tradememory
Persistent memory + autonomous strategy evolution for AI traders. Records every trade with full context across 5 memory layers (episodic, se
Open source Repository Open in the app JSON README (API)
About
Persistent memory + autonomous strategy evolution for AI traders. Records every trade with full context across 5 memory layers (episodic, semantic, procedural, strategic, affective). Outcome-weighted recall ranks past trades by P&L relevance. Evolution Engine discovers new strategies from raw price data using LLM-powered pattern discovery + vectorized backtesting. 15 MCP tools, 5 slash commands, 3 domain knowledge skills. 200+ trading MCP servers execute. None remember. TradeMemory does.
Details
- Kind
- Plugins
- Topic
- AI, RAG & memory
- Publisher
- mnemox-ai
- Origin
- marketplace
- Category
- ferramentas
- Stars
- 1
- Forks
- 1
- Last push
- 2026-03-16T14:40:15Z
- Repository state
- ativo
- Added
- 2026-08-30 01:48:58
- Updated
- 2026-08-30 01:48:58
- Origin id
mnemox-ai/tradememory-plugin/tradememory
README
# TradeMemory Plugin Persistent memory + autonomous strategy evolution for AI traders. 200+ trading MCP servers execute. None remember. TradeMemory does. ## Installation ### From GitHub (recommended) ```bash git clone https://github.com/mnemox-ai/tradememory-plugin.git claude --plugin-dir ./tradememory-plugin ``` ### Manual Copy the plugin directory into your project or pass it directly: ```bash claude --plugin-dir /path/to/tradememory-plugin ``` ### MCP Server (standalone, no plugin needed) ```bash pip install tradememory-protocol claude mcp add tradememory -- uvx tradememory-protocol ``` ## Commands | Command | Description | |---------|-------------| | `/record-trade [details]` | Record a completed trade into all 5 OWM memory layers | | `/recall [context]` | Recall similar past trades, ranked by outcome-weighted score | | `/performance [strategy]` | Generate strategy performance report with behavioral analysis | | `/evolve [symbol] [tf] [gens]` | Discover new trading strategies from raw OHLCV data | | `/daily-review [date]` | AI-powered daily reflection on trades and behavioral patterns | ## Skills ### Trading Memory | Skill | Description | |-------|-------------| | **trading-memory** | OWM architecture, 5 memory types, recall scoring, behavioral baselines | | **evolution-engine** | LLM-powered strategy discovery, vectorized backtesting, OOS validation | | **risk-management** | Affective state monitoring, tilt detection, position sizing, behavioral guardrails | ## MCP Tools (15 total) ### Core Memory (4) - `store_trade_memory` — Store a trade with context - `recall_similar_trades` — Find past trades matching current context - `get_strategy_performance` — Aggregate stats per strategy - `get_trade_reflection` — Deep-dive into a trade's reasoning ### OWM Cognitive Memory (6) - `remember_trade` — Store across all 5 OWM memory layers - `recall_memories` — Outcome-weighted recall - `get_behavioral_analysis` — Disposition ratio, hold times, Kelly criterion - `get_agent_state` — Confidence, drawdown, streaks, risk appetite - `create_trading_plan` — Prospective trading plans - `check_active_plans` — Evaluate plans against current conditions ### Evolution Engine (5) - `evolution_fetch_market_data` — Fetch OHLCV from Binance - `evolution_discover_patterns` — LLM-powered pattern discovery - `evolution_run_backtest` — Vectorized backtesting - `evolution_evolve_strategy` — Full evolution loop - `evolution_get_log` — Evolution history and graveyard ## Example Workflows ### Record and Learn ``` /record-trade XAUUSD long 5180 5210 +$150 # Stores trade, updates all memory layers, shows similar past trades ``` ### Pre-Trade Check ``` /recall London session breakout, high volatility, XAUUSD trending up # Returns past trades in similar conditions, ranked by P&L outcome ``` ### Strategy Evolution ``` /evolve BTCUSDT 1h 3 # Discovers patterns → backtests → selects → mutates × 3 generations # Validates out-of-sample → graduates survivors ``` ### End of Day ``` /daily-review today # Analyzes today's trades, checks behavioral drift, updates affective state ``` ## Requirements - Python 3.10+ - `pip install tradememory-protocol` - Optional: `ANTHROPIC_API_KEY` for LLM reflections and Evolution Engine ## Links - [Plugin repo](https://github.com/mnemox-ai/tradememory-plugin) - [Core protocol](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)