prism-quant 1.0.6

A-share low-frequency quantitative trading framework covering research, backtesting, and execution (formerly prism_quant)

PrismQuant

中文 | English

Professional-Grade Python Open-Source Quantitative Trading Framework — covering the full "Research → Backtest → Live Trading" lifecycle, building an industrial-grade quantitative closed-loop workflow.

🎓 Official Tutorials

iMOOC - AI Quantitative System Course

Official Course: Deeply deconstructing the framework's architecture from 0 to 1, covering live trading logic and industrial-grade quantitative development. An essential course for mastering PrismQuant.

📦 Installation

pip install prism_quant

Development environment (with TimescaleDB):

git clone https://gitee.com/beauty9235/prism_quant.git
cd prism_quant
docker compose up -d              # Start PostgreSQL + TimescaleDB
pip install -e .                   # Developer mode install

For previous version source code, visit: https://pypi.org/project/prism_quant/#history

✨ Key Features

Capability	Description
📥 Multi-Source Data	3 data sources (tushare / tiingo / EastMoney), covering A-shares / US / HK / Funds
💾 Smart Caching	TimescaleDB (PostgreSQL) + SQLite dual backend, incremental fetch with auto-persistence, millisecond cache hits
🛡️ Anti-Block Protection	IPv4 enforcement + random delay + exponential backoff retry for free data source stability
🧠 Rapid Development	Signal-driven architecture, BaseStrategy template, 10 lines for a complete strategy
📉 Professional Backtest	High-performance matching engine, A-share price-limit/T+1 rules, share lot rounding, minimum commission, stop-loss/take-profit, OOS testing
📊 18 Professional Charts	Monthly returns heatmap / Rolling Sharpe / Strategy comparison / Volatility cone / Position heatmap / Correlation matrix / Risk dashboard / QQ plot / Waterfall / Trade timeline, and more
⚡ Event-Driven	Sub-second market data distribution, millisecond Tick signal processing, LiveEngine orchestration
🤖 Live Trading Gateways	Pluggable adapters (PaperTrade simulation / miniQMT live), seamless switching

⚡ Quick Start

import prism_quant as prq

# 1. Define strategy logic
class MyStrategy(prq.strategy.BaseStrategy):
    def generate_signals(self, data):
        bands = prq.indicators.TechnicalIndicators().boll(data["Close"])
        return prq.strategy.SignalGenerator().boll_signals(data["Close"], bands)

# 2. Minimal backtest & results (auto-cached)
engine = prq.backtest.BacktestEngine()
engine.set_parameters("GOOGL", "2025-07-26", "2026-01-26")
engine.load_data()                         # First run: remote fetch → auto-save; next: instant cache read
engine.add_strategy(MyStrategy(name="BOLL"))
engine.run_backtest()
engine.show_report()
engine.show_chart(use_plotly=False)        # 5-panel performance chart

# 3. Professional chart analysis
from prism_quant.charts import ReturnsChart, PortfolioChart

# Monthly returns heatmap (essential for interviews/roadshows)
ReturnsChart().plot_monthly_heatmap(returns, use_plotly=True)

# Risk dashboard (VaR backtest + volatility + max drawdown)
PortfolioChart().plot_risk_dashboard(returns, equity, use_plotly=True)

🔌 Data Sources & Integrations

Type	Integration	Market	Status
Data	tushare	A-shares/HK (premium quality, token required)	✅
Data	tiingo	US stocks (free API key)	✅
Data	eastmoney	OTC Funds (Stock/Bond/Mixed/QDII)	✅
Data	miniQMT	A-share market data (requires XunTou client)	✅
Trade	PaperTrade	Local simulation, Tick-driven matching	✅
Trade	miniQMT Trade	A-share live trading	✅

Minimal miniQMT live trade setup

from prism_quant.live import LiveEngine

engine = LiveEngine(symbol="000001.SZ", signal_interval="1m")
engine.set_data_gateway("miniqmt", interval=3.0, mode="poll")
engine.set_trade_gateway(
    "miniqmt",
    userdata_mini_path=r"D:\BrokerQMT\userdata_mini",
    account_id="1234567890",
)

See details:

• documents/LiveEngine.md
• documents/MiniQmtTrade.md
• documents/MiniQmtLiveEngine.md

🏗️ Project Architecture

prism_quant/
├── core/                            # Base classes, config, logging
│   ├── base.py
│   ├── config.py
│   └── logger.py
├── data/                            # Fetch, clean, store
│   ├── fetcher.py                   # 3 sources + anti-block + auto-cache
│   ├── cleaner.py                   # Missing value handling (dropna / fillna)
│   ├── storage.py                   # CSV file storage
│   ├── database.py                  # SQLite database storage
│   ├── timescale.py                 # TimescaleDB (PostgreSQL) production storage
│   ├── source_map.py                # Gateway → source name mapping
│   └── miniqmt_xtdata.py            # miniQMT / xtquant historical bars
├── indicators/                      # Technical & fundamental factors
│   ├── technical.py                 # SMA/EMA/RSI/MACD/BOLL/ATR/OBV/CCI/ADX/KDJ
│   ├── fundamental.py               # PE/PB/ROE/Dividend Yield etc.
│   └── talib_indicators.py          # TA-Lib equivalent (performance benchmark)
├── strategy/                        # Strategy base & signal generation
│   ├── base.py                      # BaseStrategy template
│   └── signals.py                   # SignalGenerator (Bollinger/CCI/ADX etc.)
├── backtest/                        # Backtest engine & metrics
│   ├── engine.py                    # BacktestEngine (A-share T+1 / price limit / OOS)
│   ├── metrics.py                   # Alpha/Beta/Sharpe/Sortino/VaR/CVaR
│   ├── performance.py               # PerformanceReporter
│   └── optimizer.py                 # Grid search parameter optimizer
├── live/                            # Event engine & live orchestration
│   ├── event_engine.py              # Event loop & priority queue
│   ├── gateways.py                  # DataGateway / TradeGateway abstractions
│   ├── gateway_registry.py          # Gateway factory & registry
│   ├── engine.py                    # LiveEngine orchestration
│   └── models.py                    # TickData / OrderRequest etc.
├── adapters/                        # Pluggable data / trade adapters
│   ├── data/                        # Data gateways (yfinance / miniQMT)
│   └── trade/                       # Trade gateways (Paper / miniQMT)
├── trader/                          # Execution engine & order management
│   ├── engine.py                    # ExecutionEngine (slippage/commission/A-share rules)
│   ├── order_manager.py             # OrderManager (order lifecycle)
│   ├── position_manager.py          # PositionManager (position tracking & T+1 freeze)
│   └── market_rules.py              # MarketRules (A-share lots/stamp tax/price limit)
├── charts/                          # 🆕 18 professional quantitative charts
│   ├── base.py                      # BaseChart (create_figure / show / save)
│   ├── theme.py                     # ChartTheme (unified colors/fonts/dark mode)
│   ├── price.py                     # Price comparison (normalized / Plotly)
│   ├── performance.py               # 5-panel backtest performance (equity/drawdown/distribution)
│   ├── signals.py                   # Bollinger/MACD/RSI + candlestick (mplfinance)
│   ├── returns.py                   # 🆕 Monthly heatmap/rolling Sharpe/QQ/waterfall/dashboard
│   ├── comparison.py                # 🆕 Strategy comparison/param heatmap/volatility cone
│   └── portfolio.py                 # 🆕 Position heatmap/correlation/risk decomp./trade timeline
└── utils/                           # Utilities
    └── check_connectivity.py        # Data source connectivity checker

Infrastructure:

├── docker-compose.yml               # PostgreSQL + TimescaleDB one-click deploy
├── Dockerfile.sync                  # Daily data sync service image
├── scripts/
│   ├── sync_daily.py                # Incremental daily data sync script
│   ├── sync.crontab                 # Crontab schedule rules
│   ├── check_data_sources.sh        # Data source connectivity check
│   └── db-init/01-init.sql          # Schema + Hypertable + Compression policy
├── tests/                           # 102 pytest unit tests
├── examples/                        # 23 examples (data/backtest/charts/live/funds)
└── documents/                       # 8 technical documents

📊 Full Chart Catalog (18 Charts)

#	Chart	Module	Matplotlib	Plotly
1	Price Comparison (normalized)	PriceChart	✅	✅
2	5-Panel Backtest Performance	PerformanceChart	✅	✅
3	Bollinger Band Signals	SignalChart	✅	✅
4	MACD Panel	SignalChart	✅	—
5	RSI Panel	SignalChart	✅	—
6	Monthly Returns Heatmap	ReturnsChart	✅	✅
7	Rolling Sharpe Ratio	ReturnsChart	✅	—
8	Rolling Metrics Dashboard	ReturnsChart	✅	—
9	Q-Q Plot vs Normal	ReturnsChart	✅	—
10	P&L Waterfall	ReturnsChart	✅	—
11	Strategy Comparison	ComparisonChart	✅	✅
12	Parameter Sensitivity Heatmap	ComparisonChart	✅	—
13	Volatility Cone	ComparisonChart	✅	✅
14	Position Heatmap	PortfolioChart	✅	—
15	Correlation Matrix	PortfolioChart	✅	✅
16	Risk Decomposition (Pie)	PortfolioChart	✅	—
17	Trade Timeline	PortfolioChart	✅	✅
18	Risk Dashboard	PortfolioChart	✅	✅

Full example: python examples/21_all_charts.py or python examples/21_all_charts.py --save

🤝 Contributing

• Feedback: Bug reports and contributions are welcome via Issue or Pull Requests.
• WeChat Official Account: Follow PrismQuant开源量化 for updates, strategies, and quantitative resources.

📄 License

MIT License. See LICENSE for details.