OneQuant ZeroCode Pro Zero-Code Quant System
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About this skill
Problem it Solves
OneQuant ZeroCode Pro targets A-share individual investors and quant learners who need to combine strategy validation, market analysis, paper trading, and risk monitoring without switching between many local tools. It is useful when the first questions are: How do current indicators look? Would a simple historical strategy have worked? Is the simulated portfolio within risk limits?
How It Works
It packages a local Flask web app with a browser interface and REST APIs around A-share data. A typical workflow includes:
- fetching index snapshots, K-lines, and sector rankings through
/api/market/overview,/api/market/kline, and/api/market/industry_rank; - reading MACD, KDJ, and RSI signals through
/api/signal/technical; - running MA or RSI backtests through
/api/backtest/maand/api/backtest/rsi; - screening names by limit-up, MA crossover, or RSI oversold conditions through
/api/selection/list; - managing simulated positions and risk through
/api/trade/buy,/api/trade/holdings, and/api/risk/overview.
It uses AkShare by default and can be configured with JQData or RQData. Outputs are expected to cite data source and retrieval time, while backtest results include a disclaimer and should not be framed as guaranteed returns or millisecond-level real-time execution.
Boundaries and Caveats
It fits A-share daily-level strategy study, local paper trading, market monitoring, and Excel export. It is not intended for high-frequency trading, live order routing, futures or options, deep US/HK stock analysis, or custom machine-learning models. Free AkShare data may have 3-5 second latency and limited minute-bar coverage, and backtest returns do not include commissions, slippage, or stamp tax by default. For long-running use, export local JSON state and clean cached files regularly.
Use Cases
- Inspect A-share K-lines, MACD/KDJ/RSI indicators, and buy/sell signals in the browser to build technical notes.
- Run MA or RSI backtests on a single A-share name, review the equity curve and performance metrics, and save the record.
- Screen A-share stocks by limit-up, MA golden cross, or RSI oversold rules, then export candidates for review.
- Buy and sell A-share names in a simulated account, track holdings and orders, and check position-level risk alerts.
Best For
- A-share individual investors who want to validate strategies and monitor simulated portfolio risk before going live.
- Quant learning beginners who want to understand strategy logic through MA and RSI backtests plus K-line indicators.
- Python data analysts who want to pull market data via REST APIs, run backtests, and export results.
- Finance content creators who need A-share market data, sector rankings, and financial metrics for charts.
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