A specialized memory management system for PAI trading agents that standardizes decision logs, tracks performance, and automates strategy refinement.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install trading-memory-management
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install trading-memory-management using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Trading Memory Management Skill?
The Trading Memory Management skill is a comprehensive framework designed for high-frequency or algorithmic trading environments. It provides a structured methodology for recording every decision, rationale, and market state, ensuring that your AI agent learns from every market movement. By integrating this skill within Openclaw Skills, developers can transform raw trading data into a high-value knowledge base that supports sophisticated backtesting and strategy optimization.
This skill bridges the gap between execution and learning. It doesn't just record what happened; it uses specialized scripts to evaluate the quality of those memories, extract long-term lessons, and maintain a clean, actionable state snapshot. This ensures that the trading system remains agile and informed by past successes and failures.
Trading Memory Management Skill Use Cases
- Standardizing daily trading logs to include timestamps, confidence levels, and execution results.
- Tracking system states across cycles to maintain continuity in automated trading strategies.
- Building a centralized repository of trading rules (R001-R006) and documenting their historical effectiveness.
- Creating an 'Error Ledger' to analyze recurring mistakes and implement preventative measures.
- Automating the migration of daily insights into long-term strategic memory files.
How Trading Memory Management Skill Works
- The system captures every trade decision (BUY/SELL/HOLD) and logs it into a date-specific Markdown file.
- A JSON-based state snapshot is updated simultaneously to keep track of cycle counts and system health.
- At 23:00 daily, the memory evaluator script assesses the quality of the day's records.
- High-value insights are automatically extracted and appended to a long-term memory file (MEMORY.md).
- On a weekly basis, the system archives logs older than 30 days and purges low-quality or redundant data to optimize storage and retrieval speed.
Trading Memory Management Skill Setup
To initialize this skill within your environment, ensure the directory structure is prepared and the Python utility scripts are accessible.
# Create the memory directory structure
mkdir -p memory/trading
# Initialize the state snapshot file
echo '{"last_decision_time": "", "cycle_count": 0, "daily_stats": {}, "system_state": "idle"}' > memory/crypto_trading_state.json
# Create essential tracking files
touch memory/trading_rules.md memory/trading_mistakes.md
Ensure that memory_evaluator.py and auto_memory_manager.py are configured to run as scheduled cron jobs or hooks within your Openclaw Skills workflow.
Trading Memory Management Skill Data Schema & Taxonomy
The skill utilizes a multi-file schema to ensure data is categorized by its lifecycle and utility:
| File Category | Path | Data Content |
|---|---|---|
| Daily Logs | memory/trading/YYYY-MM-DD.md |
Decision rationales, confidence levels, and execution outcomes. |
| State Snapshot | memory/crypto_trading_state.json |
Real-time counters, last decision timestamps, and session stats. |
| Strategy Rules | memory/trading_rules.md |
Formalized trading rules (R001-R006) and trigger conditions. |
| Mistake Tracker | memory/trading_mistakes.md |
Analysis of errors (M001-M010) and prevention steps. |
| Long-term Memory | MEMORY.md |
Distilled high-value experiences for cross-session learning. |
Trading Memory Management Skill Advanced Features
- Memory Quality Scoring: Integrated evaluation scripts to rank the usefulness of trading logs.
- Automated Lifecycle Management: Built-in weekly archiving protocols to prevent 'memory bloat' in long-running agents.
- Standardized Error Taxonomy: Pre-defined error codes (M001-M010) for faster root cause analysis.
- Cross-session State Persistence: Robust JSON snapshots that allow trading agents to resume context after restarts or crashes.
- Strategic Distillation: Logic to separate transient market noise from permanent trading wisdom within the Openclaw Skills framework.
SKILL.md
Loading
Related Openclaw Skills
HK_LEETF_Trading
A professional AI-driven skill for analyzing Hong Kong leveraged ETF holdings, rebalancing windows, and arbitrage opportunities.
Heartbeat Task Automator
A robust scheduling and execution engine for managing recurring AI agent tasks, trading logic, and system maintenance.
Capability Evolver
A powerful meta-skill that empowers AI agents to autonomously analyze history, self-repair, and evolve their capabilities.
CryptoTrading AI Agent
An autonomous AI-driven trading agent that executes data-backed crypto decisions every 10 minutes using dual-model verification and strict risk rules.
Windows UI Automation
A PowerShell-powered skill for programmatically controlling mouse, keyboard, and window states on Windows desktops.
Restaurant Evaluator
A professional evaluation system for catering entrepreneurs to scientifically assess site selection, calculate payback periods, and identify business risks.
Featured*
