Python vs C++: Which Should You Learn for Quant Research?

One of the most common questions from aspiring quants is: "Should I focus on Python or C++?" The answer, as typical in finance, is "it depends". But it depends on the specific role you want.

1. Python: The King of Research

In 90% of Quantitative Research (QR) roles, Python is the primary language.

• Why? The ecosystem (Pandas, NumPy, Scikit-learn, PyTorch) is unmatched for data analysis.
• Use Case: Backtesting strategies, cleaning data, building ML models.
• Interview Focus: Data manipulation, vectorization (avoiding for-loops), and basic algorithms.

2. C++: The King of Execution

For Quantitative Developers (QD) and High-Frequency Trading (HFT) roles, C++ is non-negotiable.

• Why? Speed. When you are competing for microseconds, the overhead of the Python interpreter is unacceptable.
• Use Case: Building the order gateway, market data feeds, and execution algorithms.
• Interview Focus: Pointers, memory management, template metaprogramming, and OS intervals.

The Hybrid Model

Modern trading firms often use a hybrid approach. The core engine is written in C++ for speed, but it exposes Python bindings (using tools like pybind11). This allows researchers to write strategies in Python that run on the C++ engine.

Verdict