This mirrors real exchange order matching and prevents look-ahead bias.

While simple vectorized backtests (e.g., df['signal'] = df['close'].pct_change() ) are fast, they are unrealistic. QF-Lib uses an . Every trade, bar, or tick triggers an event. This accurately models market microstructure, order books, and latency—crucial for high-frequency or intraday strategies.

| Metric | Value | |--------|-------| | Total Return | +42.3% | | Sharpe Ratio (annual) | 0.91 | | Max Drawdown | -18.2% | | Number of trades | 12 |

One of the library's strongest features is its suite of analytical tools. It can automatically generate comprehensive "tearsheets" that include:

is an open-source Python library designed to provide high-quality tools for quantitative finance , specifically focusing on backtesting investment strategies and portfolio management. Core Functionality

The qf_lib.common module is the workhorse of the library. It contains implementations of hundreds of technical indicators and helper functions. However, unlike standard implementations, these indicators are designed to work seamlessly with the library’s specific data containers.