QuantLib-Python

Warnings

• gotcha QuantLib uses its own robust date, calendar, and day counter system (e.g., `ql.Date`, `ql.Calendar`). Mixing directly with Python's `datetime` objects without proper conversion or misunderstanding QuantLib's specific business day conventions can lead to incorrect or unexpected results.
  Fix: Always use `QuantLib.Date` and `QuantLib.Calendar` for internal calculations, converting to/from `datetime` explicitly only when interacting with external Python libraries or data sources. Consult QuantLib documentation for specific date arithmetic and calendar rules.
• gotcha QuantLib heavily relies on an 'observable' pattern for dependency management (e.g., an instrument observing a yield curve). If underlying data or objects change, dependent objects might not automatically update their calculated values unless explicitly refreshed or properly observing these changes.
  Fix: Ensure proper observer registration for dependent objects. In scenarios where values seem stale, explicit calls to `update()` on observable objects might be necessary to trigger recalculations or verify that the observation chain is correctly configured.
• gotcha While robust, the Python bindings introduce a performance overhead compared to direct C++ QuantLib usage. For very high-frequency calculations, large-scale Monte Carlo simulations, or iterative optimizations, this overhead can become significant.
  Fix: Profile critical sections of your code to identify performance bottlenecks. For highly performance-sensitive tasks, consider offloading computations to a C++ extension or pre-calculating results where feasible, or explore alternative pure-Python libraries for less complex tasks.

Install

• pip install QuantLib-Python Install using pip

Imports

• QuantLib

  import QuantLib as ql

  It is common practice to import QuantLib with the alias 'ql' for brevity.

Quickstart

This quickstart demonstrates how to import QuantLib, set a global evaluation date, create QuantLib Date objects, and use a calendar function. Setting the evaluation date is a fundamental first step for most QuantLib applications.

import QuantLib as ql

# Set a global evaluation date (crucial for many QuantLib calculations)
todaysDate = ql.Date(15, ql.May, 2023)
ql.Settings.instance().evaluationDate = todaysDate

# Create another QuantLib Date object
another_date = ql.Date(15, ql.May, 2024)

# Print dates and check a simple calendar function (e.g., TARGET)
print(f"Evaluation date: {todaysDate}")
print(f"Another date: {another_date}")
print(f"Is evaluation date a business day (TARGET calendar)? {ql.TARGET().isBusinessDay(todaysDate)}")