A professional-grade toolkit for quantitative investment research, combining academic modeling libraries with industrial trading frameworks.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install jiadong-quant-tools
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 jiadong-quant-tools 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 Quant Tools?
Quant Tools is a comprehensive suite designed for developers and researchers building sophisticated financial models. It bridges the gap between academic theory and real-world execution by integrating specialized libraries for factor analysis, portfolio optimization, and causal inference. By leveraging this suite within Openclaw Skills, users can access a full-stack research pipeline that covers everything from data acquisition to AI-enhanced strategy validation.
The toolkit is structured to support high-performance vectorized backtesting and rigorous statistical verification. It addresses the common pitfall of overfitting by incorporating advanced causal inference tools, ensuring that strategies developed are robust and scientifically sound before deployment in live markets.
Quant Tools Use Cases
- Performing multi-factor effectiveness tests including IC, IR, and stratified backtesting.
- Building AI-driven trading strategies using Reinforcement Learning and BERT-based sentiment analysis.
- Optimizing asset allocation using Modern Portfolio Theory and Black-Litterman models.
- Conducting high-performance, tick-level backtesting for high-frequency trading strategies.
- Establishing a collaborative team research environment with centralized data gateways.
How Quant Tools Works
- Data Acquisition: Utilize AkShare to pull macro, fundamental, and market data into standardized formats.
- Factor Research: Analyze factor signals using Alphalens for validity and VectorBT for rapid vectorized backtesting.
- AI Enhancement: Integrate FinBERT for market sentiment factors or FinRL for training reinforcement learning agents.
- Causal Validation: Apply EconML to verify that strategy returns are driven by causal factors rather than random noise or overfitting.
- Portfolio Optimization: Calculate optimal asset weights and risk parity using PyPortfolioOpt.
- Execution & Deployment: Bridge the research to live trading via the VeighNa framework or expose models as microservices using FastAPI.
Quant Tools Setup
To begin using Quant Tools within your Openclaw Skills environment, follow these installation steps:
# Create and activate a specialized environment
conda create -n quant_env python=3.10
conda activate quant_env
# Install core research libraries
pip install akshare alphalens vectorbt pyportfolioopt econml
# For advanced derivative pricing (QuantLib)
conda install -c conda-forge quantlib-python
# Deploying the trading framework via Docker
docker pull vnpy/vnpy:latest
Quant Tools Data Schema & Taxonomy
The skill manages financial data across several specialized layers to ensure research integrity:
| Layer | Primary Tools | Data Format | Metadata Focus |
|---|---|---|---|
| Data Layer | AkShare | Pandas DataFrame | Ticker, Timestamp, OHLCV |
| Analytics Layer | Alphalens, VectorBT | Performance Series | IC/IR, Sharpe Ratio, Drawdown |
| AI Layer | FinBERT, FinRL | Tensor/JSON | Sentiment Score, Policy Gradients |
| Verification | EconML | Statistical Summary | Treatment Effects, Confidence Intervals |
| Optimization | PyPortfolioOpt | Vector/Weight Map | Expected Returns, Covariance |
This structured approach allows Openclaw Skills to maintain a clean lineage from raw market data to final portfolio weights.
Quant Tools Advanced Features
- Causal Inference Integration: Use EconML to statistically eliminate backtest overfitting risks.
- Vectorized Computation: Perform massive parameter grid searches at lightning speed using NumPy-based engines.
- Distributed AI Research: Support for Transformer and LSTM model training within the Qlib framework.
- High-Frequency Support: Tick-level backtesting capabilities for latency-sensitive strategy development.
- Collaborative Infrastructure: Native Kubernetes and JupyterHub support for multi-analyst research pipelines.
SKILL.md
Loading
Related Openclaw Skills
AI News ZH
AI News ZH is a specialized automation pipeline that harvests global AI news from premium English sources and delivers curated Chinese daily reports.
AssemblyAI Large Audio Transcriber
A robust transcription engine for Openclaw Skills designed to handle massive audio files with speaker labeling and automatic language detection.
City Life Copilot
An intelligent urban assistant that combines mapping APIs and advanced scraping to provide personalized, mood-aware, and data-driven city life solutions.
EchoMark
EchoMark is a decentralized rating system that enables AI agents to evaluate the performance of the tools they use, creating a collective intelligence for tool selection.
Smart Site Selection AI Agent Skill
An advanced AI location intelligence expert that automates site selection for physical businesses using real-time LBS data and strategic analysis.
Zhoupu Order Import
A specialized automation tool for converting raw order data into Zhoupu system-compliant Excel import templates.
Featured*
