The quantitative strategy development process typically follows: Idea → Data → Strategy Coding → Backtesting → Forward Testing (Paper Trading) → Live Trading. Python’s ecosystem provides open-source tooling covering the entire workflow, enabling individual quant enthusiasts to build a complete strategy research environment at minimal cost.
## Backtesting Framework Selection
**Backtrader**: the most widely used Python backtesting framework domestically and internationally; object-oriented design supports multi-asset, multi-timeframe, custom indicators, and realistic trading cost (commission/slippage) modeling. Medium learning curve, solid documentation, active community.
**VectorBT**: vectorized backtesting based on NumPy/Pandas; extremely fast (100-1,000× faster than Backtrader); suited for large-scale parameter scanning and optimization, but weaker support for event-driven strategies.
**Zipline-reloaded**: the open-source backtesting engine from Quantopian (now defunct), maintained by the community; built-in US stock data support; high integration with the Python quant ecosystem.
## Key Performance Metrics
**Sharpe Ratio**: excess return (relative to risk-free rate) divided by return standard deviation; measures excess return earned per unit of risk. Formula: (Annualized Return – Risk-Free Rate) / Annualized Volatility. Industry standard: SR>1 is acceptable, >2 is excellent, >3 is elite.
**Maximum Drawdown (MDD)**: the maximum peak-to-trough decline over any interval; measures worst-case capital loss. Formula: MDD = (Trough – Peak) / Peak. For assessing real trading psychological tolerability, MDD is more intuitive than volatility.
**Calmar Ratio**: annualized return divided by maximum drawdown; measures annualized return earned per unit of maximum risk. Particularly important for CTA trend-following strategies, which typically have larger drawdowns.
See [Quantitative Investing Intro](https://sunqi.org/quantitative-investing-intro-en/) and [Backtrader official documentation](https://www.backtrader.com/docu/).
