"Required for LLM reflections and Evolution Engine (optional, rule-based fallback without it)","TRADEMEMORY_API":"API endpoint, defaults to http://localhost:8000 (optional)"}},"os":["linux","darwin","win32"],"homepage":"https://github.com/mnemox-ai/tradememory-protocol"}}

TradeMemory Protocol

Give your AI agent persistent trading memory. TradeMemory records every trade, recalls past decisions weighted by outcome quality, discovers behavioral patterns, and autonomously evolves new strategies from raw price data.

Outcome-Weighted Memory (OWM) — 5 memory types (episodic, semantic, procedural, affective, prospective) that score recall by P&L outcome, context similarity, recency, and confidence. Winning trades surface first.

Evolution Engine — LLM-powered strategy discovery. Feed it OHLCV data from any exchange, it generates candidate patterns, backtests them vectorized, validates out-of-sample, and graduates survivors. No manual rule writing.

Platform-agnostic — works with MT5, Binance, Alpaca, or any broker that outputs trade data. 1,233 tests passing. MIT licensed.

Installation

pip install tradememory-protocol

Verify:

python -c "import tradememory; print('TradeMemory ready')"

Setup

Claude Desktop (via uvx)

Add to your Claude Desktop MCP config:

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

Claude Code

claude mcp add tradememory -- uvx tradememory-protocol

Manual (local server)

python -m tradememory

Runs the MCP server on stdio. For the REST API server:

python -m tradememory.server
# Runs on http://localhost:8000

MCP Tools Reference

Core Memory (2 tools)

Tool	Purpose
get_strategy_performance	Aggregate stats per strategy: win rate, PnL, profit factor, best/worst trades
get_trade_reflection	Deep-dive into a specific trade's reasoning and lessons learned

OWM Cognitive Memory (6 tools)

Tool	Purpose
remember_trade	Store a trade into all 5 OWM memory layers with automatic behavioral updates
recall_memories	Outcome-weighted recall — scores memories by P&L, context similarity, recency, confidence
get_behavioral_analysis	Procedural memory stats: hold times, disposition ratio, lot variance, Kelly criterion
get_agent_state	Current affective state: confidence level, drawdown %, win/loss streaks, risk appetite
create_trading_plan	Create a prospective trading plan with entry/exit conditions and risk parameters
check_active_plans	Check status of active trading plans, evaluate against current market conditions

Evolution Engine (5 tools)

Tool	Purpose
evolution_fetch_market_data	Fetch OHLCV data from Binance for backtesting and pattern discovery
evolution_discover_patterns	LLM-powered pattern discovery from price data — generates candidate trading rules
evolution_run_backtest	Vectorized backtest of a candidate pattern — returns Sharpe, win rate, max drawdown
evolution_evolve_strategy	Full evolution loop: generate → backtest → select → eliminate across generations
evolution_get_log	Get log of past evolution runs with graduated strategies and graveyard

Decision Audit Trail (2 tools)

Tool	Purpose
export_audit_trail	Export trading decision records with SHA-256 tamper detection for compliance review
verify_audit_hash	Verify integrity of a trading decision record by recomputing its SHA-256 hash

Available Commands

Tell your agent these things in natural language.

Record a Trade

"Record my trade: XAUUSD long 0.05 lots, entry 5180, exit 5210, profit $150"

"Remember my XAUUSD short trade, entry 5200, exit 5165, profit $175. London session breakout, high volume, confidence 0.8."

Recall with OWM

"What trades have I taken in similar market conditions? Current context: ranging market, low volatility, Asian session."

Returns memories ranked by outcome-weighted score — winning trades in similar contexts surface first.

Check Performance

"Show my trading performance this week"

"Compare my VolBreakout vs IntradayMomentum strategy performance"

Behavioral Analysis

"Show my behavioral analysis — am I cutting winners short?"

Returns disposition ratio, hold time asymmetry, lot sizing variance vs Kelly criterion.

Agent State

"What's my current confidence level and drawdown?"

"Am I on tilt? Check my affective state."

Trading Plans

"Create a trading plan for XAUUSD long if price breaks above 5200 with ATR confirmation"

"Check my active trading plans against current market conditions"

Evolution Engine

"Evolve a strategy for BTCUSDT on the 1h timeframe — 3 generations, 10 candidates each"

"Discover 5 trading patterns from ETHUSDT 4h data over the last 90 days"

"Backtest this pattern against BTCUSDT 1h data"

"Show me the evolution log — which strategies graduated?"

AI Reflection

"Run a reflection on my last 20 trades"

"What patterns have you found in my London session trades?"

Security & Permissions

Network access during install: pip install downloads from PyPI. Standard Python package installation.

Network access at runtime: The MCP server runs on stdio by default — no network access. The REST server runs on localhost:8000 and does not make outbound requests. If ANTHROPIC_API_KEY is set, the reflection engine and Evolution Engine send data to the Claude API. Evolution Engine fetches OHLCV data from the Binance public API.

Environment variables: All environment variables are optional. They are stored in your local .env file and never logged or sent to external services (except ANTHROPIC_API_KEY which authenticates with the Anthropic API).

File system access: TradeMemory writes to a single SQLite database file (tradememory.db) in the project directory. No files are created or modified outside the project.

No implicit permissions: This skill does not auto-install dependencies, modify system files, or require elevated privileges.

Environment Variables

Variable	Required	Description
ANTHROPIC_API_KEY	No	Enables LLM reflections and Evolution Engine. Without it, reflections use rule-based analysis; Evolution is unavailable.
TRADEMEMORY_API	No	REST API endpoint, defaults to http://localhost:8000

Links

• GitHub: https://github.com/mnemox-ai/tradememory-protocol
• PyPI: https://pypi.org/project/tradememory-protocol/
• Tutorial: https://github.com/mnemox-ai/tradememory-protocol/blob/master/docs/TUTORIAL.md
• Demo: python scripts/demo.py (30 simulated trades, full L1→L2→L3 pipeline)

Related Skills

Skill	Path	Description
Strategy Validator	.skills/strategy-validator/SKILL.md	Validate trading strategies for overfitting using 4 statistical tests (DSR, Walk-Forward, Regime, CPCV). Use when the user says "validate my strategy", "check my backtest", or "is this overfitting?".