A professional quantitative trading assistant for strategy generation, performance-optimized backtesting, and live trading integration.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install quant-trading-cn
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 quant-trading-cn 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 Trading Expert (quant-trading-cn)?
The Quant Trading Expert is a specialized skill designed for developers and traders looking to build robust algorithmic trading systems. Drawing from extensive real-world experience in the Indian stock market (Zerodha) and adaptable to A-shares, this skill provides a structured framework for the entire trading lifecycle. It addresses critical production issues like tick size rounding and VWAP resets that often cause discrepancies between backtesting and live results. By leveraging high-performance tools like Polars and Parquet, it enables backtesting speeds up to 25x faster than traditional methods, making it an essential addition to your library of Openclaw Skills.
Quant Trading Expert (quant-trading-cn) Use Cases
- Generating custom trading bots from scratch with embedded risk management parameters.
- Enhancing existing trading code to fix production bugs and optimize execution performance.
- Fetching live stock universes and indices to create dynamic, liquidity-filtered stock pools.
- Performing high-speed backtests using vectorized data processing to validate strategy signals.
- Analyzing post-trade performance including P&L decomposition, Sharpe ratios, and drawdown.
How Quant Trading Expert (quant-trading-cn) Works
- Initialize the interactive wizard to define your specific trading style, stock universe, and risk appetite.
- Execute the universe-fetch script to retrieve current market constituents and liquidity data from exchanges.
- Develop or refine strategies using built-in knowledge across 16 key domains, including signal generation and rebalancing logic.
- Run the automated code checker to identify and fix 30+ common pitfalls like improper tick size rounding.
- Execute high-performance backtests using Polars vectorization for rapid validation before transitioning to live trading.
Quant Trading Expert (quant-trading-cn) Setup
This skill requires python3 to be installed. Start building your algorithmic trading infrastructure with these Openclaw Skills commands:
# Launch the interactive trading bot wizard
./scripts/wizard.sh
# Fetch latest stock universe constituents
./scripts/universe-fetch.sh --indices nifty50,nifty100
# Audit your trading script for production readiness
./scripts/check-code.sh ./my_trading_bot.py
Quant Trading Expert (quant-trading-cn) Data Schema & Taxonomy
| Component | Description | Data Format |
|---|---|---|
| KNOWLEDGE.md | Detailed documentation covering 16 trading technical domains | Markdown |
| NUANCES.md | A database of over 30 production pitfalls and their fixes | Markdown |
| scripts/ | Collection of shell scripts for wizard setup and data fetching | Bash |
| references/ | Original English reference materials for global adaptation | Markdown |
Quant Trading Expert (quant-trading-cn) Advanced Features
- Fortress signal generation logic with a documented 65% win rate and multi-factor confirmation.
- High-performance data handling using Parquet caching and Polars vectorization for 37x faster processing.
- Built-in adaptation templates for transitioning strategies between Indian markets and Chinese A-shares.
- Advanced risk management modules implementing the Kelly Criterion and portfolio heat monitoring.
- Comprehensive signal attribution to track precisely which indicators triggered specific trades.
SKILL.md
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