OneQuant A-Share Quant Trading Setup Wizard
Paste the following prompt into your AI chat to install this skill:
Please install @user_a9caeb3c/quant-trader-setup according to https://skillhub.cn/install/skillhub.md.
About this skill
Problem
A-share personal research and strategy validation often scatter across market software, Python scripts, and spreadsheets. K-lines, MA/RSI signals, paper positions, and risk thresholds are hard to keep in one local workflow, and programmatic access is not obvious.
How It Works
The skill documents configuration and usage for the local OneQuant 4.0 Flask app. Key capabilities include:
- Market and stock analysis: index quotes, sector and concept boards, northbound flows, dragon-tiger lists, and daily/weekly/minute K-lines via
/api/market/kline. - Technical signals and backtesting:
MACD,KDJ,RSI, MA/RSI backtests, and multi-strategy comparison. - Rule-based screening and paper trading:
limit_up,ma_golden,rsi_oversold, plus local simulated buy/sell, position PnL, and stop-loss/take-profit settings. - Programmatic integration: REST API, Python
requests, CLI, and batch scripts; data outputs should cite source and retrieval time.
Boundaries
- Best for single-user local use, low concurrency, A-share daily/minute research, and paper trading.
- Not for high-frequency trading, live order execution, futures/options, deep HK/US equity analysis, or custom machine-learning strategies.
AkShareis free public data with second-level delays; backtest returns exclude commissions, slippage, and stamp tax, and should not be treated as investment advice.
Use Cases
- Validate an MA crossover strategy after close: pick a stock, set short/long periods, run a backtest, and inspect the equity curve.
- Screen limit-up stocks after close: call the selection API, fetch the top 10 tickers, and export an Excel file for review.
- Review paper-account risk: check position ratios, stop-loss/take-profit settings, and confirm whether any single position exceeds a threshold.
- Overlay indicators on stock K-lines: view daily or intraday candles with MACD, KDJ, RSI, and current buy/sell signals.
Best For
- A-share individual investors: want to validate trading strategies and monitor paper-position risk.
- Quant beginners: need to understand backtesting workflows and practice with MA/RSI strategies.
- Python data analysts: want to fetch quotes and backtest results in batches via REST APIs.
- Finance content creators: need market data, K-line charts, and screening results as materials.
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