An AI-powered quantitative trading simulation system that automates strategy generation and execution using real-market data.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ai-quant-trader
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 ai-quant-trader 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 AI Quant Trading Assistant?
The AI Quant Trading Assistant is a sophisticated framework designed for AI-driven quantitative trading simulations. By leveraging the AKShare library for real-time market data and LLMs for strategy synthesis, this entry in the Openclaw Skills library allows developers to build, backtest, and automate trading workflows. It bridges the gap between complex financial datasets and actionable trading insights, providing a safe, simulated environment for testing high-frequency or long-term investment theories.
Whether you are a seasoned quant or a developer exploring fintech, this skill provides the necessary building blocks to automate portfolio management. It integrates seamlessly into the Openclaw Skills ecosystem, offering both a full Python-based implementation for power users and a simplified dialogue-based version for quick analysis and strategy ideation.
AI Quant Trading Assistant Use Cases
- Rapidly prototyping and backtesting new trading strategies using natural language descriptions.
- Automating stock screening based on technical indicators like MACD or RSI.
- Managing risk through automated stop-loss and take-profit triggers in a simulated portfolio.
- Evaluating AI-generated trading logic against real-time market data to refine win rates.
How AI Quant Trading Assistant Works
- Initialize the simulated trading environment by setting a starting capital via the command interface.
- Generate a custom trading strategy by providing a natural language description to the AI engine.
- Screen for potential trades using AI-driven daily recommendations or specific technical conditions.
- Enable automated trading for specific stock tickers using the generated strategy logic.
- Monitor portfolio performance through win-rate statistics and automated risk management protocols like trailing stops.
AI Quant Trading Assistant Setup
To get started with this skill from the Openclaw Skills repository, ensure you have the necessary Python environment or use the simplified dialogue-based version. To install dependencies for the local engine, use:
pip install akshare pandas numpy
Once installed, you can launch the assistant and use commands like /交易 设置本金 100000 to initialize your simulation.
AI Quant Trading Assistant Data Schema & Taxonomy
The skill organizes its internal logic through a modular Python structure to ensure clean data flow and extensibility:
| File | Responsibility |
|---|---|
broker.py |
Handles the simulation engine, order matching, and transaction fee rules. |
strategy_gen.py |
Contains the core AI logic for translating natural language into trading strategies. |
risk_manager.py |
Manages risk parameters including stop-loss, take-profit, and trailing stops. |
stock_screener.py |
Filters the market based on real-time indicators and AI analysis. |
data_provider.py |
Interfaces with the AKShare API to fetch historical and live market data. |
AI Quant Trading Assistant Advanced Features
- Support for custom transaction fee rules mimicking real-world brokerage structures.
- Dynamic strategy parameter optimization using large language models to refine entry and exit points.
- Multi-ticker automated monitoring and execution across different market sectors.
- Trailing stop-loss (mobile take-profit) functionality to lock in gains during trend reversals.
- A simplified interaction mode available directly through the Openclaw Skills interface for environments without Python dependencies.
SKILL.md
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