To understand the power of QF-Lib, you must understand its five pillars:
To use QF-Lib in a typical research project, a developer usually follows these steps:
: Feed the results into the TearsheetGenerator to visualize the strategy's risk and return profile. Troubleshooting and Common Issues qf-lib
| Component | Responsibility | |-----------|----------------| | | Normalizes tick, bar, and fundamental data from CSV, SQL, or live APIs. | | Strategy Engine | Hosts user-defined logic, generates signals (buy/sell/hold). | | Portfolio Manager | Translates signals into orders, applies position sizing and risk limits. | | Execution Simulator | Matches orders against historical/live market data, accounts for slippage and commissions. | | Event Bus | Asynchronously passes market data, signals, orders, and fills between components. |
We implement a simple strategy using 50-day and 200-day simple moving averages on AAPL (2015–2020). To understand the power of QF-Lib, you must
In the world of algorithmic trading and quantitative research, speed and accuracy are not just advantages; they are the bare minimum. However, building a robust research pipeline from scratch is a monumental task. Data fetching, signal generation, risk management, and backtesting engines often require thousands of lines of boilerplate code.
Installing QF-Lib is straightforward via pip , though note that it relies heavily on pandas and numpy . | | Portfolio Manager | Translates signals into
QF-Lib abstracts data fetching. It reads multiple instruments (equities, FX, futures) and timeframes. It handles alignment automatically. For example, aligning daily inflation data with minute-by-minute equity bars is handled without manual reindexing.
data_provider = CSVDataProvider('aapl_2015_2020.csv') engine = BacktestEngine( start_date='2015-01-01', end_date='2020-12-31', initial_capital=100000, data_provider=data_provider, strategy=MyStrategy, commission=1.0, slippage=0.0001 ) results = engine.run()
This tracks all open positions, cash, and realized/unrealized P&L. It enforces position limits and leverage constraints. Most importantly, it calculates during the backtest.