vectorized backtesting
To gain an understanding of vectorized backtesting consider the following:
1. What Does “Vectorized” Mean?
In computing and mathematics, vectorization refers to expressing operations in terms of vectors (arrays of data) instead of iterating over elements one by one.
Example — Non-vectorized approach:
returns = []
for i in range(1, len(prices)):
returns.append((prices[i] - prices[i - 1]) / prices[i - 1])
Example — Vectorized approach using pandas:
returns = prices.pct_change()
This is much faster because operations are offloaded to optimized C-level routines, avoiding Python-level loops.
2. Why Is Vectorization Done in Backtesting?
Backtesting involves simulating a trading strategy on historical data. Vectorization is used for:
- Speed — Processes large datasets much faster than loops.
- Simplicity — Cleaner and shorter code.
- Scalability — Handles more data and more complex strategies efficiently.
For high-frequency or multi-asset backtests, vectorization is critical for performance.
3. How Does It Relate to Backtesting?
Backtesting typically involves:
- Signal generation — e.g., buy when price > moving average.
- Trade execution — calculating positions and applying transaction costs.
- Performance evaluation — computing returns, drawdowns, Sharpe ratio, etc.
A vectorized backtest performs all of these steps using array operations.
Example:
prices = df['Close']
signal = prices > prices.rolling(20).mean() # vectorized signal
positions = signal.shift(1).astype(int) # vectorized position
returns = prices.pct_change()
strategy_returns = positions * returns # vectorized strategy returns
Each line operates on entire time series data rather than single values, making the simulation fast and efficient.
Summary
Vectorized backtesting is an efficient method for simulating trading strategies using array-based operations (via NumPy and pandas). It is:
- Faster than traditional loops
- Scalable to large datasets and complex strategies
- Readable and maintainable
Hilpisch emphasizes this method because it is foundational for building high-performance, professional algorithmic trading systems.