: Feed the results into the TearsheetGenerator to visualize the strategy's risk and return profile. Troubleshooting and Common Issues
The library is designed to be data-agnostic but includes built-in connectors for popular professional data providers like and Quandt . It uses a "DataHandler" architecture to ensure that strategies remain consistent regardless of whether they are using live or historical data. Why Choose QF-Lib Over Alternatives? qf-lib
Unlike generic data science tools, QF-Lib is built specifically for institutional-grade financial research. It bridges the gap between academic theory and practical trading by offering high-level abstractions for time-series analysis, risk management, and performance attribution. Core Capabilities of QF-Lib : Feed the results into the TearsheetGenerator to
: Use the BacktestTradingSession to simulate the strategy over a historical period. Why Choose QF-Lib Over Alternatives
(Keywords: quant finance library, backtesting framework, event-driven backtester, Python quantitative analysis, qf-lib tutorial.)
# Logic if fast_ma.iloc[-1] > slow_ma.iloc[-1] and fast_ma.iloc[-2] <= slow_ma.iloc[-2]: self.broker.order_percent(self.ticker, 1.0) # Buy 100% of capital elif fast_ma.iloc[-1] < slow_ma.iloc[-1] and fast_ma.iloc[-2] >= slow_ma.iloc[-2]: self.broker.order_percent(self.ticker, 0.0) # Sell everything
To use QF-Lib in a typical research project, a developer usually follows these steps: