OneQuant 4.0 A-Share Quantitative Trading System

Problem

A-share retail traders and quant learners need a locally controlled workflow for market viewing, K-line analysis, strategy backtesting, paper trading, and risk checks. OneQuant 4.0 implements these modules in a local Flask app and exposes REST APIs for scripted use.

How It Works

It can be used through a web UI, REST API, CLI, batch scripts, or Docker. Core capabilities include:
- Market analysis: index quotes, sector rankings, northbound flow, and dragon-tiger list.
- K-line signals: multi-period K-line charts with MACD, KDJ, and RSI, plus buy/sell signals.
- Screening and backtesting: filter stocks by limit_up, ma_golden, or rsi_oversold, then run MA or RSI strategies and review performance.
- Paper trading and risk: buy/sell orders, holdings, PnL, trade history, stop-loss/take-profit settings, and position alerts.
- Reports: export trade logs, backtest results, and holdings to Excel.

A typical workflow is to check market or K-line data first, validate a strategy through the backtest API, and then review simulated account risk.

Boundaries

It suits single-user local research, conditional screening, and basic strategy validation. It is not intended for high-frequency trading, live order execution, futures/options, or deep HK/US equity analysis. Data depends on AkShare, JQData, or RQData; free sources may have higher latency and limited intraday coverage. Backtest returns exclude commissions, slippage, and stamp duty. The default broker is a mock, so live trading requires separate broker integration, authorization, and compliance checks.